In which Jill shares a word on quantifying the value of information. (And the word is: “Don’t.”)
More CEOs than ever before are considering data to be a critical business enabler. The good news here is that this translates into a willingness to invest in data—its skills, its enabling technologies, its quality, and its protection. The bad news is that executives are still unsure of the processes and tool sets necessary to manage corporate data in a sustained way.
— The New IT: How Technology Leaders Are Enabling Business Strategy in the Digital Age (McGraw-Hill, 2015), page 127-128.
“And then what?”
The recently-appointed Chief Data Officer at a regional bank looked at me as if I’d just asked him whether he still loved his wife.
But it was a typical question for me, a version of the well-worn “What’s the desired outcome?” we’d all learned as junior consultants back in the day. Predictable as the question is, I’m always a little vexed when it makes C-level executives uncomfortable.
The “data is a corporate asset” aphorism has traveled to the C-suite faster than the express elevator in your headquarters building. And why not? Executives understand that data is a critical component of running their businesses more effectively, getting new customers, and driving innovation.
Many of these executives find comfort in the idea that there is an economic value to data, allowing them to apply accounting and operational principles to data that they apply to other assets oh-so-well. Yes, data has value. And yes, that value may be tangible or intangible. But before executives approve efforts to calculate that value they should ask themselves the following question:
If we embark on quantifying data’s value, how will we use the result?
My “…and then what?” question to the CDO was a variation of this. I was really asking him how he and his team would apply the results of a data valuation exercise. Because quantifying data can be a Trojan horse for other often more basic questions, including:
- What data do we have? Often data quantification is a euphemism for conducting a basic data inventory of what data exists, what state it’s in, where it originates, and how it’s managed. By couching all this as “data valuation,” companies add complexity, and often months or even years of additional work that will never pay off.
- How do we prioritize the data? Many companies understand that they need to invest in data, just like they invest in other assets. Trouble is, they don’t know where to begin. Assuming that data should be prioritized based on its monetary value is risky. It’s also subjective, inviting political debates between departments, a frequent outcome of which is all parties agreeing to disagree and retreating back to their silos.
- How do we convince executives that our data is important and requires investment? This is often the struggle of smart people who spend their time finding, accessing, reconciling, correcting, provisioning, and validating data. They often rely on their own internal relationships and manual processes to turn raw data into consumable information. They might not have the organizational authority to convince executives of the importance of data. Thus they feel that promising a financial valuation of data will pique executives’ interest.
- How can we become a digital business if we don’t get our arms around our data? It’s true that mobile devices, sensors, engines, and smart appliances generate data as well as consume it. But linking the monetary value of existing data and new data emitted from digital devices won’t necessarily make you more digital-ready. In fact, it could slow you down.
- How do we come to consensus about where to start? Some companies that launch a data valuation effort often use it as a pretext for getting people across organizations to agree on an action plan, or to simply make them more data-aware.
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. (Chapter 6 of The New IT illustrates such a prioritization process.) 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!