Data Use: Living Together

As set out in Strategies for Unlocking the Value of Third-Party Data, organizations are faced with both a need and an opportunity to revisit how they acquire and extract value from third-party data. In Solving the 80/20 Data Dilemma, six phases associated with acquiring and extracting value from data were identified: Discover, Assess, Acquire, Adapt, Use, Monetize (optional).

With Discovery, Assessment, Acquisition, and Adaptation completed, the ‘Use’ phase begins. This should be far less active than the previous phases. The challenges of finding and assessing data are a distant memory. The frustrations of acquisition and making data fit for purpose have been overcome. The data product is now being used – likely in a point solution – to deliver against the specific value proposition for which it was originally sought. Things seem fine, but is it really working? Did you make the right decision? What’s the return on investment? Did you think this far ahead when you started?

At this point, there is little you can do to change the outcome – good or bad. Without supporting technology like a collaborative data exchange, most organizations will have spent significant time and effort and will now be locked into a multi-year licensing agreement. If the data hasn’t worked out as hoped there are few levers you can pull to change things. Whether good or bad, you can spend some time understanding your outcome to determine the impact of your efforts and gain valuable insight into how you might improve.

Trying to scientifically measure return on investment for a given data product you are consuming can be really clear or very ambiguous. Understanding the total resource that went into the various phases of acquiring and extracting value from data can also be difficult. Here are some thoughts on what to look for:

  1. Quantifiable business value. Some use cases easily lend themselves to measurement as they are directly aligned to a measurable outcome that can be tested with or without a given data product. In many circumstances, the relationship is ambiguous, so precise estimates can be elusive. Directly end-user feedback and A/B testing or stack-ranking alternatives are good options to explore.
  2. Total cost of ownership. Third-party data costs more than the price of the license. When you assess the cost, you need to think about people’s time, the tools and technologies used, and the storage and processing necessary to extract value. You also need to consider opportunity costs. What if you had not bought the data? What if you had got the data faster? Thinking about costs helps you examine your decision-making and third-party data capabilities.
  3. False economies. Your technology project will have a run-rate. If a lack of data leads to expensive resources sitting idle you may be suffering from false economies. Was there ‘analysis paralysis’ during the assessment phase? Did drawn-out negotiations on licensing costs really generate a better financial outcome? 
  4. Future returns. What have you done that can be reused by your organization? How much of your hard work can be shared by others? Can you syndicate costs efficiently if there is new demand for the product you are using? Will you be able to realize economies of scale? Regardless of your outcome, you can help others avoid mistakes and have a better chance of success in what can often be a high-cost, high-risk endeavor.

Effective strategies for maximizing the value of third-party data require a collaborative, organization-wide mindset. To be truly effective they also need enabling technologies like collaborative data exchanges to shift the current paradigm. Most of all they require attention and focus, without which nothing will change and many organizations will find themselves very far behind where they hoped to be. Data is only valuable when it’s used and getting to that point as efficiently as possible should be a business imperative for every large data-driven organization.

Authored by Anthony Cosgrove (Co-Founder) at Harbr

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