Four Steps to Fixing Your Bad Data

At this point, most everyone has heard that S&P downgraded U.S. government debt on Friday. An important piece of the story involves the $2.1 trillion error in debt that S&P provided in its original rationale. As one might expect, officials are using the error and S&P's earlier failure to properly rate bundled mortgage products to argue that the downgrade is incorrect. S&P's reputation, whether the downgrade is correct or not, is sure to take a hit.

Unfortunately, reputational risk is only the tip of the iceberg when it comes to bad data quality. From falsified mortgage applications and bundles of toxic mortgages, to incorrect credit ratings and balance sheets that couldn't be trusted, the financial crisis is as much about bad data as it is about unfettered greed. And the carnage continues, as counterparties sue one another and courts deny foreclosures when the paper trail is suspect.

And no industry is immune. Think health care is immune? Or the military? And the food industry? No such luck. In marketing, bad data make it more difficult to know the potential client. In procurement and logistics, bad data send deliveries off in the wrong direction. In manufacturing, bad data mean components don't fit together properly.

Want to anger a customer? Work for a mobile phone company, and send him or her an incorrect bill.

Bad data make it harder to make and execute decisions. It is trite to observe that decisions are no better than the data on which they are based. Worse, bad data contribute to "analysis paralysis" and stasis it causes. And the bloodiest political battle I've seen erupted when two divisions, using "close but not the same" data, reached two very different conclusions.

Bad data stymie analytics and "big data." You simply can't trust the insights when you can't trust the inputs.

In the military and health care, bad data kill people.

In light of these problems, it is almost prosaic to observe that bad data increase cost. Executives ask their staffs to track down the "facts" that just don't look right (a "shadow activity" that doesn't appear in job descriptions or expense breakouts). On a larger level, departments implement "shadow functions" and companies "shadow departments" to deal with the bad data. It is non-value-added work, and it is stunning that, in these times of fiscal austerity, it is allowed to continue.

I can go on and on. Unless you're one of the special few who address data quality aggressively and proactively, you're almost certainly suffering from the above. Fortunately, we can do something about data quality. The following four key steps can point your company in the right direction.

  1. Admit you have a data quality problem. Just like any twelve-step program, admitting the problem is key. From there, follow the next three steps for data improvement.
  2. Focus on the data you expose to customers, regulators, and others outside your organization. Take a careful look at your system of controls. Is it up-to-snuff? Make sure — not only that the right controls are in place, but that you're actually using them. Every time.
  3. Define and implement an advanced data quality program. Making sure data leaves the door correctly may be a viable short-term alternative, but there is already too much data, and the quantities are growing. Just as manufacturers found that they had to "prevent errors at their sources," so too with data. You need a quality program that does so.
  4. Take a hard look at the way you treat data more generally. Almost everyone readily acknowledges that "data are among our most important assets." But they don't manage them that way. Indeed, data are almost invisible. And the top person responsible for data may be an architect buried deep in the bowels of IT. If this description rings true, you need to get an aggressive data program, with real talent, budget, and teeth in place.

To be clear, steps three and four are profound. You have to do a lot of things reasonably well (focus, measure, manage processes, eliminate root causes, etc.) to manage quality. Similarly, the notion that "data are assets" sounds simple and is anything but. Everyone touches data in one way or another, so the tendrils of a data program will affect everyone — the things they do, the way they think, their relationships with one another, your relationships with customers. This work is not for the faint of heart. But it is essential.

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