Step No. 4: Embed the Model into the Production Application
Step No. 4: Embed the model into the production application
Now that they have a model that appears to be working, it's time to integrate the application of the model with their production software. The operations team uses the same tool from the cleansing step to execute the cleansing and model application functions in a single workflow embedded within their application. The tool is able to ingest large amounts of data and apply the model quickly and efficiently.
Step No. 5: Update and refresh
Over time, the billing team provides feedback to the operations team. They'd like a prioritized list of cases to investigate based on a scoring system. They'd also like more historical information to determine how long the fraud has been occurring. The operations team once again leverages the powerful set of tools that can take them through data discovery to production deployment.
The motivators for embedding analytics are rapidly becoming clear in the industry. Streamlining business processes, enabling operational decisions to be made quickly after receiving critical information, and substantially improving business performance are key reasons for adopting an embedded analytics approach. This has never been easier to do than it is today using the tools now available.
Adopting a practical, best-practices approach of understanding the data, cleansing the data, iteratively developing a working model, embedding the model into the production application, and updating and refreshing information back to operations teams will help drive success through the benefits of analytics.
Jim Falgout has 20+ years of large-scale software development experience and is active in the Java development community. As Chief Technologist for Pervasive DataRush, Jim's responsible for setting innovative design principles that guide Pervasive's engineering teams as they develop new releases and products for partners and customers.
Prior to Pervasive, Jim held senior positions with NexQL, Voyence Net Perceptions/KD1 Convex Computer, Sequel Systems and E-Systems. An officer with Toastmasters, Jim is an experienced public speaker. He has delivered presentations to user groups, client conferences and IEEE working groups. He is a popular analyst and go-to thought leader. Jim has a Bachelor's degree (cum laude) in Computer Science from Nicholls State University. He can be reached at firstname.lastname@example.org.