As you start working with Power BI, you’ll encounter an important decision: How do I connect to data in my reports, and what is the difference between Import vs Direct Query Power BI? Then you google for insights and find a few “technical consultant” focused blogs, that discuss significant differences thing sentences, and we wanted to make a comprehensive article for more audience members.
Your chosen connection method will depend on the source database and your analytics needs. Once connected, you can visualize and analyze the data in your reports using Power BI’s interactive dashboard. Thatâs where âImportâ and âDirect Queryâ come into play. But what does Import vs Direct Query Power BI mean?
Both allow you to uncover hidden opportunities using data. Data governance for PowerBI is essential for operationalizing how data is refreshed in analytics projects. This ensures that the dashboard meets the organization’s analytics needs and takes advantage of the benefits of data governance. This means youâre not guessing between the directory method (aka live) or Import (aka extract) between each report because itâs an established offering for analytics projects. It’s advantageous for your analytics needs. Teams often set a few data freshness and time analytics options and then apply those limitations to all incoming reports. This ensures the data access credentials are up-to-date, providing a snapshot of the most recent information.
Introduction to Connecting to Data in Powerbi
You may also encounter this situation when you realize that the DirectQuery feature doesn’t work with your underlying data source or that the Import feature doesn’t update fast enough. You may wonder if you need to rebuild your data models.
The decision to use analytics extends beyond databases and includes various data sources such as online services, spreadsheets, APIs, and more.
In Power BI, users can choose the direct query method for their analytics needs. This choice becomes noticeable as they set up data connections and build their dashboards in Power BI.
You are choosing between Import Vs. Direct Query in Power BI, at first, is easy to skip without considering its long-term impact or the implications it may have as your prototype dashboard goes from DEV to PROD. When working with Direct Query to utilize data sets effectively, it is essential to understand the data connectivity and the underlying data source.
The first time you see the comparison between “Import Vs. Direct Query”
The first time, in Power BI, is while connecting to data.
Suppose youâre using a relational database like Microsoft SQL Server. In that case, you can import data into Power BI using Import Mode or connect directly to the database using Direct Query Mode for analytics.
As we researched, we found many technical blogs written to help people explain the tech technical aspects and insights using Power BI Service and Power BI Desktop. Still, we didn’t find direct content that explained it in a way we could easily share with business, sales, marketing teams, or executives using Power BI service and Power BI desktop. Ideally, this comprehensive guide will help explain to technical and non-technical users, as both should know about the process from multiple perspectives because it presents the overall availability of the data with both ups and downsides.
Consider Import and Direct Query as two different roads or paths leading to the same destination.
Insights in the Power BI service. Each road or path, including direct query, has advantages and considerations; weâll help you navigate them. Whether youâre just starting your Power BI journey or looking to make more informed choices about data connections, this direct query may become your friendly companion.
Import Mode in Power BI is like bringing all your data into Power BI using DirectQuery. Itâs fast, flexible, and lets you create powerful visualizations. With a direct query, you can work on your data even when offline, just like playing with building blocks.
On the other hand, Direct Query Mode is more like having a direct line to your data source with direct query. DirectQuery is a real-time feature in Power BI that doesn’t store your data inside the platform. Itâs as if youâre looking at a live feed.
Selecting between Import or Direct Query involves critical decisions, like choosing between different game modes.
What is Import Data Mode?
The Import Data Mode in Power BI is like bringing all your data into Power BI’s playground using DirectQuery. Hereâs a closer look:
The most common method used in Power BI is the DirectQuery Import Data Mode. In this direct query mode, you directly pull data from various sourcesâsuch as databases, spreadsheets, online services, and moreâinto Power BI.
This is extract in Tableau Desktop.
Power BI’s internal engine copies and stores the data using a direct query. Think of it as filling your toy box with all your favorite toys, including direct queries, making them readily available whenever you want to play.
This approach offers several key benefits:
Benefits of Import Data Mode
Speed: Since the data is stored within Power BI’s direct query functionality, it can be processed and analyzed quickly. With DirectQuery, your reports and visualizations using DirectQuery respond almost instantly, providing a smooth user experience.
Offline Access: With DirectQuery, you can work on your reports in Import Mode without an internet connection. Itâs like having direct toys wherever you go without accessing the original data source.
Data Transformation and Modeling: In Import Mode, direct query gives you complete control over your data. To build a coherent and insightful dataset, you can shape, clean, and create relationships between tables with direct queries. This natural flexibility is like being the master of your toy kingdom, arranging everything just how you want.
How to Import Data in Power BI
Importing data into Power BI is straightforward:
Data Source Selection: First, you choose the direct data source you want to import from. This could be an SQL database, an Excel spreadsheet, a cloud service like Azure or Google Analytics, or many others that support direct queries.
Data Transformation: You can perform data transformations using Power Query, a powerful tool built into Power BI. This step allows you to clean, reshape, and prepare your data for analysis.
Data Modeling: In this phase, you create relationships between tables, define measures, and design your data model. Itâs like assembling your toys in a way that they interact and tell a meaningful story.
Loading Data: Finally, you load the transformed and modeled data into Power BI. This data is ready to build reports, dashboards, and visualizations.
Data Transformation and Modeling
Data transformation and modeling are critical steps in Import Mode:
Data Transformation: Power Query allows you to perform various transformations on your data. You can filter out unnecessary information, merge data from multiple sources, handle missing values, and more. This is like customizing your toys to fit perfectly in your playtime scenario.
Data Modeling: In Power BIâs Data View, you define relationships between tables. These relationships enable you to create meaningful visuals. Itâs similar to connecting different parts of your toys to create an exciting and cohesive storyline.
Performance Considerations
While Import Mode offers many advantages, itâs essential to consider performance factors:
Data Refresh: As your data evolves, you must regularly refresh it to keep your reports current. The frequency and duration of data refresh can impact the overall performance of your Power BI solution.
Data Volume: Large datasets can consume a significant amount of memory. Monitoring and optimizing your data model is essential to ensure it doesnât become unwieldy.
Data Source Connectivity: The performance of data import depends on the speed and reliability of your data source. Slow data sources can lead to delays in report generation.
Data Compression: Power BI uses compression techniques to reduce the size of imported data. Understanding how this compression works can help you manage performance effectively.
What is Direct Query Mode?
Direct Query Mode in Power BI is like allowing an executive to see data when it’s in the database. They are running a query on that database when they start the report. This is great for dashboards that only have a few users or if the database is optimized for traffic, you can increase the traffic. However, as a rule of thumb, it’s best to keep direct queries for those who need to access data immediately and try to use Import for everything else.
This usual question of “when was this refreshed?” will have the exciting answer of “when you opened the report.”
This is called “Live” in Tableau Desktop.
In Direct Query Mode, you establish a direct connection from Power BI to your data source, such as a database, an online service, or other data repositories. Instead of importing and storing the data within Power BI, it remains where it is. Imagine it as if youâre watching your favorite TV show as itâs being broadcast without recording it. This means youâre always viewing the most up-to-date information, which can be crucial for scenarios where real-time data is essential.
Benefits of Direct Query Mode
Real-time or Near-real-time Data: Direct Query provides access to the latest data in your source system. This is invaluable when monitoring rapidly changing information, such as stock prices, customer interactions, or sensor data.
Data Source Consistency: Data isn’t duplicated in Power BI; maintain consistency with the source system. Any changes in the source data are reflected in your reports, eliminating the risk of using outdated information.
Resource Efficiency: Direct Query Mode doesnât consume as much memory as Import Mode since it doesnât store data internally. This can be advantageous when dealing with large datasets or resource-constrained environments.
Supported Data Sources
Power BIâs Direct Query Mode supports a variety of data sources, including:
Relational Databases: This includes popular databases like Microsoft SQL Server, Oracle, MySQL, and PostgreSQL, among others.
Online Services: You can connect to cloud-based services like Azure SQL Database, Google BigQuery, and Amazon Redshift.
On-premises Data: Direct Query can also access data stored on your organizationâs servers, provided a network connection.
Custom Data Connectors: Power BI offers custom connectors that allow you to connect to various data sources, even those not natively supported.
Creating a Direct Query Connection
Setting up a Direct Query connection involves a few steps:
Data Source Configuration: Start by defining the connection details to your data source, such as server address, credentials, and database information.
Query Building: Once connected, you can create queries using Power BIâs query editor to specify which data you want to retrieve. Think of this as choosing the TV channel you want to watch.
Modeling and Visualization: As with Import Mode, youâll need to design your data model and create visualizations in Power BI, but with Direct Query, the data stays in its original location.
Performance Considerations
While Direct Query offers real-time data access, there are some performance considerations to keep in mind:
Data Source Performance: The speed of your Direct Query connection depends on the performance of your data source. (Your dashboard calculations and complexity are equally crucial for performance, but this is the distance between data source and the dashboards). Slow or poorly optimized databases can delay retrieving data, but that’s dashboard-level performance and not data source performance. Both are significant, and both are different.
Query Optimization: Efficiently written queries can significantly improve performance. Power BIâs query editor provides tools to help you optimize your queries.
Data Volume: Large datasets may still impact performance, especially when complex calculations are involved. Efficient data modeling is essential to mitigate this.
Data Source Compatibility: Not all data sources are compatible with Direct Query. Ensure your data source supports this mode before attempting to create a connection.
Direct Query Mode is a powerful tool when you need real-time access to your data, but understanding its benefits, limitations, and how to optimize its performance is crucial for a successful implementation in your Power BI projects.
When to Use Import vs. Direct Query
Regarding Power BI, how you access and interact with your data is not one-size-fits-all. It depends on your specific needs and the nature of your data. In this section, weâll explore the scenarios that favor two fundamental data access modes: Import Mode and Direct Query Mode. Additionally, weâll delve into the concept of Hybrid Models, where you can blend the strengths of both modes to create a tailored solution that best fits your data analysis requirements. Whether you seek real-time insights, optimized performance, or a careful balance between data freshness and resource efficiency, this section will guide you toward making the right choice for your unique scenarios.
Scenarios Favoring Import Mode
Data Exploration and Transformation:Import Mode shines when you clean, shape, and transform your data before creating reports. It allows you to consolidate data from multiple sources, perform calculations, and create a unified data model within Power BI. This is especially valuable when dealing with disparate data sources that require harmonization.
Offline Accessibility: Importing data into Power BI provides the advantage of working offline. Once youâve imported the data, you can create, modify, and view reports without needing a live connection to the source. This is crucial for situations where consistent access to data is required, even when the internet connection is unreliable or unavailable.
Complex Calculations: Import Mode allows you to perform complex calculations, aggregations, and modeling within Power BI. This is advantageous when you need to create advanced KPIs, custom measures, or calculated columns that rely on data from various sources.
Performance Optimization: You can optimize performance by importing data into Power BI. Since the data resides within Power BIâs internal engine, queries and visualizations respond quickly, providing a smooth user experience, even with large datasets.
Data Security and Compliance: Import Mode is often favored when data security and compliance are paramount. By controlling access to the imported data, you can protect sensitive information, making it suitable for industries with strict regulatory requirements.
Scenarios Favoring Direct Query Mode
Real-time Data Analysis: Direct Query Mode is essential when you require up-to-the-minute data insights. Itâs perfect for monitoring stock prices, tracking website traffic, or analyzing real-time sensor data. With Direct Query, you see changes as they happen.
Large and Evolving Datasets: When working with massive datasets that are frequently updated, importing all the data can be impractical or resource-intensive. Direct Query ensures you always work with the most current information without worrying about data refresh schedules or storage limitations.
Data Source Consistency: In situations where maintaining data source consistency is critical, such as financial reporting or compliance monitoring, Direct Query ensures that your reports reflect the exact state of the source data, avoiding any discrepancies or data staleness.
Resource Efficiency: Direct Query is resource-efficient since it doesnât store data internally. This makes it suitable for scenarios where memory or storage constraints are a concern, especially in large enterprises or organizations with limited IT resources.
Hybrid Models: Combining Import and Direct Query
In some cases, the best approach involves combining both Import and Direct Query modes in what is known as a âHybrid Model.â Hereâs when and why you might choose this approach:
A blend of Historical and Real-time Data: Hybrid models are beneficial when you need a combination of historical data (imported for analysis) and real-time data (accessed through Direct Query). For example, you might import historical sales data while using Direct Query to monitor real-time sales.
