From Big Data to Big Value: Is Your Data Working Hard Enough to Generate Business Value?

Insurers have always been custodians of big volumes of data and have access to excellent analytical and actuarial talents. The time is right to think about monetizing your data to create Big Value.

Insurers have always relied on data to understand customer needs and underwrite risks. And they have been very good at collecting this data too. Every year insurers collect huge volumes of data through their online systems, contact centers, agency systems, incoming letters and forms and even from partners such as MVR, CLUE and other similar sources. Some may even say insurers are too good at collecting data. But let us ask a different question – how good are they at using these huge volumes of data? Unfortunately for insurers, the answer to this question is not equally encouraging. My interactions with many leading insurers indicate that the percentage of all available data that insurers are using effectively often ranges below 10 – 15 percent. Just as a warehouse of unsold inventory does not provide a return on investment, a warehouse of unused Big Data cannot deliver value unless insurers develop new business models to put it to use.

There is a silver lining to this dark cloud though, and that is what makes me very excited and optimistic. Many insurers have started to consider data as a strategic asset and are looking at means of harnessing and harvesting true business value for their data. Working with many leading insurers, I see two fundamental shifts that will enable deliver Big Value from data for insurers.

Flipping the Script

Traditionally insurers have surveyed their warehouse of data and asked themselves, “We’ve got all this data, so what can we do with it?” When this question reaches the business team, they typically ask for few more reports to better understand the data or what can be done with the data. And off goes the business intelligence team (or by whatever name the group may be called internally) creating new data marts to get the new reports. From this approach, efforts focus on determining what kind of data (structured and unstructured, internal and external) is available, how it can be retrieved, stored and processed, and what reports and insights can be generated from it. Only at this point does anyone seem to ask, “How can we use these reports and insights to deliver value for the business?” Such a process, though not intentionally, risks putting data infrastructure ahead of business goals, which leads to wasted effort generating reports no one ever really needed (or reads).

We’ve worked with insurers to effectively flip the script, working “right to left” by asking “What could create a competitive advantage for this business?” We did this by bringing in insurance domain specific business use cases to jump start this discussion. From this point, the organization can now ask what insights are needed to deliver that result, what data is required to generate those insights, and, finally, what infrastructure must be in place to support that data.

Thinking “right to left,” insurers will also fuel predictive and prescriptive analytics. Because now you are thinking what business value you want, which goes beyond looking back at historical outcomes (descriptive analytics) to identifying possible business outcomes (predictive analytics) and providing actionable recommendations (prescriptive analytics) to achieve those business values.

Data Monetization Mindset

I have used the word “mindset” here very intentionally. Data monetization is about a shift in the thinking process. It does not really have to lead to insurers selling data tomorrow. But, it is very important that insurers think of data as a business asset. And if data is a business asset, we should be able to make money off it. Whether we make the money by selling the data to another party or we use it to fuel more business growth internally or potentially launch newer innovative business lines is a question that is a natural result of this shift in the thinking. In fact for all practical reasons, the insurer may choose still not to sell their data or insight to another company for foreseeable future.

Thinking “right to left” is a big step in this direction. For insurers who have already started on this journey, the transition to data monetization will be much simpler and natural. It is all about putting the business value you want to create before you start worrying about how you will process the large volume of data. You have to start thinking about how best can you package your data and insights such that it enables your partners’ (internal or external) business models and creates maximum shared value for you and your partners.

Having said that, data monetization will also require some IT preparedness. It will require a very different type of data infrastructure readiness for insurers to be really effective and efficient in this game. As you start considering putting data to hard work, you are pretty much behaving like a clearance house or credit score provider, where accuracy and speed are critical to meet the service levels. You will need the capability to store larger volumes of data, access and analyze them in real time, query and search any information at any time and do all of these at a much faster speed. And if you are doing this more often than ever before, it has to be at a price point that is better than just affordable. Fortunately, this is possible with today’s technology innovations.

(Related: The 3Cs of Insurance Customer Intimacy in the Digital Era)

An initiative with a leading market research firm can serve as an example. The organization has setup its new data platform using Big Data technologies that will enable them to glean more valuable insights from existing and additional external sources of data that will be even more valuable for their buyers of data and insights. And they are doing this at a much faster speed and more importantly at a lower cost than what their traditional data environment enabled.

Insurers have always been custodians of big volumes of data and have access to excellent analytical and actuarial talents. The time is right to think about monetizing your data to create Big Value.

Arunashish Majumdar // Arunashish (Arun) Majumdar, Chief Architect, leads TCS’ Insurance technology practice in North America. He specializes in transformation initiatives that improve customer experience, business agility, operational efficiency, and technical currency. He has handled strategic initiatives to define and deliver enterprise digital strategy, IT portfolio simplification, and core systems modernization for several TCS customers.

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