Want to stop nearly any enterprise initiative dead in its tracks? Mention a lack of data, and you’ll shut down everything from mobile apps, to inventory management, to a new HR initiative. On the surface, using data as a “must have” makes sense. If you want a more accurate sales forecast, you’ll clearly need historical data, just as launching a new product likely requires data on the market and potential customers. However, with data, the perfect can quickly become the enemy of the good, and data becomes an easy delay tactic.

Stop enabling bad data behavior

Too often, IT leaders are partners in crime with everyone who invokes the data excuse. For years, many of us have preached the virtues of “one source of the truth” and data management until we were blue in the face, so when one of our colleagues wonders aloud whether the organization has the right data to move forward, we’re all too quick to join them in suggesting we need more data, cleaner data, or consolidated data.

The problem is that a single monolithic, perpetually-clean, universally available data source is generally a pipe dream. Even tiny organizations have key information spread across multiple systems, and larger organizations might have bits, pieces, and redundancies across dozens of systems. Throw in a few SaaS (software as a service) or cloud apps, and you might not even have access to the data sources that underpin the rented systems. The engineer in most of us happily starts designing the perfect solution to these problems, and is quickly thinking in months or years rather than weeks, and millions rather than thousands of dollars.

SEE: Culture, automation and self-service: The keys to big data success (Tech Pro Research)

Decisions are better than data

We tend to forget that the purpose of data is to enable decision making, and collecting data is not an end in itself. It’s rare that we have the perfect, full set of data related to every business problem, but it’s equally rare that you actually need “perfect” data, or that perfect data is even an attainable goal. Companies that make quick, reasonably well-informed decisions will almost always outmaneuver the company that spends months-long quests on the search for data while the market passes them by.

Rather than fueling the data excuse by delving into all the options for corralling and massaging, try to determine what fidelity of data is needed to make a decision. For example, when travelling by airplane, the only data I need are the destination city or perhaps the airport if it’s a city with multiple airports. However, if I were travelling by bicycle, I’d want detailed directions, locations of bike paths, and areas to secure my bicycle. Both activities are related to transportation, but one requires much higher-fidelity routing data than the other. Even the same activity might have different fidelity requirements depending on a number of factors. Continuing the example above, the pilot of a plane requires far more data than a passenger.

When you refocus a discussion on the decision, you may also be able to find alternate data that help make the decision, but are easier to acquire from a technical perspective. Creative use of trend analysis or visualization can also make up for imperfect data, and in many cases, time can be a factor that makes an imperfect decision today better than a perfect one next month.

SEE: Special report: Turning big data into business insights (free PDF) (TechRepublic)

Steering conversations in this manner requires some business savvy and finesse, especially since the data excuse is often little more than that: an excuse. Blaming complex technologies for one’s inability to make a potentially risky decision is often used as a trump card, especially in organizations that are averse to failure. If you work at an organization where people actively avoid decision making, or failure is swiftly punished, you’ll likely be even more subject to the data excuse, as it’s an effective means of self-preservation and passing the buck to IT, rather than citing fear as the primary motivator for not moving forward.

Even with the best systems, and an endless budget and staff, you’ll likely never achieve a data nirvana where every datum exists only in one place, and is perfectly cleansed, de-duplicated, and readily accessible. Even if that were possible, not having data is often not the reason why projects stall and key decisions remain unmade. Look for the data excuse, and rather than piling on, try to identify how to get the organization to a decision and a move forward, rather than using data as an excuse for stasis.

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