If you're facing one of these challenges, launching a formal data monetization effort can be overkill, if not a waste of precious time. Even with a formal methodology, quantifying the value of data can be arduous, and - even when rigorous mathematical formulae are applied - subjective.
Managers grappling with proving data value usually make a bigger impact when they instead focus on formalizing a data prioritization process that aligns with their companies' strategies. Companies often designate and fund projects according to their strategic alignment or their business impact. Data should become part of these processes.
If, for instance, a new Digital Wallet project has been approved, the data that enables that new functionality should be part of the budget and its value should align with the digital wallet's benefits to the business. This project-focus lessens the risk of data monetization and aligns data with tactical delivery. In fact, the broader the data monetization effort - beware any "Enterprise Data Monetization" efforts - the more fraught.
If you must quantify the value of data, by all means do so in the context of its role in solving a specific problem. This problem should either be costing the company money or impeding new revenues. Find a problem your company wants to solve, quantify the value of solving it, and illustrate data's role in that solution.
For instance, at an international bank we calculated the cost of individual data audits that were mandated by both the bank's internal risk department as well as by external regulators. These audits resulted in fire drills that cost the bank money. In 2014 these audits cost over $2.3 million -- $862,000 of that consumed by pinpointing, formatting, and deploying the right data. By automating the access to key audit data, we were able to bring the cost down to $120,000 per year. And improvements in employee satisfaction and technology upgrades had a ripple effect outside of the tangible cost savings.
Simply put, there are better ways to answer the questions above than initiating a large-scale or (God forbid) enterprise-wide data monetization effort. Apply more rigor to project prioritization, or enlist executives in "yes/no" decisions. Simply put, data monetization exercises often take more time and cost more money than their outcomes warrant. In the time it takes to truly quantify the value of information, companies could actually be delivering something new.
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