Automated Discovery in the Data Warehouse
One of the world’s largest insurers was managing a data warehouse where prescriber data was imported from 25 different data sources across the country. Throughout this process, administrators and Subject Matter Experts managed the integration of this new data manually.
The data had to be profiled by running a number of queries then investigated by SMEs to ensure the data was accurate, without anomalies or extreme outliers. After merging these data sets, SMEs would then have to check the final data warehouse to ensure there were no duplicate or erroneous records following the data join. This work required many people and lots of computing resources, costing both time and money. Knowing that their data would continue to grow prolifically, the insurer needed a new approach. They found their solution in EmcienScan.
Using EmcienScan, the insurer quickly extracted an extensive profile of new data before investing any manual efforts. Instead of SMEs manually searching through each source for outliers, EmcienScan alerts them when outlying values appear in the data stream, and gives insight as to why they are there. This is critical to discovering incorrectly coded data and fraudulent activity. An initial scan of each data source revealed the high-value variables within each table, which has allowed the team to reduce the time of their joins and get data to analysts faster. What once took days of human effort and extensive computing resources could now be conducted in a matter of minutes.
Not stopping there, the team used Emcien’s APIs to scan the incoming data streams automatically. This API-driven workflow transformed the slow, manual process into an automated data factory. Incoming data is immediately profiled and compared to previous scans, only triggering an alert when human intervention was required. The results of this automated workflow were nothing short of transformational. Analysts and SMEs who were spending sixty percent of their time on prep and data management quickly went from cost centers to value creators, eliminating costly errors and addressing questionable claims.
Reduce operations costs
Accelerate data joins
Automatically identify data integrity issues
Monitor changes in prescriber activity
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