Not All Data Is Created Equal

When you’re driving through a blizzard, all the snow on your windshield looks the same. If you were to stop and examine the individual snowflakes more closely, you would discover an astonishing variety of shapes and formations. While linguists and anthropologists bicker over how many words the Eskimos really have for snow, the simple truth is that there are many different kinds of snow.

Like snow, data comes in a wide variety. There’s personal data, demographic data, geographic data, behavioral data, transactional data, military data, and medical data. There’s historical data and real-time data. There’s structured data and unstructured data. It often seems as if we are surrounded by rising mountains of data.

The big difference between snow and data is that unless you own a ski resort, snow isn’t perceived as economically valuable. Data, on the other hand, is increasingly seen as a source of power and wealth.

If you live in a region where winter snowstorms are common, then your town probably has a fleet of snowplows and a snow emergency plan. Very few companies, however, have developed comprehensive policies and robust practices for categorizing and prioritizing their data.

“The main challenge in creating policies and practices for managing data effectively is the limited ability of most businesses to identify data assets and categorize them in terms of criticality and value,” says Chris Moschovitis, an IT governance expert and chief executive officer ...