Oil Spills & Bad Data

A couple of recent pieces about the oil spill in the WSJ highlight some common BI challenges. The first highlights the ongoing difficulty of getting accurate information.

Not every company needs to track such complicated and fluid phenomena as crude oil spewing from a failed well into a huge body of water. Not every company has to deal with literally burning platforms. But BP’s need to track accurate data that is broadly understood across organizational boundaries is fairly common. All types of organizations struggle to capture accurate data and further struggle to assure cross-functional understanding. Many organizations can’t even agree on standard definitions of basic metrics like “sales revenue” or “customers” across business units.

The second piece outlines what can happen when questionable data is integrated into widely used risk management models (registration/subscription required to access). Greater risk may result when an entity improperly interprets its data, or believes its data can answer questions that it can’t answer. In fact, bad or misunderstood data may have helped cause both the current oil spill crisis and the bank meltdowns of 2008-09:

… models that were supposed to predict bad outcomes failed—financial models didn’t correlate risks relating to a housing bubble, and drilling models didn’t predict what would happen if a rig blew far below sea level.

We live at a time when we expect access to good information, whether to assess financial risks or the impact of drilling accidents.

It’s an old saying, but it’s worth repeating: good decision making starts with good data. But, I’d go even further: in today’s more complex businesses, core data must not only be high quality, but must also be broadly understood across all the silos, platforms and “rigs,” of the organization.