Data is used everywhere in world, but have you ever actually seen what data looks like? Excel sheets, tables, schemas, nulls, security, and so much more. When I first joined Great Expectations (GX) I had little to no experience with the word data. (Aside from the tradition meaning of the word.)
GX allows for users to inject data onto the GX Cloud app and apply rules ("Expectations") to ensure that the data is as they expected. The need for fast and easy application of many tests across all columns was the top pain point.
Product Designer, UX Researcher & Designer, Visual Designer
Great Expectations users that enjoyed the flexibility of GX generated "Data Docs". Users found that the results GX were providing based on their defined tests were extremely helpful but lacked ease of use, readability, and ability to display the results to the shareholders in reporting.
Our Go-to-Market team was busy interviewing multiple companies and real users to; show off GX capabilities, understand user needs & pain points, and moments of delight while presenting GX Cloud.
A common theme users expressed interest in was a data health dashboard to be provided, users expressed a desired to have better visual representation of their data to help better understand the data, and offer better reporting.
We identified 7 groups of data points that users were most concern about: Volume / Schema / Completeness / Uniqueness / Numeric / Validity and Custom SQL statements, that users expressed that would be the most beneficial and applicable to their data.
Our goal was to provide a dashboard view into their uploaded data and have a easily understandable view. GX Cloud needed to stand out from GX OSS offering, users that were familiar with GX had already built Expectations and were running them on regular schedule to ensure their organizations data was as expected. Users that were not familiar need even more to select GX Cloud as their primary data quality tool.
We need to first understand where in the user flow would be the best and ideal place to populate a visual dashboard. Using Miro we reviewed our current user journey and came to the conclusion the most ideal place was after user had connected to a datasource. GX Cloud would then quickly process the data and users would see a data dashboard to be able to get a snapshot view of their data in its current state.
We were able to quickly put together as high fidelity prototype to interview with our cohorts of users to understand if needs / pain points were addressed.
The Product team reviewed this in detail and agreed that the placement of the dashboard felt addressed users needs and we moved into working on a high fidelity design to present back to the companies and users we previously talked to see if it met the following needs:
Our Product team reviewed the set of designs with customers and presented the prototype to 5 companies to see if the data dashboard had addressed their needs. Since we were returning to the users we had spoke with previously we did not create a new scenario for users.
After successful connection to users data:
∙ Does the view give you insight in the current dataset?
∙ What are the next steps you would take?
∙ Does the suggested Expectations meet your needs?
Overall the feedback from users were generally positive. Visual aspect was helpful, but new pain points and needs were surfaced. Users that were familiar with GX OSS were able to access a feature called profiling. Users were able to apply a larger amount of Expectations to the whole table, but the issue was that it provided too many various base Expectations and created a large amount of clutter and unneccessary Expectations.
We learned a lot from the feedback users had given us. Overall we determined that this work did not address to most urgent needs. This was ultimately shelved for future use.
We then pivoted to allowing users to leverage AI analysis of users data. This was a very sensitive subject to users since we did not want to expose users data, we opted for a more broader approach. We worked on allowing users to simply select an option when connecting their data to apply general Expectations such as a Schema based test to show users that the general structure of the data was as expected. Users expressed a more urgent need to apply a larger number of tests to the data that they want to test.
My biggest takeaway working on this project was to advocate for allowing users to have access to a feature that we were already aware of from our OSS offering. The data can be unclear but it is up to us to analyze and push for working on a feature that users do not speak of since they are already using on GX OSS but it was just not offered on GX Cloud.
