MacGyvering, or the ability to quickly pull together solutions to vexing problems in the resourceful way as secret agent MacGyver did in the TV series of the same name, is seen as a good thing. Anyone who has been in the corporate workforce long enough knows that many of the most successful projects have had their share of MacGyvering behind them, held together with duct tape and chewing gum.
In the information technology world, there has been no shortage of such improvisation. As documented in a recent Forbes article, Farmers Insurance, for one, has a very solid team of MacGyvers within its customer relations department. The challenge was the company was relying on multiple data sources for its customer information. Not an uncommon problem, of course. And, as many other companies are forced to do, Farmer’s sales reps had to manually pull data from these sources and, in the words of the company’s CIO, Ron Guerrier, to do a lot of “MacGyvering” to cobble information together on the fly and deliver superior customer service.
In the case of Farmers, the sales reps were really good and pulling together the data – but this often required months of training to get new employees up to speed. Plus, the existing legacy CRM system was expensive to maintain. So while MacGyvering may seem cheap, it actually isn’t. The problem of multiple data sources from multiple systems has been one that the industry has been wrestling with for years. Even back in the 1990s, as Web services and Web-based client-to-host solutions came on the scene, the main pain point they were addressing was the ability to abstract essential functions away from the underlying mainframes and into a more standardized Web application within a cross-enterprise service layer. The data usually stayed where it was first placed, often in the mainframe format. But with each new acquisition or line of business, new data sources are constantly being added.
That battle still continues to this day. Add to that the volumes of social media, geolocation and unstructured data (such as telematics and weblogs), and there is a growing stew of data sources.
Is cloud and SaaS finally alleviating some of the pain with this pain point? The Forbes article, written by Tom Groenfeldt, describes how Farmers Insurance made the transition, employing Salesforce as an automated data-transfer vehicle.
In the Farmer’s implementation, the carrier is standardizing on the Salesforce platform, providing customer service representatives with cross-enterprise customer data, extending across all of its product lines, including home, auto, motorcycle and motor homes. Salesforce will be automatically puling data from various back-end sources to offer a comprehensive screen to customer service reps. The key to much of this will be Salesforce’s “Einstein” platform, which employs machine learning and predictive analytics to provide greater insights on customer information.
Of course, the devil is in the details, and the data silo challenge is likely to keep vexing many insurers for some time to come. But cloud is another big step in the journey toward achieving the much-sought-after 360-degree view of the customer. But insurers will still always need their MacGyvers to keep things running in a pinch, no matter how advanced the technology.
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