Data Volume Management: You can use Import Mode for the most critical or frequently accessed data and Direct Query for less frequently accessed or rapidly changing data. This way, you strike a balance between performance and data freshness.
Combining Data Sources: Sometimes, you may need to combine data from sources best suited for different modes. For example, you might import financial data from a spreadsheet (Import Mode) and connect to an external API for real-time market data (Direct Query).
Optimizing Performance: By strategically choosing where to use Import and Direct Query, you can optimize the overall performance of your Power BI solution. For instance, you can alleviate resource constraints by using Direct Query for the most resource-intensive data sources while leveraging Import Mode for the rest.
Hybrid models provide flexibility and allow you to tailor your Power BI solution to meet your organization’s specific needs, combining the strengths of both Import and Direct Query modes to maximize efficiency and data freshness.
A Comprehensive Overview of Data Refreshes when choosing between Important VS Direct Query.
To navigate this landscape effectively, one must understand the nuances of data access modes. In this section of the âPower BI Comprehensive Guide,â we delve into two pivotal aspects: âScheduled Refresh in Import Modeâ and âReal-time Data in Direct Query Mode.â These elements are the gears that keep your data engine running smoothly, offering distinct advantages for different scenarios.
Scheduled Refresh in Import Mode automates keeping your data up-to-date, ensuring your reports and dashboards reflect the latest information. Weâll explore its benefits, such as automated data updates and historical analysis while considering factors like data source availability and performance impact.
Real-time Data in Direct Query Mode opens a window into the world of instantaneous insights. Discover how this mode allows you to access data as it happens, perfect for scenarios like stock market analysis, web analytics, and IoT data monitoring. However, weâll also delve into the critical considerations, such as data source performance and query optimization.
Lastly, weâll examine the critical topic of Data Source Limitations, where not all data sources are created equal. Understanding the compatibility and capabilities of your data sources, especially in the context of Direct Query Mode, is vital for a successful Power BI implementation.
As we navigate these aspects, youâll gain a deeper understanding of the mechanics that drive data access in Power BI, empowering you to make informed decisions about which mode suits your unique data analysis needs. So, letâs dive into the world of data access modes and uncover the tools you need for data-driven success.
Scheduled Refresh in Import Mode
Scheduled Refresh is critical to working with Import Mode in Power BI. This feature lets you keep your reports and dashboards up-to-date with the latest data from your source systems. Hereâs a more detailed explanation:
Scheduled Refresh allows you to define a refresh frequency for your imported data. For example, you can set it to refresh daily, hourly, or even more frequently, depending on the requirements of your reports and the frequency of data updates in your source systems. Power BI will re-query the data sources during each scheduled refresh, retrieve the latest information, and update your datasets.
Scheduled Refresh is beneficial in several scenarios:
Automated Data Updates: It automates the data retrieval and refresh process, reducing manual efforts. This is particularly useful for large datasets or multiple data sources.
Timely Insights: Scheduled Refresh ensures that your reports and dashboards always reflect the most current data available. This is essential for data-driven decision-making.
Historical Analysis: It allows you to maintain a historical record of your data, enabling you to analyze trends, track changes over time, and make informed historical comparisons.
However, itâs essential to consider some key factors when setting up Scheduled Refresh:
Data Source Availability: Your data sources must be accessible and available during the scheduled refresh times. If the data source becomes unavailable, the refresh process may fail.
Performance Impact: Frequently scheduled refreshes can strain your data source, so balancing data freshness and performance is essential.
Data Volume: The size of your dataset and the complexity of data transformations can affect the duration of the refresh process. Optimizing your data model and query performance is crucial.
Real-time Data in Direct Query Mode
In Direct Query Mode, real-time data access is one of its defining features. Hereâs a more detailed explanation:
Direct Query Mode lets you connect to data sources in real-time or near-real time. This means that when new data is added or updated in the source system, it becomes immediately available for analysis in your Power BI reports. Itâs like having a live feed of your data, and itâs precious in scenarios where timeliness is critical.
Some use cases for real-time data in Direct Query Mode include:
Stock Market Analysis: Traders and investors rely on up-to-the-second stock price data to make informed decisions.
Web Analytics: Businesses need real-time insights into website traffic, click-through rates, and user behavior to optimize their online presence.
IoT Data Monitoring: Industries like manufacturing and healthcare depend on real-time data from IoT sensors to ensure smooth operations and patient safety.
Real-time data in Direct Query Mode comes with considerations
Data Source Performance: The performance of your data source becomes crucial, as any delays or downtimes in the source system will directly impact the real-time data feed.
Query Optimization: Queries in Direct Query Mode should be optimized to minimize latency and ensure fast response times.
Data Source Limitations
While Power BI supports a wide range of data sources, itâs essential to be aware of potential limitations, especially in Direct Query Mode. Hereâs an overview:
Data Source Compatibility: Not all data sources are compatible with Direct Query Mode. Some sources might not support real-time access or have limited capabilities when used in this mode. Itâs essential to check the documentation and compatibility of your data source with Power BI.
Complex Transformations: In Direct Query Mode, some complex data transformations possible in Import Mode may not be supported. This can impact your ability to create calculated columns or measures directly within Power BI.
Performance Considerations: Direct Query Mode’s performance depends heavily on your data source’s performance. Slow or resource-intensive queries on the source side can lead to slower response times in Power BI.
Understanding the limitations and capabilities of your data sources is crucial for making informed decisions when choosing between Import Mode and Direct Query Mode in your Power BI projects.
Performance Considerations Using Import vs Direct Query Power BI
Factors Affecting Import Mode Performance
In import mode, performance considerations are essential for efficient data analysis. The primary factor influencing import mode performance is the size and complexity of your dataset. When dealing with larger datasets, loading data into the local or in-memory cache can become resource-intensive and time-consuming. As the dataset grows, memory usage increases, potentially leading to performance bottlenecks. Additionally, the complexity of data transformations and calculations within the data model can slow down import mode. To mitigate this, data model optimization becomes crucial, ensuring that the model is streamlined and calculations are as efficient as possible. Another factor affecting performance is the hardware resources available. Adequate RAM and CPU power are necessary to support large datasets and complex calculations. Lastly, the frequency of data refreshes should be carefully considered. Frequent refreshes can strain system resources and impact the user experience, so finding the right balance between data freshness and performance is essential.
Factors Affecting Direct Query Mode Performance
Direct Query mode, on the other hand, introduces a different set of performance considerations. This mode connects to the data source in real time, eliminating the need to load data into a local cache. However, the speed and reliability of the data source connection become critical factors. A slow or unreliable connection can lead to delays in query execution, impacting the user experience. Additionally, the complexity of queries plays a significant role in Direct Query mode. Complex queries involving multiple data sources or intricate calculations can result in slower
performance. Itâs imperative to optimize your queries to ensure they run efficiently. Furthermore, the performance of Direct Query mode relies heavily on optimizing the data source itself. Proper indexing and tuning of the data source are essential for fast query execution. Lastly, managing concurrency is vital in this mode, as multiple users accessing the same data source concurrently can lead to performance challenges. Therefore, implementing effective concurrency management is necessary to maintain a smooth user experience.
Optimization Tips for Import vs Direct Query Power BI
Several optimization strategies can be employed to enhance the performance of both import and Direct Query modes. First and foremost, data cleansing should be a priority. Cleaning and preprocessing the data before importing or connecting in Direct Query mode can significantly reduce unnecessary data, improving performance. Data compression techniques should also be utilized to reduce data size and optimize memory usage, especially in import mode. Implementing appropriate indexing strategies is crucial in both modes. In Direct Query mode, this ensures that tables in the data source are well-indexed for faster query execution, while in import mode, it helps with data retrieval efficiency. Aggregations can be employed in import mode to precompute summarized data, substantially boosting query performance. Partitioning large datasets is another valuable technique for import mode, as it helps distribute the load and improves data refresh times. Regular performance monitoring is essential to identify and address bottlenecks, ensuring data analysis and reporting remain efficient over time.
Security and Data Sensitivity when Using Import vs Direct Query Power BI
Data Security in Import Mode
Regarding data security in import mode, protecting the data stored in the local cache is paramount. Access control measures should be implemented to restrict data access based on user roles and permissions. This ensures that only authorized individuals can view and interact with sensitive data. Encryption is another critical aspect of data security at rest and in transit. Encrypting the data protects it from unauthorized access or interception during transmission. Furthermore, maintaining audit logs is essential for tracking data access and changes made to the data model. This auditing capability enhances security and aids in compliance and accountability efforts.
Data Security in Direct Query Mode
In Direct Query mode, data security focuses on securing data at the source. Secure authentication methods should be implemented to ensure that only authorized users can access the data source. Proper authorization mechanisms must be in place to control access at the source level, ensuring that users can only retrieve the data they are entitled to view. Additionally, data masking techniques can be employed to restrict the exposure of sensitive information in query results. By implementing data masking, you protect sensitive data from being inadvertently exposed to unauthorized users, maintaining high data security and privacy. Overall, in both import and Direct Query modes, a robust data security strategy is vital to safeguard sensitive information and maintain the trust of users and stakeholders.
Compliance and Privacy Considerations: Import vs Direct Query Power BI
Compliance and privacy considerations are paramount in data analysis and reporting using import or Direct Query modes. Ensuring compliance with regulations such as GDPR and HIPAA is a top priority. This involves controlling data access, implementing encryption measures, and defining data retention policies that align with legal requirements. Data residency is another critical aspect to consider. Determining where your data is stored and transmitted is essential to ensure compliance with regional data residency regulations and restrictions. Data anonymization or pseudonymization should also be part of your compliance strategy to protect individual privacy while still allowing for meaningful analysis. Furthermore, consent management mechanisms should be in place, enabling users to provide explicit consent for data processing and sharing. These considerations collectively form a robust compliance and privacy framework that ensures your data analysis practices adhere to legal and ethical standards.
Data Modeling and Transformation
Data modeling in import mode involves structuring your data to optimize the efficiency of data analysis. One of the critical principles often applied in this mode is the use of a star schema. Data is organized into fact tables and dimension tables in a star schema. Fact tables contain the core business metrics and are surrounded by dimension tables that provide context and attributes related to those metrics. This schema design simplifies query performance, allowing for more straightforward navigation and data aggregation.
Calculated columns play a crucial role in import mode data modeling. By creating calculated columns for frequently used calculations, you can improve query speed. These calculated columns can encompass various calculations, such as aggregations, custom calculations, or even derived dimensions, which simplify and expedite generating insights from your data. Furthermore, defining relationships between tables is essential in import mode to ensure data can be accurately and efficiently navigated. Properly defined relationships enable users to create meaningful reports and visualizations.
Data Modeling in Direct Query Mode
In Direct Query mode, data modeling focuses on optimizing query performance rather than designing data structures in the local cache. Crafting efficient SQL queries is paramount in this mode. Ensuring your queries are well-structured and utilizing database-specific optimizations can significantly impact query response times. Query optimization techniques, such as query folding, are valuable for pushing data transformations back to the data source, reducing the amount of data transferred and processed by the reporting tool.
Additionally, proper indexing of tables in the data source is critical. A well-indexed data source can dramatically improve query execution speed. Indexes enable the database to quickly locate the necessary data, reducing the time it takes to retrieve and process results. Data modeling in Direct Query mode is closely tied to the performance optimization of the underlying data source. Ensuring the data source is well-tuned for query performance is essential for delivering fast and responsive reports.
Differences and Limitations Visualization and Reporting
Building Reports in Import Mode
Building reports in import mode offers several advantages, primarily regarding the complexity and richness of visualizations and dashboards that can be created. Since data is stored locally in a cache, it is readily available for immediate manipulation and visualization. This means you can make interactive and visually appealing reports with various visual elements, including charts, graphs, and complex calculated fields. However, there are limitations to consider. Reports in import mode may suffer from slower refresh times, especially when dealing with large datasets. Additionally, real-time data updates often require scheduled refreshes, resulting in data lag between updates and the availability of new information in reports.
Building Reports in Direct Query Mode
Building reports in Direct Query mode offers real-time data access without the need for data duplication. This model is well-suited for scenarios where up-to-the-minute data is critical. However, the level of complexity in visualizations may be limited compared to import mode. Due to the need for real-time querying and potential performance constraints, some complex visualizations may not be feasible. High-concurrency scenarios can also impact query responsiveness, as multiple users accessing the same data source concurrently may experience delays in query execution.
