The Truth About The Truth

The difference between data, information, and the truth has never been so important.

The second reason that the truth is hard to know is a result of our human frailties. We often crave the truth so badly that we're all too willing to believe what we're told, regardless of how honest the source is or believable the facts are. And once we've latched on to a perceived truth, its aura of authenticity and the sycophantic nature of groupthink make it self-perpetuating. This is what happens with groupthink, management by committee, and other such evils of the modern world. Which is a major reason why consultants such as myself can actually make an honest living: More often than not my job is to show how a decision, strategy, or basic assumption that is fundamentally wrong has been distorted into something resembling the truth. Once the truth about the "truth" is known, the process of disentangling the viral nature of falsehood and the search for the real truth can get underway. This happens all day, every day, in every company, even my own. Strike two.

The increasing automation of business processes — using the enterprise software we all love to hate — can take the time/distance problem and the self-perpetuating nature of perceived truth and create a runaway train effect that can take a little untruth and turn it into a major operational disaster. The infamous case of Nike's supply chain software misdirecting shipments around the world forced Nike not only to take a major write-down but also suffer more than $2.5 billion in lost market capitalization. All because a single set of wrong assumptions — about where $100 million in finished goods had to end up in Nike's global supply chain — were treated as the truth when they couldn't have been farther off base. Strike three. You're out.

Ripple Effects

What makes this more scary is that many companies are building an IT world full of Web services that could take an untruth from one company and send it flying in real time across an entire supply chain, creating a network effect of bad data and bad processes that could do a lot of damage to a lot of innocent companies before anyone found out, if indeed they ever did. Imagine if you were a logistics partner or supplier to Nike taking in automatic demand and shipment data from your number one partner and planning your business processes accordingly. Imagine 20 companies, or 200, doing the same: factories in Asia, shipping companies across the Pacific, distributors and retailers in the United States, all marching in lockstep to the same bad data. The ripple effect could turn into a tsunami before anyone could stop it.

It turns out that in our rush to put information to work, we forgot to put in place the formal mechanisms that can legitimately question our initial assumptions and keep the runaway truth train from heading down the wrong track. And as companies make use of external Web services over which they have little or no control, the garbage-in/garbage-out problem becomes even scarier. A bad external service — a logistics tracker, a foreign exchange calculator, or a credit authorization system — could send your company down the road to lost revenues and lost customers, and you wouldn't even know it until it was too late.

So before you're left wondering what happened to the WMDs or your partner's last shipment, start questioning your initial assumptions. Then worry about how those assumptions get perpetuated, unchallenged, throughout your increasingly automated IT systems. And then worry about how hard it will be to fix the problems once the runaway train has left the station. The truth about the truth is that it's just as hard to know as ever before. And while that hasn't stopped us from running with what little truth we think we possess, it should at least give us pause before we send those boots to Bali instead of Baltimore.

We need to address these problems now, before it's too late to do anything except watch the real truth — not the one we think we know — unfold before our eyes.

Joshua Greenbaum is a principal at Enterprise Applications Consulting. He researches enterprise apps and e-business.