Everyone has probably heard the cliché “Data is an Asset.” But how do we break it down pragmatically into an actionable backlog and apply some “data-driven metrics” toward attributing and increasing the value of data.
It’s a fascinating and challenging paradigm – to increase the value of data. Like many other assets, the value is typically not intrinsic; it must be harnessed. It requires a process of extraction, enrichment, polishing, and packaging. Even then, the value realization is not obvious or guaranteed, until the consumer engages with it. Think of diamonds. They go through an extensive process from rock to finished product, and still only possess value once purchased by a consumer at a defined market rate.
To optimize data, we must understand its value creation life cycle.
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Life of Data:
Collection and Acquisition
Acquiring and collecting data has evolved. The age-old issues of generating, storing, and moving data have benefited from technical advances and have matured to the extent where we can now focus on value creation. Naturally, the new-age issues have reared their ugly head. Expenses of data management have only increased, at best, plateaued.
Emphasis on data collection strategy should shift to asking the ‘Why’ upfront. Do we really need to acquire, duplicate, store, and manage all that data, or will it be more efficient to just acquire the essence of that data? Simplistically, if you are eventually going to query the dataset, we must consider running the same query at the source of data.
Assess and Analyze
This is a crucial and another engineering-heavy phase. However, business design and visionary input is equally crucial to the success of this phase. Often, Technology teams are left with having to deal with this issue. A car drives only as well as the driver. So be the driver. Be clear about the problem statement. Engineers and technicians are great at solving problems, and technical advances have allowed them to push the limits on what they can do with data. Be the visionary force of advanced and innovative solutions. Be the dreamer and let the artists (i.e., engineers) paint a better version of the picture.
Publication
Significant effort and negotiations are involved in collecting and analyzing data. Frequently this is done with a vision – to build a new service, product, experiment, and innovate. Often, it’s done to remediate a pain point, improve an existing service or mitigate risk. Irrespective of the drivers, it’s never obvious to customers and users of data how it can directly or
Engage
This is the pinnacle of value realization. Allow consumers to engage with data. The art is in creating a super simple, slick interface to hide all the complexity and allow customers to consume data the way they want it. For example, an iPhone is used by millions of users, each in a different way. The engagement factor with data is a sweet spot where “what’s in it for me” and “I’ll consume it the way I want it” converge. Engagement is the ultimate reward, but very hard to create and maintain. It’s never accomplished overnight, needs continuous and sustained innovation, but creates stickiness. And anytime we establish prolonged engagement with customers, we create value, and opportunity.
Are we ever done?
No…never! Broader engagements in the market can reveal new sources of data to acquire. Unlocking correlations from new drivers, market conditions and other considerations can further enhance insights and understanding, and value. It is the beauty of the data cycle – if you do it right, the insights are continuous. Learn more about our ground-breaking data solution, openIDL (open Insurance Data Link), and connect with Ruturaj at ruturajw@aaisonline.com.