Deployment and Sharing
Publishing Reports in Import Mode
Publishing reports in import mode is relatively straightforward, as the reports are self-contained with data stored in the local cache. These reports can be published on various platforms and accessed by users without directly connecting to the original data source. Users can interact with these reports offline, which can be advantageous when internet connectivity is limited. However, managing data refresh schedules effectively is essential to ensure that the data in the reports remains up-to-date.
Publishing Reports in Direct Query Mode
Publishing reports in Direct Query mode requires a different approach. These reports are connected to live data sources, and as such, they require access to the data source to provide interactivity. Users must have access to the data source to interact with the reports effectively. This modeâs dependency on data source availability and performance should be considered when publishing reports. Ensuring the data source is maintained correctly and optimized to support the reporting workload is essential.
Sharing Options and Limitations
Sharing options differ between import and Direct Query modes due to their distinct characteristics. Import mode reports are more portable, containing the data within the report file. Users can share these reports independently of the data source, simplifying distribution. In contrast, Direct Query reports have more stringent requirements since they rely on a live connection to the data source. This means that sharing Direct Query reports may involve granting access to the data source or hosting the reports on a platform that provides the necessary data connectivity. These considerations should be factored into your sharing and distribution strategy.
Best Practices: Import vs. Direct Query Power BI
Like most SaaS products that are packed full of optimal or suboptimal decisions that will meet expectations during testing time, and we recommend you begin testing as soon as possible to ensure your system can handle Direct Query or the Import Mode, which has a limit of 8 total schedule windows unless you decide to utilize the PowerBI REST API, we will save that for another blog, and know itâs a good step for batch style refreshes that can be accessed via standard programming languages or data engineering services.
Best Practices for Import Mode
To optimize performance in import mode, several best practices should be followed. First, data models should be optimized for speed and efficiency. This includes using star schemas, calculated columns, and well-defined relationships between tables. Data compression and aggregation techniques should be employed to reduce data size and enhance memory usage. Scheduled data refreshes should be during off-peak hours to minimize user disruption. Monitoring and managing memory usage is essential to prevent performance degradation over time, as large datasets can consume substantial system resources.
Best Practices for Direct Query Mode
In Direct Query mode, query optimization is critical. Craft efficient SQL queries that fully utilize the databaseâs capabilities and optimizations. Ensure that tables in the data source are appropriately indexed to facilitate fast query execution. Monitoring data source performance is crucial, as it directly impacts the responsiveness of Direct Query reports. Educating users on query performance considerations and best practices can also help mitigate potential issues and ensure a smooth user experience.
Common Pitfalls to Avoid
Common pitfalls must be avoided in Import and Direct Query modes to ensure a successful data analysis and reporting process. Overloading import mode with massive datasets can lead to performance issues, so itâs essential to balance the size of the dataset with available system resources. In Direct Query mode, neglecting to optimize data source indexes can result in slow query performance, harming the user experience. Implementing proper data security and compliance measures in both modes can expose sensitive data and lead to legal and ethical issues. Finally, neglecting performance monitoring and optimization in either mode can result in degraded performance and user dissatisfaction.
Use Cases and Examples
Industry-specific Examples
Data analysis and reporting are critical in decision-making and operations in various industries. For instance, in the retail industry, businesses use data analysis to track sales performance, optimize inventory management, and make data-driven pricing decisions. Data analysis helps monitor patient outcomes, assess treatment efficacy, and improve healthcare delivery. The finance sector relies on data analysis for tracking financial transactions, detecting fraud, and making investment decisions. Each industry has unique challenges and opportunities where data analysis can drive improvements and efficiencies.
Real-world Use Cases
Real-world use cases for data analysis and reporting are diverse and encompass many applications. Sales analytics is an everyday use case involving analyzing sales data by region, product, and time to identify trends and opportunities. Customer engagement analysis helps businesses measure customer satisfaction, engagement, and loyalty, providing insights to enhance the customer experience. Operational efficiency analysis identifies bottlenecks, streamlines processes, and optimizes organization resource allocation. These use cases illustrate how data analysis and reporting can be applied across various domains to improve decision-making and drive positive outcomes.
Conclusion
In conclusion, choosing between import mode and Direct Query mode depends on your specific data analysis and reporting needs and your data environment’s capabilities: performance, security, and compliance considerations.
Here is an excellent place to start inviting others to the conversation and ensure others understand what is happening without extra engineering. Like executives getting LIVE reports versus EXTRACTS, maybe this is where we talk about STREAMING?
All modes offer unique advantages and limitations, and a well-informed decision should align with your organizationâs goals and requirements. Staying updated on emerging trends and developments in data analysis tools is essential to adapt to evolving needs and technologies. Practical data analysis and reporting are critical for informed decision-making and success in todayâs data-driven world.
The ability to network with data science professionals is a valuable skill that can open doors to exciting opportunities and foster your personal and professional growth. It would be best if you created long-lasting connections while networking. Long-lasting relationships that will get you ahead in life, and similar to attending school, these are people who you can depend on for your entire lifetime.
Whether you are an Excel guru, analyst, engineer, intern, office admin, executive, or just someone interested in data science, building a solid network of data professionals can provide insights, mentorship, collaboration opportunities, and potential job prospects.
This article will guide you through the essential steps to effectively network with data professionals.
The more you practice, the more you can recall these successful attempts and your confidence will grow.
Being a technical person, it’s easy to rabbit-hole unnecessarily about strange topics related to what you love! Learning social cues before you start messaging people or meeting new people is good. Every new person will help you learn. Document everything in a spreadsheet and create a dashboard to share your success over some time.
How Can I Tell If I’m Being Annoying?
It can be challenging to understand whether or not you’re coming across as being annoying, and we think it’s best to be yourself, honest, and truthful. However, what if being yourself isn’t working? Perhaps we can pick up some new strategies before we begin. Often, looking back on previous convos can be an excellent way to realize what strategies are working and what’s not working. This is why many organizations are moving to NLP solutions built into their phone call systems; this allows them to hear what is working and what is not working with immediate feedback.
It’s essential to be aware of social cues to determine if you might be annoying someone during a conversation. Here are some signs that may indicate the other person is getting annoyed:
Body Language: Watch for signs of discomfort in their body language. These may be signs of irritation or discomfort if they fidget, cross their arms, or avoid eye contact.
Short Responses: If the person begins responding with quick, curt answers or seems disinterested in continuing the conversation, it’s a sign that they may not be enjoying the interaction.
Repetitive Topics: If you keep bringing up the same topic or steering the conversation back to yourself, the other person may find it annoying. It’s crucial to balance talking about yourself with showing genuine interest in their thoughts and experiences.
Overwhelming Questions: If you’re bombarding the person with too many questions or questions that are too personal, they may feel overwhelmed or uncomfortable.
Lack of Engagement: If the other person stops asking you questions or stops actively participating in the conversation, it could be a sign that they’re not enjoying the interaction.
Interrupting: Constantly interrupting or not allowing others to speak can be annoying. It’s important to let them express themselves and actively listen.
Unwanted Advice: Offering unsolicited advice or opinions on sensitive topics can be irritating. It’s generally best to offer advice or opinions when asked.
Negative Tone: If you sense a change in the person’s tone, such as becoming more curt or sarcastic, it may indicate annoyance.
Physical Distancing: If the person physically moves away from you during the conversation, it’s a clear sign that they may be uncomfortable.
Excessive Texting or Distraction: If the person starts checking their phone frequently or appears distracted, it could indicate that they are no longer engaged in the conversation.
It’s essential to be sensitive to these cues and adjust your behavior accordingly.
While working in customer service jobs before college, I spoke to hundreds of people per day, and had an opportunity to see what’s working for me and what’s not. Then while working at Tableau Software, I attended many sales conferences, and used my years of customer service experience and applied it to my interpersonal communication skills.
by tyler garrett, founder of dev3lop
Interpersonal communication is an exchange of information between two or more people. It is also an area of research that seeks to understand how humans use verbal and nonverbal cues to accomplish a number of personal and relational goals.
from wiki
If you suspect you may be annoying someone, it’s a good idea to politely ask if everything is okay or if they’re still interested in the conversation.
Here are ten ideas you can ask someone during the convo to check to see if you’re being annoying. I enjoy #1. Hopefully, these spark ideas on how to communicate comfortably with others.
“I hope I’m not talking too much about myself. How’s the conversation been for you?”
“Is there anything you’d like to discuss or any more interesting topic?”
“Am I being too intense or enthusiastic about this topic?”
“Are there any specific things I’ve said or done that bother you?”
“Is there anything I can do to make our conversation more enjoyable?”
“I’ve noticed I’ve been asking a lot of questions. Is there anything else you’d like to share or discuss?”
“Is there a specific way you prefer to have conversations I should be aware of?”
“Do you have any feedback or suggestions on how I can improve our interaction?”
“Is there a topic or subject you’re passionate about that we can discuss instead?”
“I want to ensure you’re comfortable in our conversation. If there’s anything I should change, please let me know.”
Respect their response and be prepared to exit the conversation if needed gracefully. Remember that not everyone will find the same annoying, so it’s also essential to be yourself and know the other person’s comfort level.
Managing Toxic Users in Online Communities
Dealing with Toxic Online Communities and Users: 6 Strategies for Safeguarding Your Well-Being
While meeting data science gurus, you’ll quickly learn not every community is the same and not all data gurus are the same. Encountering toxic behavior or a toxic online community/user can be distressing, and it’s inevitable. Here are six strategies to help you navigate and protect your well-being in such situations:
Limit Interaction: The first and most effective step is to limit your interaction with toxic individuals or communities. Avoid engaging in arguments or responding to negative comments. If possible, mute, block, or unfollow toxic users to minimize exposure to their content.
Seek Support: Reach out to friends, family, or trusted online friends for emotional support. Discussing your experiences with those you trust can provide a sense of validation and help you process your feelings about the situation.
Report and Document: If the toxicity crosses a line into harassment or abuse, use the platform’s reporting mechanisms to alert moderators or administrators. Document any offensive or harmful content, which can help build a case if needed.
Maintain Boundaries: Set clear boundaries for what you’re willing to tolerate. Don’t be afraid to assert yourself and express your discomfort when necessary. Remember that it’s okay to disengage from any community or individual who consistently exhibits toxic behavior.
Importance of Blocking: Blocking toxic individuals is crucial in protecting your online well-being. Blocking prevents further interaction and provides peace of mind, allowing you to curate a safer and more positive online environment.
Self-Care: Prioritize self-care. Engage in activities that bring you joy, relaxation, and peace. This may include stepping away from online interactions, pursuing hobbies, or practicing mindfulness. Taking care of your mental and emotional well-being is essential in the face of toxicity.
Dealing with toxicity online can be challenging, but employing these strategies, including the importance of blocking, can help you safeguard your well-being and maintain a positive online experience.
Attend Conferences and Meetups
Now that you’re ready to leave the nest check out a local meetup. It’s time to leave the toxic people behind!
Let’s think hard: where can we meet tech people? You can hear about new companies, companies hiring, people pitching their new products, and even sitting at a local coffee shop.
However, once you’re all done with coffee, you could head to data science conferences and meetups, as they are the cornerstone of building a robust network within the data professional community. Often, it’s one big party from sun up until sun down; most of the time, everyone is having a great time, and it’s always an easy way to meet someone with an interest equal to yours.
Here’s an in-depth exploration of why these events are so effective for networking:
1. Networking Opportunities: Data science conferences and meetups attract professionals from various backgrounds and expertise levels. This diversity provides an ideal setting for expanding your network. Whether you’re a seasoned data scientist or just starting out, you’ll have the chance to connect with like-minded individuals who share your passion for data.
2. Knowledge Sharing: These events are hubs of knowledge sharing. Not only do you get to attend presentations and workshops led by experts, but you can also engage in discussions with fellow attendees. The exchange of ideas, experiences, and insights can be precious, enhancing your understanding of the field.
3. Exposure to the Latest Trends: Data science is rapidly evolving. Conferences and meetups often feature talks on cutting-edge technologies, methodologies, and tools. By staying informed about the latest trends and developments, you can position yourself as an informed and forward-thinking professional, which can be attractive to potential collaborators or employers.
