Customers have—and always will—want to have accurate and timely information in their business intelligence tools. There are many reasons reports and metrics may not be accurate: the wrong data was used, there are duplicate metrics with slightly different results, or the underlying query was incorrect. On the other hand, providing timely data is conceptually simple: just provide direct access to the production database. However, that is often not practical (or even allowed) for technical or security reasons. Solving this typically includes some process such as streaming or batching data to a warehouse and running the appropriate ELT jobs. Unfortunately, this introduces complexity which can make the data untrustworthy.
Let’s see how the Stemma Data Catalog works with Tableau, the world’s leading analytics platform to address three common problems people face when creating a new dashboard or metric.
Does this metric already exist?
Metrics are often defined in two ways: computed by a BI tool, or as an existing value already in the warehouse. To know whether a metric already exists, you’ll need to search in both places.
The challenge is that you need to search across multiple tools. Even worse, you may not have access to all of those tools, so you may actually need to request access to an existing metric instead of creating a new one. The volume of similar information to sift through can be a task in and of itself; consider that even the most well-kept data warehouse often has hundreds (or even thousands) of tables that all seem to be almost identical. You may even have that one data scientist in your marketing department that has a shell script which creates a new table every morning with their name in it (I’m looking at you mkt.matt_cac_2021_11_04!).
Stemma indexes Tableau dashboards along with their underlying tables, providing a one-stop search for all data assets. Even better, Stemma tracks how many times a dashboard has been viewed, instantly highlighting which is most commonly used.
Is the right data being used?
What junior data analyst hasn’t been in an executive review meeting only to have their VP (who used to have their job 8 years ago) tell them that the table they used to build the report is the wrong one? Building high quality, trustworthy dashboards starts with making sure that you’re using the right tables and columns in the first place..
Stemma automatically correlates your Tableau dashboards to the corresponding table asset by reconciling the table names extracted from the Tableau Metadata API with the existing table metadata Stemma has captured from your warehouse. This allows you to start your exploration by finding trustworthy reports and, in turn, using the lineage to discover which tables are useful for you.
You can also explore the table’s lineage upstream or downstream to get an end-to-end view of where the data came from.
How can I use this metric?
Let’s look at a single metric as an example: cost of acquiring a customer, or CAC. A company may have a high level dashboard that includes one rolled-up value for CAC but a data analyst might want to look at this metric a different way: by channel, or date range, or both. Furthermore, the analyst may want to explore this metric using SQL so that they can create a derived table with the output.
Tableau provides Stemma with the SQL that it creates to generate each sheet and, in turn, Stemma indexes this SQL as well to allow users visibility to the code. Users can copy and paste the code into their IDE to jump-start the SQL exploration.
To learn more about Stemma and Tableau contact Stemma at info@stemma.ai.
