Being able to capture different business metrics and organize them in different catalogs is most valuable. We can organize these metrics into sales-related metrics, customer-related metrics, supply chain-related metrics, etc. Data catalog and data literacy are really the great capabilities of this solution that we leverage. A data catalog is coupled with the business glossary, which enables data literacy. In the business glossary, we can maintain definitions of different business terms and metrics, and then the data catalog can be searched with them. Data lineage is very important to us. It is related to the origin of the data. For example, if a particular metric gets calculated from certain data, how did this data originate? From which source system or table did this data originate? After we have the data, lineage is populated, and some advanced users, such as data scientists, can use this data lineage to get to the details. Sometimes, they are interested in kind of more raw data so that they can get details of the raw table from which these metrics are getting calculated. They can use that raw data for their machine learning or AI use cases. I used a couple of different Smart Data Connectors, and they were great. One of the Smart Data Connectors that we used was for our Microstrategy BI solution, so it was a Microstrategy Smart Data Connector. Microstrategy is our enterprise reporting tool, and a lot of the metrics were already built in different reports in Microstrategy. So, it made sense to use this connector to extract all the metrics that were already being used. By using this connector, we could connect to Microstrategy, pull all the metrics and reports from that, and then populate our business glossary with those details. This was a big advantage of using the Microstrategy Smart Data Connector. Another Smart Data Connector that we used was the Python Connector. It enabled us to build the data lineage. We already have a lot of ETL kind of processes built by using Python and SQL, and this connector can reverse engineer that and graphically show how the data flows from the source. This work was done by our data engineers or IT teams, but the business teams didn't understand how it is built. So, by giving them a visual representation of that, they became more data literate. They understood how the data flows from different tables and ultimately lands in the final tables. It can be stood up quickly with minimal professional services. Its installation and configuration are not that complicated, and we can easily and quickly stand it up. We wanted to get faster time to value. So, we did a small professional services engagement to come up to speed in terms of how to use the product. Its installation and configuration were pretty quick, but afterward, for configuring it, we wanted to make sure that we have the right processes established within the tool.