4. Access to Experts: These events frequently bring in prominent figures in the data science world as speakers. Meeting and interacting with these experts can be invaluable for your career. You can gain insights, seek advice, and even establish mentor-mentee relationships with individuals who have succeeded.
5. Potential Mentorship: Conferences and meetups are excellent places to find mentors or advisors who can guide your data science journey. Many experienced professionals are open to offering guidance, sharing their experiences, and helping newcomers navigate the intricacies of the field.
6. Serendipitous Encounters: Sometimes, the most fruitful connections happen by chance. You might meet someone who shares a common interest, has complementary skills, or works on a project that aligns with your goals. These serendipitous encounters can lead to productive collaborations, research projects, or job opportunities.
7. Building Your Reputation: Active participation in conferences and meetups can help you establish your reputation in the data science community. You can showcase your expertise and gain recognition as a knowledgeable and engaged professional by asking insightful questions during sessions, giving presentations, or contributing to panel discussions.
8. Friendships and Support: Beyond professional benefits, attending conferences and meetups can lead to personal connections and friendships. Having a network of supportive peers can be instrumental in overcoming challenges and celebrating successes.
In conclusion, attending data science conferences and meetups is more than just a way to acquire knowledge. It’s a strategic approach to building a network of professionals who can offer guidance, collaboration, mentorship, and even potential job opportunities. By actively participating in these events and seizing networking opportunities, you can enrich your career and make lasting connections in the data science world.
Utilize LinkedIn
LinkedIn is a large website where you can host your resume and have headhunters reach out to you about jobs. There’s more to it but if you’re networking, you’re also probably on the market to get a job. Having a LinkedIn is a best practice.
Why use LinkedIn? LinkedIn is a powerful tool for networking with data professionals.
Once you’ve created a well-structured LinkedIn profile highlighting your skills, achievements, and interests in the data field. You can begin to join data science groups and engage in discussions, connect with professionals, and reach out for informational interviews or collaborations.
You’re now a content creator; you need to regularly share relevant content and insights to establish your credibility within the data community. It’s not mandatory, but it’s a great way to meet others and tell the algorithm you’re essential, giving you more visibility on your posts.
Utilize LinkedIn for Effective Networking with Data Professionals
LinkedIn has emerged as an indispensable tool for networking and career development in today’s digital age. When it comes to the data science field, here’s how you can harness the power of LinkedIn for networking with data professionals:
1. Optimize Your Profile: Your LinkedIn profile is your digital identity in the professional world. To make the most of it, ensure your profile is complete, accurate, and engaging. Highlight your skills, education, and relevant experience. Use a professional photo and write a compelling summary that encapsulates your passion for data and career goals.
2. Join Data Science Groups: LinkedIn offers various groups and communities tailored to diverse professional interests. Look for data science groups, such as “Data Science Central,” “Data Science and Machine Learning,” or specific groups related to your niche within data science. Joining these groups is an excellent way to connect with like-minded individuals who share your interests and are actively involved in the field.
3. Engage in Discussions: Once you’re a member of these groups, actively engage in discussions. Share your insights, ask questions, and participate in conversations related to data science topics. By contributing meaningfully to these discussions, you demonstrate your knowledge and passion for the field, and you’ll start to gain visibility among your peers.
4. Connect with Professionals: Leverage LinkedIn’s networking capabilities by connecting with data professionals whose work or interests align with yours. When sending connection requests, personalize your messages, indicating your desire to connect and potentially collaborate or learn from each other. A personalized message is more likely to be well-received than a generic one.
5. Informational Interviews: LinkedIn is a valuable platform for contacting data professionals for informational interviews. If you’re interested in a specific career path or seeking advice, don’t hesitate to request a brief conversation. Many professionals are open to sharing their insights and experiences, making informational interviews a potent networking tool.
6. Showcase Your Knowledge: Establish your credibility within the data community by regularly sharing relevant content, such as articles, research papers, or your own insights on data science trends. Sharing valuable content keeps you engaged with your network and positions you as an informed and influential professional.
7. Personal Branding: Use LinkedIn to build your brand in data science. This involves consistently sharing your experiences, achievements, and the projects you’ve worked on. When others see your accomplishments, they’re more likely to respect and connect with you as a professional.
8. Recommendations and Endorsements: Ask for recommendations and endorsements from colleagues, mentors, or supervisors who can vouch for your skills and expertise. These endorsements add credibility to your profile and make you more attractive to potential employers or collaborators.
9. Stay Updated: LinkedIn is a dynamic platform, and the more active you are, the more likely you are to stay on the radar of your connections. Regularly update your profile with new skills, experiences, and accomplishments. Share industry news and engage with your connections’ content to stay in the loop with the latest developments in data science.
In summary, LinkedIn is a powerful networking tool for data professionals. By creating a strong and engaging profile, actively participating in data science groups, connecting with professionals, sharing insights, and using the platform to seek advice or collaborations, you can expand your network, enhance your credibility, and open doors to a wealth of opportunities within the data science community.
Online Forums and Communities
Participating in online data science forums and communities like Stack Overflow, Kaggle, or Reddit’s r/datascience can help you connect with professionals and enthusiasts. Each community has its ups and downs; consider them an ocean of possibility and take everything with a grain of salt.
Ensure you actively contribute to discussions, seek advice, and offer assistance to others.
These communities often provide a supportive environment for learning and networking.
Leveraging Online Forums and Communities for Networking in Data Science
In the digital age, online forums and communities have become invaluable hubs for knowledge sharing, networking, and collaboration within the data science field. Here’s how you can make the most of these online platforms:
1. Active Participation: Engaging with online data science communities requires active participation. Whether you choose platforms like Stack Overflow, Kaggle, or Reddit’s r/datascience, actively contribute to discussions, respond to questions, and join conversations on topics that interest you. Participating regularly demonstrates your passion for the field and makes yourself more visible to others in the community.
2. Seek Advice and Share Knowledge: Online forums provide an excellent platform to seek advice when facing challenges or uncertainties in your work or studies. Don’t hesitate to ask questions; you’ll often find experienced professionals willing to provide guidance. Conversely, offer assistance and share your knowledge if you have expertise in a particular area. This reciprocal exchange of information is a powerful networking tool.
3. Showcase Your Skills: These platforms allow you to showcase your skills and expertise. You establish yourself as a knowledgeable and helpful professional when you help others by providing thoughtful and insightful responses. This can lead to others reaching out to connect or collaborate with you.
4. Collaboration Opportunities: Online communities are teeming with individuals working on data-related projects. By actively participating in these communities, you increase the likelihood of finding potential collaborators. Whether you’re looking for partners on a research project, a coding challenge, or a data competition, these platforms are fertile ground for forming connections with like-minded professionals.
5. Learning and Skill Development: Online forums are not just about networking but also about continuous learning. You’ll gain valuable insights and learn new skills by participating in discussions and seeking answers to your questions. This helps you advance in your data science journey and gives you more to bring to the table when networking with others.
6. Building Your Reputation: A strong presence in online data science communities can help you build your reputation in the field. You become a respected figure in the community when you consistently provide high-quality responses, engage in thoughtful discussions, and showcase your skills. Others will likely contact you for collaborations, advice, or job opportunities.
7. Supportive Environment: Many data science forums and communities have a culture of support and encouragement. The sense of camaraderie and shared passion for data science creates a welcoming environment for networking. You’ll often find individuals who are eager to help and share their experiences.
8. Networking Beyond Borders: Online communities are not bound by geographical constraints. You can connect with data professionals worldwide, gaining a diverse perspective and expanding your network far beyond your local area.
9. Staying Informed: Many online platforms feature discussions on the latest trends, tools, and technologies in the data science field. Staying active in these communities keeps you updated about industry developments and enables you to discuss emerging trends.
In conclusion, participating in online data science forums and communities is an effective way to connect with professionals and enthusiasts, learn, share your expertise, and find collaboration opportunities. The supportive environment of these platforms makes them ideal for networking, and active involvement can help you build a strong network while enhancing your knowledge and skills in the field.
Collaborate on Projects
Collaborative projects are an excellent way to network with data professionals. Join data-related projects on platforms like GitHub or Kaggle and contribute your skills and expertise. Working together on real-world projects builds your experience and allows you to connect with people who share similar interests.
Harnessing the Power of Collaborative Projects for Networking in Data Science
Collaboration on data-related projects is a dynamic and practical approach to network with data professionals while simultaneously honing your skills and gaining hands-on experience. Here’s an in-depth look at the benefits and strategies of collaborating on data projects:
1. Real-World Experience: Collaborative projects allow you to apply your data science skills to real-world problems. By actively participating in these projects, you gain practical experience and enhance your problem-solving abilities. This hands-on experience is highly regarded by employers and collaborators alike.
2. Skill Development: Working on collaborative projects exposes you to diverse challenges, data sets, and problem domains. This exposure helps you expand your skill set, allowing you to become a more versatile and knowledgeable data professional.
3. Networking with Peers: Collaborative platforms such as GitHub, Kaggle, and GitLab often attract a community of data enthusiasts and professionals. By contributing to open-source projects or joining data challenges, you connect with like-minded individuals who share your passion for data science. These peers can become valuable connections for future collaborations or career opportunities.
4. Exposure to Diverse Perspectives: Collaborative projects often involve individuals from various backgrounds, each offering a unique perspective and set of skills. This diversity can lead to innovative solutions and foster creative thinking. Engaging with people from different professional and cultural backgrounds broadens your horizons and enriches your problem-solving capabilities.
5. Building a Portfolio: The projects you collaborate on are a testament to your skills and expertise. A portfolio showcasing your contributions to meaningful data projects can be a powerful tool for attracting potential collaborators, mentors, and employers.
6. Open Source Contributions: Open-source projects are a great way to give back to the data science community while expanding your network. Many data professionals appreciate contributions to open-source tools and libraries, which can lead to recognition and new opportunities within the community.
7. Interdisciplinary Collaboration: Data science often intersects with various fields, from healthcare to finance to climate science. Collaborative projects offer a chance to work with professionals from other domains. This interdisciplinary experience can provide unique networking opportunities and broaden your understanding of how data science applies across industries.
8. Problem Solving and Critical Thinking: Collaborative projects involve tackling complex data problems. By participating in these projects, you not only enhance your technical skills but also develop your problem-solving and critical-thinking abilities. These qualities are highly valued in the data science community and can set you apart.
9. Enhanced Communication Skills: Collaborating with others on data projects requires effective communication. You’ll need to articulate your ideas, share your progress, and clearly explain your work. These experiences can improve your communication skills, which are crucial for networking and collaboration.
10. Showcasing Your Value: When you actively contribute to a collaborative project, you demonstrate your dedication and value as a team player. This can lead to more meaningful connections with peers and mentors who appreciate your commitment to the project’s success.
In conclusion, collaborative projects are not just a means of building experience and enhancing your skills but also an exceptional way to network with data professionals who share your interests and passions. Through hands-on collaboration, you can build a strong network, expand your horizons, and open the door to exciting opportunities within the data science community.
Attend Webinars and Online Courses
In the age of digital learning, webinars, and online courses offer an excellent opportunity to network with data professionals from the comfort of your home. Sign up for webinars, workshops, and courses hosted by experts in the field. Engage in Q&A sessions and discussion forums to connect with presenters and fellow participants.
The Power of Webinars and Online Courses for Networking in Data Science
In our digital era, webinars and online courses have revolutionized learning and networking. They provide an incredible opportunity to connect with data professionals, learn from experts, and expand your network. Here’s a detailed exploration of how you can effectively network through webinars and online courses:
1. Convenience and Accessibility: Webinars and online courses allow you to access valuable content and network with professionals without geographical limitations. You can participate from the comfort of your home or office, making it a flexible and accessible way to engage with the data science community.
2. Expert-Led Learning: Many webinars and online courses are led by industry experts and thought leaders in the data science field. Attending these events expands your knowledge and gives you access to influential professionals who are often open to networking and engagement.
3. Engage in Q&A Sessions: Most webinars and online courses include interactive Q&A sessions. This is an excellent opportunity to ask questions, seek clarification, and interact with presenters. Engaging in these sessions allows you to stand out and be remembered by the experts leading the event.
