Enhance SQL Insights: Feature Parity With Trends

Hey everyone! Today, we're diving deep into a feature request that could significantly enhance the way we use PostHog, specifically focusing on SQL Insights. This article will break down the current disparities between UI insights and SQL insights, explore potential solutions, and discuss the benefits of bringing feature parity to these tools. So, let’s get started!

The Current Disparity Between UI and SQL Insights

Currently, there’s a notable difference in the display options available between UI insights and SQL insights in PostHog. This disparity can affect how users analyze data and draw conclusions, potentially leading to inefficiencies and a less-than-optimal user experience.

UI Insights: A Rich Visual Experience

When we talk about UI insights, we’re looking at a feature-rich environment that offers a wide array of display options. These options include the ability to show values on series, display trend lines, and perform statistical analysis directly within the interface. This makes it incredibly easy for users to visualize their data and identify trends at a glance. The graphical nature of UI insights allows for quick interpretations, making it a go-to choice for many when exploring data. Moreover, the interactive elements in UI insights enable users to drill down into specific data points, filtering and segmenting information to gain deeper insights.

For example, you can easily see how a particular metric changes over time, identify peak periods, and even compare different segments of your user base. The ability to overlay trend lines helps in understanding the overall direction of the data, while statistical analysis tools provide a more rigorous examination of the data's properties. All these features contribute to a comprehensive understanding of the data, making UI insights a powerful tool for data exploration and analysis. However, the limitations in SQL insights mean that some of this rich functionality is lost when dealing with more complex queries that require SQL.

SQL Insights: Powerful but Limited

On the other hand, SQL insights, while incredibly powerful for complex queries and data manipulation, lack some of the visual display options available in UI insights. Currently, users don't have the same level of flexibility when it comes to visualizing data derived from SQL queries. This means that while you can extract and transform data using SQL, presenting that data in an easily digestible format can be challenging. The absence of features like showing values on series, trend lines, and statistical analysis within SQL insights means users often have to resort to external tools or manual methods to visualize their data, adding extra steps to the analysis process.

This limitation can be particularly frustrating when dealing with large datasets or complex queries where visual aids can significantly enhance understanding. Imagine running a sophisticated SQL query to identify user behavior patterns, but then having to export the results to a spreadsheet to create a simple trend line. This not only takes more time but also introduces the potential for errors during data transfer and manipulation. The goal is to bridge this gap and bring the visual power of UI insights into the SQL realm, making data analysis more seamless and efficient. This is where the feature request for parity in display options becomes crucial, aiming to provide users with a consistent and powerful analytical experience across both UI and SQL insights.

The Proposed Solution: Feature Parity for SQL Insights

The core of this feature request is to bring feature parity, or at least the most useful display features, into SQL insights. This means equipping SQL insights with the same visualization capabilities that make UI insights so effective. Let's break down the key features that users are asking for:

Show Values on Series

One of the most requested features is the ability to show values on series directly within SQL insights. This feature allows users to see the actual data values plotted on the graph, making it easier to interpret the results of their queries. Instead of having to hover over each data point or refer to a separate table, the values are readily visible, providing immediate context and clarity. This is especially useful when dealing with time-series data, where understanding the magnitude of changes over time is crucial. For instance, if you’re tracking website traffic, showing values on the series allows you to quickly identify peak traffic periods and compare them with off-peak times. This visual representation of the data can highlight patterns and anomalies that might be missed when looking at raw numbers alone.

Moreover, showing values on series enhances the overall readability of the graph. It eliminates the need for constant cross-referencing between the chart and the data table, streamlining the analysis process. This feature is particularly beneficial for users who prefer a more visual approach to data analysis, as it translates complex SQL query results into an intuitive graphical format. By integrating this feature into SQL insights, users can gain a clearer and more immediate understanding of their data, leading to faster and more informed decision-making. It bridges the gap between the power of SQL queries and the accessibility of visual analytics, making SQL insights a more user-friendly and effective tool.

Show Trend Lines

The ability to show trend lines is another critical feature that users are eager to see in SQL insights. Trend lines provide a visual representation of the overall direction of the data, making it easier to identify patterns and predict future trends. This is invaluable for understanding long-term changes and making strategic decisions based on data-driven insights. For example, if you're tracking user engagement metrics, a trend line can quickly show whether engagement is increasing, decreasing, or remaining stable over time. This high-level overview can then inform more detailed investigations into the underlying causes of these trends.

Trend lines also help in smoothing out short-term fluctuations in the data, revealing the underlying direction more clearly. This is particularly useful when dealing with noisy data that might have significant day-to-day variations. By focusing on the trend, you can filter out the noise and get a better sense of the overall pattern. Additionally, trend lines can be used to compare different datasets, allowing you to see how trends in one area relate to trends in another. For example, you might compare trends in website traffic with trends in user registrations to understand how marketing campaigns are affecting user acquisition.

Integrating trend lines into SQL insights would significantly enhance its analytical capabilities. It would allow users to quickly identify and interpret trends without having to export data to external tools or perform manual calculations. This not only saves time but also reduces the potential for errors. By adding this feature, SQL insights can become a more complete and powerful tool for data analysis, empowering users to make better decisions based on clear and visually compelling trend data.

Statistical Analysis

Including statistical analysis tools within SQL insights would be a game-changer for many users. This feature would enable users to perform more in-depth analysis of their data directly within the PostHog interface. Statistical analysis can provide valuable insights into the significance of trends and patterns, helping users understand the statistical properties of their data. Imagine being able to calculate metrics like mean, median, standard deviation, and p-values directly from your SQL query results. This would allow for a more rigorous examination of the data, ensuring that conclusions are based on solid statistical evidence.

For example, if you're running A/B tests, statistical analysis tools can help you determine whether the differences between the test groups are statistically significant or just due to random chance. This is crucial for making informed decisions about which changes to implement. Similarly, if you're analyzing user behavior data, statistical analysis can help you identify segments of users that are significantly different from the average, allowing you to tailor your strategies to specific groups. The ability to perform these analyses within SQL insights eliminates the need to export data to external statistical software, streamlining the workflow and reducing the complexity of data analysis.

Statistical analysis features could also include correlation analysis, regression analysis, and hypothesis testing, providing a comprehensive toolkit for data-driven decision-making. By integrating these capabilities into SQL insights, PostHog can empower users to go beyond simple data visualization and perform sophisticated analyses that uncover deeper insights. This would not only enhance the value of SQL insights but also make it a more versatile and indispensable tool for data analysis.

Alternatives Considered

One alternative that has been considered is to simply build the insight using the UI when possible. While this is a viable option for some use cases, it's not always feasible. SQL insights are often necessary for complex queries that cannot be easily replicated in the UI. For users who need the power and flexibility of SQL, the lack of display options becomes a significant hurdle. This limitation underscores the need for feature parity, ensuring that users can leverage the full potential of SQL insights without sacrificing visual clarity and analytical depth. The goal is to make SQL insights as intuitive and user-friendly as the UI, providing a seamless experience for all types of data analysis tasks.

Additional Context and Debug Information

This feature request originated from a support ticket (https://posthoghelp.zendesk.com/agent/tickets/36028), highlighting a real-world need for improved display options in SQL insights. Debug information, including session details, exceptions, and location within PostHog, further underscores the importance of this enhancement. The project is currently in