Data analytics takes many forms in today’s organizations from front-end visualization tools to apps to enterprise business-intelligence tools to pervasive, behind-the-scenes algorithms than drive applications.
The ideal is, of course, to be able to bake in or embed analytical processing and outcomes into day-to-day operations. For insurers, this means the ability to better engage customers throughout their lifecycles, as well as offer new types of services. Fraud detection, of course, is a must, and analytics can do much of the heavy lifting in this area as well.
Many insurance organizations, of course, have extensive legacy-based infrastructures, with many critical applications written in COBOL many years ago and continually updated and maintained. It’s often costly and difficult to redesign these systems to support real-time or near-real-time analytics, as demanded by today’s environments.
Many organizations are looking at redesigning their systems or building new capabilities into their enterprise architecture to better support analytics. But it may work better to develop analytical functions as separate sideline, to be merged into the broader organization when tested and ready. That’s the view of Mark Torr, senior director at SAS, who is an advocate of what is called the “Lambda architecture,” which is a faster way for an enterprise to support both streaming, always-on data, along with the more traditional batch loads.
Torr calls this process marrying the “factory” with the “lab.” Along with efforts to modernize the traditional enterprise architecture that may be present, add a second platform (the lab) to support analytics-driven innovation, Torr advises. “This second platform, often provisioned as something like an innovation lab, provides an agile environment to develop a dynamic, frequently changing platform free of the constraints of the more slow moving enterprise platform.”
The lab platform enables new ideas to be tested or incubated for later release into the broader organization. The alternative, Torr continues, is to keep up with across-the-board modernization efforts, to rebuild or launch new systems that incorporate data analytics. This will deliver, but will proceed only fast as modernization can take place.
This blog entry has been republished with permission.
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