4. Discussion Forums: Many online courses offer discussion forums where participants can interact, share insights, and discuss the course content. These forums are platforms for learning and great places to connect with like-minded individuals. Actively participating in discussions can lead to networking opportunities.
5. Build a Learning Network: As you attend webinars and online courses, you’ll naturally connect with fellow participants who share your interests and goals. These connections form the basis of your “learning network,” a group of individuals with whom you can exchange knowledge, insights, and experiences.
6. Gain Exposure to New Ideas: Webinars and online courses often introduce you to new ideas, trends, and technologies in the data science field. By staying informed and discussing these emerging topics, you position yourself as someone passionate about staying up-to-date, which can be attractive to potential collaborators or employers.
7. Networking Beyond Borders: Online courses often have a global reach, allowing you to network with data professionals worldwide. This diversity can provide unique perspectives and create networking opportunities beyond your local network.
8. Connecting with Instructors: Instructors of online courses are typically experienced professionals or academics in the field. Engaging with them can lead to valuable networking opportunities. You can ask for advice, share your experiences, and potentially establish a mentorship or collaboration with them.
9. Expand Your Skillset: Online courses are designed to provide in-depth knowledge and skill development. As you gain expertise in specific areas of data science, you become a more attractive collaborator and network contact for those looking for individuals with specialized skills.
10. Share Insights: When participating in webinars and online courses, you can share your own insights and experiences. This positions you as a valuable contributor to the community, and others may reach out to connect with you based on your contributions.
In conclusion, webinars and online courses offer a convenient and effective way to network with data professionals. By actively engaging in Q&A sessions, discussion forums, and other interactive components, you can connect with experts, build your learning network, and stay on the cutting edge of data science while expanding your connections within the field.
Seek Mentorship
When I was on the Tableau Consulting team at Tableau (before Salesforce acquisition), I was lucky to be mentored by many different people from around the world, and that’s why I think it’s important to begin seeking mentorship as soon as possible. Be sure to diversify your mentorship and always be on the look out for your next mentor.
Mentorship can be a valuable asset in your professional journey. Reach out to experienced data professionals you admire and respect, and express your interest in learning from them. A mentor can provide guidance, insights, and a network of their own that can greatly benefit your career.
The Value of Mentorship in Data Science: A Guiding Light for Your Career
Mentorship is a time-honored practice with immense potential for anyone looking to grow and excel in their professional journey, particularly in data science. Here’s a detailed exploration of how mentorship can be a powerful asset for your career:
1. Learning from Experience: One of the primary advantages of seeking a mentor in data science is the opportunity to learn from someone who has walked the path before you. An experienced mentor can provide valuable insights, share lessons from their journey, and guide you away from common pitfalls and challenges.
2. Tailored Guidance: A mentor can offer personalized guidance that addresses your unique career goals and challenges. By understanding your specific needs and aspirations, a mentor can provide targeted advice and recommendations, making your career development more effective and efficient.
3. Access to a Network: Mentors typically have extensive networks in the industry. You gain access to their professional contacts and connections by developing a mentor-mentee relationship. This expanded network can open doors to collaboration, job opportunities, and introductions to other influential figures in data science.
4. Accountability and Motivation: A mentor can be an accountability partner, helping you set and achieve your career goals. Regular check-ins with your mentor can keep you motivated and on track, ensuring that you progress in your career.
5. Insight into Best Practices: Your mentor can provide valuable insights into best practices in data science. They can help you understand the tools, techniques, and approaches that are most relevant and effective in the field, saving you time and effort in staying up-to-date.
6. Soft Skills Development: Data science is not just about technical skills; soft skills such as communication, problem-solving, and project management are equally crucial. A mentor can help you develop and refine these skills, making you a more well-rounded professional.
7. Feedback and Constructive Criticism: Mentors can provide feedback and constructive criticism, helping you identify areas where you can improve and grow. This feedback is often candid and based on their extensive experience, making it a valuable resource for personal development.
8. Encouragement and Confidence: A mentor can be a source of encouragement and confidence-building. They can provide reassurance during challenging times, helping you navigate setbacks and maintain a positive attitude as you progress in your career.
9. Personal Growth: Mentorship often extends beyond your professional life, positively impacting your personal development. The wisdom and guidance shared by your mentor can influence your decision-making, problem-solving abilities, and even your values and principles.
10. Legacy and Giving Back: Many experienced data professionals find fulfillment in giving back to the community by mentoring others. By being open to mentorship, you not only gain from their knowledge but also contribute to the passing down of knowledge and expertise within the data science field.
11. Networking Opportunities: You can also gain access to their professional circle through your mentor. This can result in introductions and networking opportunities that might not have been possible without their guidance.
In conclusion, mentorship is a powerful asset in your professional journey, especially in data science. Seek out experienced data professionals who inspire you, and express your interest in learning from them. A mentor can provide guidance, insights, access to a valuable network, and personalized support that can significantly benefit your career. Mentorship is a two-way street, often leading to mutually beneficial relationships that enrich the mentor and the mentee.
Use Social Media
In addition to LinkedIn, other social media platforms like Twitter can be helpful for networking in the data field. Follow data professionals, influencers, and relevant organizations. Engage in conversations, retweet, and share interesting content. Social media provides a more casual and interactive way to connect with others.
Leveraging Social Media for Networking in Data Science
In the digital age, social media platforms have evolved into powerful tools for networking and connecting with professionals in the data science field. Here’s an in-depth look at how you can maximize your use of social media for networking:
1. Broaden Your Reach: In addition to LinkedIn, explore platforms like Twitter, which offer a more casual and interactive approach to networking. By diversifying your social media presence, you can connect with a wider range of data professionals, influencers, and organizations.
2. Follow Data Professionals and Influencers: Start by identifying and following data professionals, industry influencers, thought leaders, and experts on social media platforms. Their posts, insights and shared content can provide knowledge, industry updates, and valuable connections.
3. Stay Informed: Social media is an excellent resource for staying informed about the latest trends, tools, and technologies in the data science field. By following and engaging with industry leaders, you’ll be privy to their expert opinions and insights into the rapidly evolving data landscape.
4. Engage in Conversations: Actively engage in conversations related to data science. Comment on posts, share your thoughts, ask questions, and participate in discussions. Contributing to these conversations allows you to showcase your knowledge, learn from others, and establish connections with like-minded individuals.
5. Share Valuable Content: Share interesting articles, research papers, blog posts, or insights related to data science. By consistently sharing valuable content, you position yourself as someone who is informed and engaged in the field. This can attract others who appreciate your contributions.
6. Retweet and Amplify: Retweet or share posts from data professionals and organizations that you find interesting or insightful. This spreads valuable information within your network and helps you connect with the original posters. It’s a way of showing appreciation and building rapport.
7. Participate in Twitter Chats and Hashtags: Many social media platforms, especially Twitter, host regular chats and discussions on specific data science topics using hashtags. Participate in these discussions to connect with experts and enthusiasts, learn from others, and share your insights.
8. Seek Advice and Guidance: Don’t hesitate to contact data professionals on social media if you have questions or seek advice. Many professionals are open to providing guidance and sharing their experiences, and social media offers a direct channel for these interactions.
9. Personal Branding: As you actively participate in discussions and share valuable content, you’ll build your brand within the data science community. Your online presence and contributions can make you more recognizable and memorable to potential collaborators and employers.
10. Networking Events: Social media platforms promote data science-related events, webinars, and conferences. Following these events and participating in their discussions can help you connect with fellow attendees and expand your network within the data community.
11. Be Authentic: Be yourself on social media. Authenticity is appreciated, and forming genuine connections with others is more likely when you are true to your voice and values.
In conclusion, social media platforms like Twitter offer a casual yet powerful means of networking within the data science field. By actively engaging with content, sharing your insights, and connecting with professionals and influencers, you can expand your network, stay informed, and open doors to a world of opportunities and collaborations in data science.
Attend Hackathons and Competitions
Hackathons and data science competitions are an exciting ways to meet like-minded individuals, showcase your skills, and collaborate on challenging projects. Join platforms like DataCamp, Topcoder, or HackerRank to find opportunities to compete and network with fellow participants.
Hackathons and Competitions: Catalysts for Networking and Skill Growth in Data Science
Participating in hackathons and data science competitions is a dynamic and immersive approach to networking within the data science community. These events provide an exciting skill development, collaboration, and professional network expansion platform. Here’s a detailed look at why these competitions are so valuable for networking:
1. Skill Development: Hackathons and data science competitions often present complex and real-world challenges. By participating in these events, you gain hands-on experience, apply your skills, and develop problem-solving techniques. This enhanced expertise builds your confidence and makes you a more attractive network contact.
2. Collaborative Opportunities: Most hackathons encourage collaboration. Forming teams and working with others allows you to leverage diverse skills and perspectives. Collaborators often become valuable connections for future projects or networking within the field.
3. Like-Minded Participants: Hackathons attract participants who share your passion for data science. These like-minded individuals can become your peers, collaborators, or mentors in the field. Building connections with individuals with a similar level of dedication to data science can be incredibly beneficial.
4. Competitive Edge: Successful participation in hackathons and competitions can distinguish you in the job market. Employers often value the problem-solving and teamwork skills developed in these environments. It can be a powerful addition to your professional portfolio.
5. Networking Events: Many hackathons and data science competitions feature networking events, Q&A sessions, or expert presentations. These events offer opportunities to connect with sponsors, judges, and fellow participants. Active participation in these activities can lead to meaningful connections.
6. Industry Recognition: Winning or performing well in prominent data science competitions can lead to industry recognition. Your achievements in these competitions can attract the attention of potential employers, collaborators, and mentors, ultimately expanding your network.
7. Online Platforms: Joining platforms like DataCamp, Topcoder, HackerRank, and Kaggle, or even participating in platforms like DrivenData offers you a gateway to a thriving community of data enthusiasts. These platforms host competitions and have forums, discussions, and profiles that enable networking and recognition.
8. Access to Industry Challenges: Many hackathons are sponsored by industry-leading companies and organizations. Participating in these events gives you insights into the challenges and projects relevant to these organizations. It can be a stepping stone to future job opportunities or collaborations.
9. Learning and Feedback: Hackathons provide continuous learning and feedback opportunities. Even if you don’t win, you can gain valuable feedback on your work, which can help you improve your skills and expand your network. Don’t hesitate to seek feedback from experienced participants.
10. Portfolio Building: The projects you complete during hackathons and competitions can be showcased in your professional portfolio. Sharing these achievements with potential collaborators, employers, or mentors can be a powerful conversation starter and networking tool.
11. Creativity and Innovation: These events often encourage participants to think creatively and innovatively. Engaging in such activities can help you develop a creative mindset that can benefit your career and make you more appealing to others.
In conclusion, hackathons and data science competitions are not just about winning prizes but also about the opportunities they offer for networking and skill growth. Active participation in these events can lead to collaborations, learning experiences, industry recognition, and lasting connections with like-minded individuals in the data science community.
Be Open to Informational Interviews
When you encounter data professionals whose work or career paths you admire, don’t hesitate to ask for informational interviews. These informal conversations can provide insights into their experiences, offer valuable advice, and potentially lead to future opportunities.
Embrace Informational Interviews: A Gateway to Insight and Opportunities in Data Science
Informational interviews are an often-underestimated tool for networking and personal growth in the data science field. These informal conversations can be incredibly valuable in providing insights, advice, and even future opportunities. Here’s an in-depth exploration of the benefits and strategies for making the most of informational interviews:
1. Gain Insights: Informational interviews offer a unique opportunity to gain insights into the experiences, paths, and challenges of data professionals you admire. You can learn from their journeys, achievements, and setbacks by asking thoughtful questions and actively listening.
2. Clarify Your Goals: These interviews can help you clarify your own career goals and the steps you need to take to achieve them. Through discussions with professionals who’ve walked a similar path, you can refine your own vision and develop a clearer plan.
3. Advice and Guidance: The data professionals you interview can provide valuable advice and guidance. Whether it’s about the skills you should prioritize, the organizations worth considering, or the best practices in the field, their input can be instrumental in your decision-making.
4. Expand Your Network: While the primary purpose of an informational interview is to gather insights, it can also lead to expanding your network. The professionals you interview may introduce you to others in the field, which can open doors to collaborations, job prospects, and mentorship.
