By now, most business leaders recognize that data is the fuel powering the business in this information age. Everyday, more executives say they want their business to be “data-driven” and that data is a strategic asset — it sounds great but would does it mean?
The challenge to the “data as asset” concept is that data in data warehouses is an intangible asset and therefore not valued in financial terms. Accountants have said, “how do you objectively value something you can’t touch?”. So, let’s dive into how data becomes value.
As a Finance professional and process reengineering consultant implementing finance performance management, business intelligence and customer management systems, I had also struggled explaining the value of data in the past. Unless it was a customer list purchased at arms length, data didn’t make it onto the Balance Sheet.
Not until I became an operating executive responsible for managing information systems did the answers become more obvious.
My own experience with valuing data was shaped when I recently established a Data Governance Office for the primary purpose of increasing data quality. Not until we connected the dots to decisions and more specifically explained it as, “bad data leads to misleading analyses and incorrect decisions”, was there widespread agreement that investing in this intangile “data quality” made business sense. (It also didn’t hurt that a global set of banking data regulatory requirements came ino play, but that’s another post!)
A key insight: Just as fuel in tanks has only the potential to drive our cars forward until we turn the engine on, data itself has potential value and business value once it’s being used. Although you can touch it, fuel is also potential value until a spark makes it run the engine.
Q: So how does this data “fuel” become value in a business?
A: The secret is in decisions. (Cue up your business process flow chart). Every business process has decisions, big and small. Every customer decision lives in a process, as does every product decision and every risk management decision. All these decisions of course all add up to your business model.
For example, a traditional Bank’s business model relies on decisions to lend to or trade with retail, corporate, commercial and sovereign customers and counterparties. Better data quality fuels better credit, market and liquidity risk decisions.
Every decision ultimately has a financial impact. The question to ask now: is each and every one of your decisions data-driven or do some go by gut feel?
- pricing decisions result in margin at the time of sale — does data support pricing analytics?
- lending decisions result in revenues for banks as well risk of default — does enough data support your credit risk models?
- hiring decisions having short term HR costs but don’t overlook the long term reward you expect from the intelligence you just onboarded — how do you quantify your new HR investment?
Picking up the theme of this blog, the more data-intensive your decision-making process, the more algonomic your business becomes. Increasingly using software (aka algorithms) to run pricing models and deliver the decision analytics takes you deeper into the algonomic zone. Now, I’m not saying that businesses should automate all their decisions, but in leading organizations software certainly is increasingly augmenting every human decision.
The Data-Driven Bonus: Unlike fuel, using your data doesn’t mean it’s gone or necessarily transformed. It’s a reusable resource! With incredibly cheap data storage you can keep it “forever”! More on that in future posts.
Now that we’ve know what it means to be data-driven, here’s something to think about:
Does your organization invest in improving the quality of its data fuel? Is the fuel really driving your most important decisions?