5. Mutual Benefit: Informational interviews are a two-way street. They can mutually benefit you and the professional you’re speaking with. Sharing your experiences and goals can lead to reciprocal advice and potential collaborations.
6. Soft Skills Development: Engaging in informational interviews allows you to hone your communication and networking skills. These are transferable skills that are valuable not only in data science but in any professional setting.
7. Courting Mentorship: Informational interviews can be a stepping stone to mentorship. By building a rapport with a data professional, you may find a mentor willing to provide ongoing guidance and support in your career.
8. Personalization is Key: When requesting an informational interview, it’s crucial to personalize your outreach. Express why you admire their work or career and what specific insights you’re seeking. Make it clear that you value their time and expertise.
9. Prepare Thoughtful Questions: Prepare thoughtful and open-ended questions before the interview. Ask about their career journey, challenges, important milestones, and advice for someone aspiring to follow a similar path. Thoughtful questions demonstrate your genuine interest and respect.
10. Active Listening: During the interview, be an active listener. Pay close attention to the responses and ask follow-up questions. A meaningful conversation rather than a one-sided interrogation will leave a positive impression.
11. Show Gratitude: After the interview, express your gratitude for their time and insights. Send a thank-you email to acknowledge their help and reiterate your appreciation. This courteous gesture can leave a lasting positive impression.
In conclusion, informational interviews are a valuable tool for networking and personal growth in data science. By reaching out to data professionals you admire, engaging in thoughtful conversations, and building genuine connections, you can gain insights, refine your career goals, and potentially open doors to opportunities in the field. These interviews are not just about taking but about creating mutually beneficial connections within the data science community.
Conclusion
Networking with data professionals is essential for personal and professional growth in the data science field. By attending conferences, participating in online communities, collaborating on projects, and seeking mentorship, you can build a strong network that will advance your career and enhance your knowledge and skills. Networking is a two-way street, so be open to helping others. As you invest time and effort into building your network, you’ll find that the data community is full of passionate individuals eager to connect, share knowledge, and collaborate.
Today’s blog is about the min(1) paradigm for KPI charting in Tableau desktop and how to make advanced KPI charts without needing slow table calculations to do the computations for you. Instead, we will show you how to utilize Tableau features to generate a better KPI solution. Welcome to learning how to create a min(1) KPI dashboard. And if you’re breaking into the data industry, this is a good tutorial for you and advanced gurus.
At times, developers think they need to generate many visualizations to move forward and float them in a specific way to create a dashboard; however, as data sizes increase, enterprises find these dashboards inefficient. To begin unlocking the power of data, we first must master the software!
What’s happening with KPI Dashboards in Tableau?
Making a lot of visualizations to generate a KPI chart is not the correct answer because it will slow down your Tableau Workbook! Instead, we have discovered decreasing the amount of visualization is the best path to optimization and easier cycles of support or additional features. The lack of visualizations means you can do more within the same workspace and keep track of less because it is consolidated into one chart.
A lot of charts means making a lot of data extract requests, a lot of live data requests, a lot of file data requests, a lot of tooltips, and a lot of filters,… Filters do a lot of performance degradation, also known as damaging the user experience due to generating technology inefficiently.
If you have ever been where end users need a faster dashboard, and the KPI dashboard is moving slowly, you have found a great wiki on building KPI charts in Tableau.
Learning how to make optimized Tableau KPI Dashboards is a path to building an operationalized Tableau solution you can utilize with any data source in the future; when you’re done building this one time, you can easily copy and paste this Tableau win into more workbooks.
This is a screenshot of many KPI charts on one visualization; pause here to see how this is possible.
What is the minimum value of 1? (min(1))
What is the minimum value of 1? What is X when computing min(1)=x?
One.
Finding the minimum of 1 isn’t rocket science. However, we must understand this logic to maximize the product’s functionality. Also, did you know min(1) is faster than max(1) or attr(1)?
You can “fix” a value behind the scenes to a static value, which keeps the axis from moving, giving you a conditional background to edit with the color marks card. The text marks card is now available to build conditional text or colors on text or text images.
The number Of Records or the value of 1 in a calculation will handle the generation of your KPI chart.
By the end of the Tableau min1 kpi charts article…
You will know how to make a KPI chart without creating multiple visualizations per user request.
You will know how to keep it simple and easy to support
You will understand what we are doing, why it works, and how to build it
You will have code to copy and paste and screenshots to follow along
Developing a good understanding of how to keep Tableau simple will eliminate the unnecessary bloat and server strain. By removing unoptimized development, you’re removing a wait time per interaction on your development. Most environments will not be happy with less than split second or second response times; it’s intuitive to understand how to improve the speeds of your workbooks without having to rearchitect the data.
“I recommend as data grows, you begin adding relational theory to your data pipeline, ask your data engineers to work through the atomic pieces of your data, bring the data to a 1nf, 2nf, or 3nf state until the data moves faster or vastly decreases in data sizes. It’s not unusual to aggregate second data into monthly data if your end users only need monthly data.”
Tyler Garret, Founder of Dev3lopcom, llc
Please keep it simple when crafting your tableau dashboards; the more simple the development, the easier it will be to maintain solutions in the future.
Using Min(1) offers an easy way to learn the product. However, you need to know the product!
Learning the min(1) solution from end to end will change how you solve problems.
“I gravitated to the min(1) solution because it offers a simple playing field for edits, support isn’t a nightmare, and it’s easy to conditionally color. Mostly, I enjoy that my complexities turn into calculations, the calculations I can either duplicate or copy paste back into the workbook, understanding the difference between these two or whether they nest into each other based on these single click is a large difference when generating calculations that need to be connected or not connected. Before getting too far in this tutorial, I recommend you understand the differences between duplicating the calculations and copying the calculations, and also understand the product has two spaces this becomes a negative development path or positive development path. The two places are calculations and dashboards/visualizations. Test the right click copy and duplicate as you evolve because it will define your speeds in the product for a long time.”
Tyler Garrett, Founder of Dev3lopcom, llc
Begin building your first min1 KPI bar chart.
Thanks for taking the time to start this min1 kpi bar chart tutorial. If you have any questions or get lost in the tutorial, please contact us or leave a comment. Happy to help.
To begin, start your tableau product!
Open a new visualization and build a min(1) bar chart.
a simple bar chart, min1 generates an axis length of 1
Okay, min(1), always 1. Build it.
Start typing min(1) – hit ENTER!
min(1) on sheet2
Not sure how I’m typing in this area? Double-click in the rows or columns below your marks card. First, double-click, and then you will see an input appear. If you want to harden these and not have them become “viz only static calls,” you can drag them into the workbook. I like this method because it’s all upfront, it names the calculations the name of the code, and it makes life relatively fast compared to not understanding this functionality and always building calculations, which leads to always needing to find those calculations, open those calculations, and edit those calculations. Also, when you need to delete the calculation, it takes longer because you have to see it in the workbook, and it’s not sitting in the rows bucket because you made a calculation. Based on this paragraph, you should better understand options and even the best path for you; it depends on what’s fast for you.
In short, let’s walk through these features.
Pro TIP: Tableau desktop offers a quick and easy user experience when adding content to the visualization or SHEET.
In a couple of places, you can double-click and typeârows, Columns, and here… on the bottom of the marks card.
Marks card is free to double-click below the squares. Then, you move it to the mark square of your preference.
Making a KPI Chart in Tableau using Min(1)
You have your min(1) bar established.
Make 3, hold ctrl+mouse1, and drag and drop to duplicate min1 across columns three times. Command+mouse1 click drag and drop for macOS.
Open up the axis on each and change the Fixed end to 1. Zero to One will be the end goal.
Click okay, and your simple min(1) mark will cover the entire pane.
Complete the following two similarly. You’re changing the value of the axis so that you can build a background on your KPI. This background is now easy to color conditionally. If you do a dual axis and make it a shape, now you have a background that can be conditionally colored and a shape that can be conditionally colored or changed. Welcome to a world of “smart KPI building” and the removal of all the strange multi-chart dashboards created due to not knowing these solutions.
Once completed, let’s add profit, sales, and a count of customers on the last KPI Chart.
Drag profit on your first minute (1) and sales on your second minute (1), and generate a calculated field for the final customer name count. I want to see the distinct count of customer names to understand the count of customers doing X.
Once you have these measure values on your KPI charts, you should start to see everything forming, and you can see we have some formatting ahead of us now.
Of course, profit is on the far left, and sales are in the middle, but when you open this solution again, you will not understand what we are offering the end users. To provide this solution to the end users, we need to make it sensible.
I am making the KPI Chart sensible.
Often, KPI charts come with an explanation, so don’t forget to explain what the number or chart means.
Also, pretend your end users are not super technical and can’t read minds.
Please do your best to help people make quick decisions; let’s fill in the blanks with sensible labels!
Click on any of the three to open the correct mark card.
The following steps are self-explanatory: filling in the rest of the labels, making the font subtly bigger, or a bold KPI chart will work for some people.
Think SMART in these steps. Don’t spend 5 hours making a KPI chart look a certain way. Don’t spend 10 hours making tooltips look cool. Ask end users for feedback before wasting time.
Open the ALL marks card and change the color to white.
Cleaning up your KPI charts
Cleaning up the KPI chart is about “simple clicks,” not “hard clicks.”
Hiding headers may hide a label, so let’s discuss.
Hiding headers may help remove the axis, but it may also release your axis’s title.
Some charts work great with the axis as the label; other times, it’s more complex. Feel free to play with different labeling methods.
I prefer labeling in the text pad editor, per marks card because it offers an endless amount of options. I enjoy formatting each marks card globally with the sheet formatting tools OR twiddle around with the mark level edits. Depends on what the end goals, which I enjoy designing on paper or whiteboard before I begin. Drawing pictures and steps is my method of visually seeing the algorithm.
by tyler garrett
Hide headers with a right click on the axis, and uncheck the show header.
Center the text on the “all marks” card because it will update across all marks cards! Work cleverly, not hard!
With text aligning in the middle, our KPI chart is starting to form.
These are personal preference changes; let your end users decide.
The key to utilizing the min(1) is the ability to augment Tableau to a style that fits your formatting needs. KPI charts have many variations; this particular one gives you complete flexibility to a text editor, which provides you with as much flexibility as you can write a calculation. The text can be conditionally colored based on logic, as can the box or min(1) bar in the background.
I prefer the min(1) bar versus a “square mark” sized large because a min(1) bar is predictable and fixable within editing the axis. As you need two marks colored in 1 mark card space, you can use a dual axis or conditionally color the text.
Right-click what you want to format in Tableau Desktop.
Right-click what you want to format is what I ask people to repeat when I teach them Tableau desktop, “right-click what you want to format.”
Today, we right-click the chart. Because… “we right-click what we want to format in Tableau!”
We drive to the “lines” because those are pesky, and someone in a meeting will likely ask you to remove the borders!
Boarders, dividers, tomatoes, potatoes, try and HIDE these and see if it’s the right path. It’s easy to get frustrated finding the right edit; I’m not saying I haven’t gone mad and turned everything off only to find the bar chart had borders turned on.
Sometimes, digging around these formatting menus is more accessible than telling someone the right buttons to hit because no one will memorize all these clicks. You will get better as you use Tableau more; keep clicking!
Open these, click none. Yay!
Notice we almost have it complete. We are still showing the zero line. Boo!
Removing the zero line seems like it gets most of it
Instead of wasting your time, double-check a few more things… zero line, axis ruler, maybe your min(1) has a border around the bar?
Axis ticks, zero lines, and grid lines.
Tired of clicking yet?!
Luckily, in the end, you’re developing your way into a tool that allows anyone to filter this sheet and give them blimp view aggregates! You might as well never build this again; you can swap out measures easily or make a three by 3 with a simple duplication of your sheet.
Omg, KPI charts without borders.
You now have three aggregated measures and a bar to color + tooltips conditionally.
Okay, we have simple KPI charts down. Let’s look at more advanced functionality and how to do it with copy-paste calculations!
Dynamic KPI charting in Tableau, building an on/off flag using dates and parameters.
Duplicate your sheet, and let’s begin our Dynamic KPI charting in Tableau! If you don’t already know, we are using the super store sample set of data that comes default with every installation of Tableau.
Building these dynamic charts or KPI charts is easy if you understand the min(1) steps we explained above and are excited about making on/off flags.
Also, if you’re learning, be willing to start thinking outside of the bucket! We will use that code to deep dive here and make our KPI chart more flexible and user-friendly.
We steal the code from our data buckets to generate three different buckets of data.
if DATETRUNC('month', [Order Date])> DATEADD('month', -([x]+ datediff('month',{MAX([Order Date])},today())) , TODAY()) then "Current Period" //make this 0 elseif DATETRUNC('month', [Order Date])> DATEADD('month', -([x]*2+ datediff('month',{MAX([Order Date])},today())) , TODAY()) then "Previous Period" //make this a 1 else "Filter" //make this a 2 END //[Order date] = date //[x] = parameter For supplemental reading, check out the period-over-period in Tableau.
With this new code implemented, you will only need one parameter to begin seeing period-over-period data.
Build a parameter.
call it x
data type integer
current value 1
OKAY
Drag and drop your new period-over-period calculation on the visualization. Exclude Filter.
After you exclude the filter, you’re left with two distinct periods, which is now set by your parameter.
Show parameter control by right-clicking the parameter.
Show the parameter on the sheet.
The parameter is input into your period-over-period algorithm, which takes the number of parameters inputted as months.
When you add more numbers to the input, you’re increasing the number of months. Because we don’t love static solutions, let’s optimize the date bucket solution, too!
Let’s dive back into the period-over-period calculation and make it better for end users. Start with generating another parameter!
Show the parameter and open your calculation.
Update “month” with your new parameter d, the date part.
Notice we change the notes on the bottom of the calculation; leaving comments at the bottom or top is a great way to tell others what you did.
We can simplify our life by removing the “logical-looking text values” and cutting them back to only an integer! Rename your calc to a single letter to facilitate your future development.
At this point, your calculation is relatively efficient and utilizes many parameters. When you save calc or hit OKAY, the settings on the filter will change; check out the following popup.
We expect an error because the previous filter on the “p o p” calculation or period over period calc – was only filtering on a condition. This condition is no longer possible.
The previous condition excluded the “Filter bucket” from our period-over-period solution.
Filter 2.
Filter 2! We are now showing 0, as current and 1 as previous.
This will save a lot of time when generating calculations like;
(if p=0 then (profit) else 0 end) – (if p=1 then (profit) else 0 end)
VS calculations like…
(if [p o p]=”Current period” then [profit] else 0 end) – (if [p o p]=”Previous period” then [profit] else 0 end)
And understanding how slow Tableau works with “strings” is also key to making this calculation fast.
Rename your sheet, call it “KPI-1” and begin discussing how to use this pop calculation to show dynamic coloring.
Finding the difference or change without Table Calculations
Table calculations are powerful for ad-hoc analytics but often can be outsmarted with basic logic or SQL. Outsmarting the need to use Table calculations means your workbook will be faster. Everything below shows you how to generate the difference or change variation for color variations, including an in-depth look at how to develop the solution yourself.
Having two sets of numbers and two sets of dates generates a difference or change in the data. A trend because we have an archive of data; archive data is like saying “past data or historical.”
Whether you’re hunting for a percentage difference or a subtraction difference, finding these are relatively easy with dates.
Next steps:
Write two quick calculations.
These calculations offer two simple drag-and-drop values that represent X amount of days!
Now, we can quickly see how these will start to be utilized.
0 = current period
1= previous period
If the current period is greater than the previous period, is that a negative or a positive? Logically, we use an algorithm to show a HOT or COLD coloring, aka GOOD or BAD.
Sometimes, measured values are not all black and white.
It may seem obvious that more money is a positive or GREEN, but realistically, an uptick or downtick may be green or red.
It’s always best to ask what end users first.
For the sake of this demo/blog, let’s begin using the concept that the current profit is higher than the previous period, which is green. We should celebrate our successes and use green to share this win across our dashboard, but without generating a Table calculation, we need to create a difference between the two values.
Check your work!
using the dashboard to verify calculations work.
In the final steps, make a calculation for sum([0])-sum([1]).
We need to understand what value is positive or negative.
Build another min(1) chart!
Use the parameters and your new difference calculation to build the KPI values!
Bonus points if you generate this KPI-3!showing an increase of 9,679…
We will simulate a user experience by dragging and dropping [Container] to see shipping container options onto the visualization.
Notice in our chart, we don’t have any colors showing Green or Red. However, we have logic.
If the Profit value is less than zero dollars, the value is negative. If it’s greater than zero dollars, the value is positive.
Let’s rename our calculation to save us the pain in future calculations. Rename it to “C” for change.
Now, if C>0, then positive else negative! We don’t want to pretend writing an if statement here is wise; study about Boolean calculations as alternatives.
Generate one final calculation.
You can add this to your visualization and quickly visualize the difference between periods.
Add GreenRed to your Color Marks card
you’ll see it populate your visualization
change the colors to green & red
Maybe you’re familiar with this kind of color, well here’s why I enjoy this kind of KPI charting! With the calculations we have, and logic developed, we can build an advanced color chart within 1 single KPI mark card, without having to do a dual axis.
Using text editor for conditional coloring using simple logic
Using a text editor, you are given a lot of flexibility in a user-friendly space. Trying to explain the dual axis to give an up/down arrow can be more confusing and complex to support for new users.
Ⲡup arrow
âź down arrow
By generating two more calculations, you can offer two different things to color in your text editor.
Remove the “containers” dimension and drag it to the filters. Convert to a single value list, aka radio buttons.
omg green
Radio buttons help us simulate a typical KPI experience. Our end-user has a couple of filters, and charts are likely below; filtering this way in the visualization gives us the ability to debug immediately.
Swap to Jumbo Box. Notice the negative background. What if our end user says, “We don’t want a red background.” Or maybe the end user wants 3 conditional coloring variables, and Tableau can only do a dual axis to show two.
omg red
Remove the color GreenRed from the visualization. Drag our two new positive and negative calculations with the text arrows. Put those on the Text mark card.
notice the positive arrow is missing…
Where is the positive arrow?
The positive arrow missing can be a complex few steps to figure out, you need to find each arrow, color it, and the end user will see green or red.
That’s why I rather enjoy this method. Also, using the container filter gives us the ability to see both positive and negative.
This process avoids needing to figure out where the green arrow is located. For what it’s worth if you’re not familiar with this step, I encourage you to try and build the arrows without these steps.
awesome
If you’re experienced, you may enjoy the fact that you’re not having to edit the color marks card, and you can rest assured your workbook will not break as easily as before.
Re-arrange everything according to your “design requirements.”
lovely
Okay, now you’re on the way to understanding how you can
effectively design advanced KPI charts
without developing slow tableau workbooks
and without table calculations
Below are a few final remarks about making your min(1) KPI charts easy to maintain in the future. Not advanced, but rather more for easy support.
outstanding
Ending notes about the min1 solution
Eventually, min(1) has a max, and it becomes harder to support; there are a lot of calculations to maintain, and you have to learn tricks to make it work better. Once you get there, copy-pasting things and duplicating things as needed becomes a priority, and edits become easier as you scale, but it is also a lot to maintain. Consider the amount of work/effort before committing to big asks. There’s always something easier; keep updated on Tableau Community too.
Tips about min(1) KPI for new users
I try to explain to new Tableau users in Training during a Tableau consulting engagement, “Don’t let this become the hammer” Often, Tableau has easier ways to solve the problem, but when things get custom, I always use this solution here.”
Eventually, using the same hammer over and over, it feels like, “This shoe doesn’t fit, and dashboards are slow.” Remember, at times, it’s okay to swap to a visualization, and that’s better because it’s less work for you and Tableau (most of the time).
lots of min(1) which is what though?
Pro tips about Min(1) kpis
Above in the screenshot, notice the layering of “more fields”… does it ever look like this when you’re developing your KPIs/dashboards? This happens, and here’s a fix.
Let’s chat about what’s happening in the Tableau world.
And, before we begin, let’s swap the axis.
min(1)
When we double click on columns or rows, and type min(1), we can see we are typing a calculation, that calculation is a SHEET level only calculation. It doesn’t live anywhere else in the workbook, but it does live in the workbook; from an optimization perspective, don’t bother stressing yourself out or testing what’s best; just consider what’s easiest to support and build faster, and harden later.
Type //something on your min(1) calc…
You type //TheNameYouWantToSee (hold shift, then hit enter), it drops you to a new line, and then you can type min(1)… Or you can open your typed calcs by double clicking, and start from the front of the calc.
End goal, we want our field to show up right ‘meow’ (now).
making the new line is all it takes
By adding the //comment, and a new line, you can leave a in a comment… a note to help you dig through your min(1)’s.
A nice trick to avoid “more fields” becoming Min(1) over and over.
Never fun to support min(1) over and over in heavy KPI charts!
Min 1 is tricky, but not a trick!
When I first learned this, it was called a “trick.” But the more I use it, the more I realize it’s a native feature and should be utilized because it helps make very complicated visualizations easily, unlike the <10 MB Google Sheet Error, which inevitably leads to much engineering.
We use this solution often because it helps avoid a need to edit colors in the colors marks editing screens, which can be difficult and usually generates the need to force the data to show specific values before you can tell Tableau when to change the color of an object. Eventually, meeting people become excited about improving KPI visualizations, which is challenging to do with marks alone. Doing it with text has become a fast approach to generating complex KPIs with a simple min(1).
Tableau Dashboard development and end user usage dictates meta data creation or lack thereof. We have a template for you. It helps you formulate a large amount of navigation from a single landing page. A large journey that increases views per visit. This is helpful because it shows how actions like looking at data or increasing the size of charts, will show you the journey people are taking without interviewing them. Pretend if you have thousands of users, and wanted to know if they think one chart or another is more important. This dashboard will teach you the value of adding PowerBI style expand buttons inside of your dashboards and much more.
How you optimize Tableau desktop has a lot to do with how much meta data you can collect
If you know where people travel on your dashboard, you can add features here, make updates, delete content, upgrade content, and whatever you can think about.
Over fixating on creating dashboards that answer everything means you don’t see who views these extra views, it also becomes a slow dashboard compared to a dashboard with less, and often Tableau data products suffer because lack of understanding of what Tableau server (your infrastructure) or Tableau online (sales force infrastructure) tracks about your end users and doesn’t track.
When you’re in a Tableau Training, they don’t mention the importance of “meta data creation,” that’s an advance Tableau Consulting topic, rather they show you features that consolidate user experiences. These features often generate more work and lower the amount of traffic you could be building down an A or B path.
If product managers or directors overseeing the build of a Tableau data product have little to no experience seeing a Tableau dashboard in Production or post development, your data product may be suffering. Similar to Tableau developers with no experience building websites for clients and increasing the conversion from one page to the next are not going to be as good as a web developer who understands they want to track the usage of these dashboards so that they can understand the conversion.
The end game of a Tableau data product completed means your team now has access to meta data for further discovery.
A Tableau Meta data usage strategy..
A piece of your data governance strategy is to discuss how the product usage data will be used.
If your team has no experience using Tableau beyond building charts and fun dashboards, there’s a good chance you will not know about what data is being tracked from an end user perspective because they have never been in a project where this is relevant.
Knowing about meta data captured gives you bonus points when building dashboards in Tableau. When creating data products for large audiences you will want to be sure to capture as much data as humanely possible.
Conclusively…
If you don’t know what data is tracked, you don’t know what data is not tracked.
Not understanding this information or fundamentals of Tableau will limit your dashboards optimization possibilities.
Opportunities to Optimize the User Experience
There are always numerous opportunities to optimize the user experience and many navigations will show you this quickly in Tableau. These optimizations can significantly impact user satisfaction, engagement, and overall success. Here are some key areas to consider when seeking to optimize the user experience:
a) User-Centered Design: Begin with a deep understanding of your target audience, their needs, and preferences. User research, surveys, and usability testing can help in this regard. By placing the user at the center of the design process, you can tailor the experience to meet their expectations.
b) Responsive Design: With the proliferation of devices and screen sizes, ensuring a seamless experience across various platforms is crucial. Responsive design techniques can adapt the user interface to different screen sizes, making content and functionality accessible to a broader audience.
c) Performance Optimization: Users expect speedy and responsive applications. Minimizing load times, reducing latency, and optimizing code can significantly enhance the user experience. This includes implementing techniques like content delivery networks (CDNs), browser caching, and code minification.
d) Personalization: Tailoring the user experience based on individual preferences and behaviors can lead to higher user satisfaction. Implementing personalization features, such as recommendations, customized content, and user-specific settings, can make the user feel more valued and engaged.
e) Accessibility: Accessibility improvements are essential to ensure that all users, including those with disabilities, can access and use your product. Implementing features like alternative text for images, keyboard navigation, and screen reader compatibility is essential for creating an inclusive user experience.
Improving the Most Used User Experience
When improving the most used user experience within a product or service, it’s important to focus on areas that have the greatest impact on your users. Here’s a step-by-step approach:
a) Identify Key Use Cases: Start by analyzing data and user feedback to pinpoint the most frequently used features or aspects of your product. These are the areas where you should concentrate your efforts.
b) User Feedback: Solicit feedback from users who are regular users of these key features. Understand their pain points, challenges, and suggestions for improvement.
c) Streamline Workflows: Simplify and optimize the workflows related to the most used features. Reduce unnecessary steps, automate repetitive tasks, and make the process more intuitive.
d) Performance Enhancements: Ensure that these features are as fast and responsive as possible. Users will appreciate a speedy experience, especially when using core functionalities.
e) User Interface Design: Evaluate the user interface and design to make it more user-friendly. Implement user-centric design principles and consider A/B testing to find the most effective design changes.
f) Testing and Iteration: Continuously test the improvements with real users and gather feedback to make iterative adjustments. A data-driven approach is essential for ongoing refinement.
The Opportunity to Architect a Product That Works Without Manual Intervention
Creating a product that operates without manual intervention is a goal often associated with automation, AI, and smart systems. Here are some key considerations for achieving this:
a) Data-Driven Decision Making: Implement data analytics and machine learning algorithms to enable the product to analyze data and make decisions autonomously. For example, a predictive maintenance system in manufacturing can use sensor data to schedule repairs before equipment fails.
b) Intelligent Automation: Incorporate automation into routine tasks and processes. This can range from chatbots handling customer support inquiries to software that automatically categorizes and sorts incoming emails.
c) Scalable Infrastructure: Ensure that the underlying infrastructure can scale automatically to handle increased demand. This may involve cloud-based services and elastic computing resources.
d) Self-Healing Systems: Build in mechanisms for the product to detect and resolve issues on its own. For instance, a web application can automatically restart failed services or redirect traffic in the event of a server failure.
e) Security and Compliance: Develop robust security and compliance measures to protect the product and its data, especially when it operates without manual intervention. This may include continuous monitoring, intrusion detection, and data encryption.
f) Monitoring and Reporting: Implement comprehensive monitoring and reporting tools that allow you to track the product’s performance and intervene if necessary. This ensures that, even in an automated system, there’s oversight and control.
By architecting a product that can operate autonomously, you can reduce the need for manual intervention, increase efficiency, and provide a more seamless experience for users while maintaining control over critical aspects of the system.
Not knowing what meta data is being tracked about end users will lead to a good data product suffering unnecessarily.
Once you understand what Tableau is tracking and not tracking your dashboards will start to look very different. We have found that clients will stop consolidating dashboards once they understand that consolidation isn’t always the best use case for every visualization or dashboard.
Is your Tableau data product suffering?
We found teams will face these six problems while creating Tableau data products.
Complex Dashboard Design: The Tableau dashboards are overly complex and not designed intuitively. This complexity can make it difficult for users to comprehend the information presented. Improving dashboard design and data visualization techniques can enhance user understanding.
Limited User Insights: While you can track whether users are viewing a dashboard or not through Tableau Server metadata, it doesn’t provide deeper insights into what users find valuable or the specific aspects of the data they care about. To address this, you need to implement more comprehensive analytics and gather user feedback to understand their preferences.
Data Discovery Ambiguity: Users appreciate data discovery within the dashboards, but you lack a clear understanding of their preferences. Implementing user behavior tracking, click-through analysis, and surveys can help you identify what content and features are most relevant to your audience.
Assumption of User Satisfaction: Assuming user satisfaction without direct feedback is risky. Instead, proactively seek user input through surveys or user interviews to gauge their opinions and preferences. This will help you refine your data product to better match user needs and expectations.
Limited User Interaction: Users might only engage when something is wrong with the data product. To promote more regular interaction, consider implementing features that encourage user engagement, such as notifications, personalized content, and features like data alerts to notify users of relevant changes.
Long Remediation Time: Dealing with issues and bug fixes can be time-consuming. To address this, consider implementing better monitoring and alerting systems to detect issues early and resolve them promptly. Investing in continuous integration and deployment (CI/CD) can also help in reducing downtime and improving the agility of your data product maintenance.
In summary, addressing these challenges requires a multifaceted approach that involves redesigning dashboards for better comprehension and this can be accessed by connecting to more end user meta data. Actively seeking user feedback, and implementing user tracking and analytics. Additionally, focusing on improving the support and remediation processes can help ensure that issues are resolved in a timely manner, enhancing the overall user experience with your Tableau data products.
If everything is consolidated, how do you improve?
One quick way to generating a healthy data product is considering the user data. Once you get into that meta data it will teach you more about what to build in the future.
If your team consolidated everything into one view, how does your team know what’s being used most? What if ten of the options on the dashboard are never used but generate a lot of work for the team to support?
Interviewing end users and your Tableau developers is important to creating great data products, however what if there’s hundreds of thousands of end users, and ten thousand of them are non-developers? Using Tableau meta data is important and views on each dashboard offers instant access into what’s being used most.
User experience tip; By tracking what user “learn more” you can study their user journey. Learning more or “navigation drilling” offers a window into understanding what’s important to your end users.
A navigation button in Tableau and moving the end users from one dashboard to another will teach you what users care about.
Use navigation buttons to learn more about measure values, dimensions, and ultimately discover the questions they are asking from the data.
Then it’s up to your team to capitalize on that knowledge to make that data or knowledge easier to access.
navigation object in tableau dashboard
Features in Tableau Dashboards used Incorrectly.
If Tableau dashboarding features are used incorrectly, everything will be more difficult to support or easier. The future of support will depend how you use the features and your ability to build content that is easy to reuse; copy and paste.
Over using features in Tableau is a negative and there’s a large hurdle to jump each time you need to edit or make a simple fix.
Now, the simple fix, is not a simple fix. The simple fix requires everyone to fix the feature you implemented everywhere..
Understand the user experience.
Take this website for example, how do we know people are making it to the bottom of the page without a feature that allows them to jump to another page and then tracking that page?
In web tech things are different, in Tableau server you’re fixed to their platform tracking system.
Unless you embed the Tableau environment, however this will start to get technical very fast.
If you’re trying to implement technology that isn’t native, like implementing heatmaps, then we would know if users make it to the bottom of the page. However this feature slows down the speed of the entire website and may not fit for your Tableau Server environment.
Report consolidation and migration.
We know there’s a lot of demand for consolidating reporting and migrating to different SaaS products. We found once that ‘phase’ of business intelligence ends, companies start to see the reason consolidation is a negative or positive.
Consolidating dashboards using features is a big selling point because Tableau trainers choose to show these features in training and a lot of Tableau developers choose to build dashboards with features to help consolidated, however this is not a good usage of developer time if you’re unable to uncover meta data from the usage of the data products.
Consolidating user experiences in Tableau when building a data product limits your ability to understand what a user is using the most and forces interactions with end users beyond your data product.
This dashboard can work for profit, sales, and everything that can aggregate can be a fun dashboard to build, and insightful for many, however what if people are only using the profit pie chart of the dashboard? Building a user experience to uncover these questions from the end user will help you improve your tableau product development beyond report consolidation and migration phases.
A solution, using Colibri Tableau Dashboards
Our founder Tyler crafted Colibri after 10 years of building Tableau data products. Tyler, founder of dev3lop, worked at Tableau Software as a Full time employee before leaving to create Dev3lop | Consulting Services.
In Tyler’s words;
I added what I feel is an important aspect to generating meta data that will help you understand what is important to your end users who use the product, and it will tell you what data structures are being accessed the most. Drilling into a chart, better screenshots of the chart, and drilling into the data of a chart is the KEY of the entire solution. This amount of navigation gives you a huge foundation of clicks completed. Colibri can be your “template” and it doesn’t need to be tailored to Google analytics.
Today, we would like to highlight the functionality of Date Buckets, which is how we like to think of it mentally, and others call it Period-over-Period Analysis within Tableau Desktop. Both periods are buckets of dates and work great with min(1) kpi dashboards and often used in our Tableau Consulting engagements.
This blog delves into a method for date calculations to be used as trailing periods of time, to gain access to quick change between two periods in Tableau. In other words; We are focusing on identifying the last two periods in your data source, and the end user supplies a value to increase those buckets based on a date part you pick.
This approach enhances the efficiency and clarity of your analytical processes with Tableau and is easy to re-use. There are many ways to write this calculation and this is one way to write the calculation.
between dates filter
In Tableau this between date filter will create two calendar inputs, most executives don’t want to click anything.
It only takes 3 steps to build self generating, automated (not static set filters), date buckets in tableau desktop that trail with your max date in the date column [w].
lol, type this stuff or paste the code coming from this tutorial.
Below please find my quick win tutorial as a means of quickly winning⌠on any Tableau workbook with a date and a parameter.
We will be using the SuperStore Subset of data.
Which comes with every license of Tableau Desktop. In your data, you probably have a date. Use that date and follow along with these next two steps.
To begin, you need a date, and a parameter.
Step 1, make a date variable named W.
Create a new calculated field in tableau desktop, call it W.
make a simple variable W in place of your date. your date goes in this calculated field.
Now make the parameter.
Step 2, make a parameter variable named X. Itâs an integer.
This will be the number of âXâ per period of analysis.
make a simple variable X in place of your parameter.
Paste the calculation below in any workbook with a Date and Parameter.
Above, if you followed along, you will not need to make any major changes to the calculation.
if DATETRUNC('month', [W])> DATEADD('month', -([X]+ datediff('month',{MAX([W])},today())) , TODAY()) then "Current Period" //make this 0 elseif DATETRUNC('month', [W])> DATEADD('month', -([X]*2+ datediff('month',{MAX([W])},today())) , TODAY()) then "Previous Period" //make this a 1 else "Filter" //make this a 2 END //[W] = date //[X] = parameter
Drag drop this on to the view, right click filter, filter filterâŚ
Now, only two buckets of time are available. Youâre welcome!
Automated period over period analysis in Tableau
You’ve just implemented automated date buckets in Tableau, allowing end-users to control visualizations using the bucket generator. Personally, I find the tool most effective when using it in a daily context rather than a monthly one. However, the monthly option provides a convenient way to encapsulate dates within distinct periods, while the daily granularity offers a simpler and more immediate view.
Having a rapid date divider or bucket automation at your disposal is highly advantageous. It empowers you to visually highlight disparities between two date periods or employ the calculations for logical flagging, subtracting values, and determining differences, all without relying on the software to construct these operations through window calculations.
Optimization date buckets or period over period in Tableau
Optimization #1: remove LOD calculations
Nothing against LOD calcs, except they are slow and built to help users who donât know SQL.
{max(W)} seeks to find the max date, you can find it easier using a subquery in your select statement. If you donât know what that means, ask your data architect supporting your environment to add the max(date) as a column, and have it be repeated per row too. They will know what to do or you need a new data architect.
Optimization #2: stop using % difference or difference table calculations
Nothing against table calculations, except they are slow and built to help users who donât know SQL.
Optimization #3: change strings to integers.
Nothing against strings, except they are slow.
Itâs likely not your fault that youâre using strings in 2018 with if statements, itâs probably because someone taught you who also did not know how to write optimized Tableau calculations.
Optimization #4: âmonthâ date part⌠add a swapper.
The Datetrunc is used to round the dates to the nearest relative date part, thatâs just how I explain it easily.
Date part can be a parameter.
DATEPART(date_part, date, [start_of_week])
NO I Donât mean the Function Datepart.
DATETRUNC(date_part, date, [start_of_week])
YES I Mean Date_part, which is scattered in the calculation and easy enough to replace with a parameter full of date_parts. Now end user can play a bit more.
Optimization #5: remove max(date), add an end date parameterâŚ
Remove {max(date)} or the subquery of max(date) explained above because you can give your end user the opportunity to change the end date using parameter.
