When I talk to executives at credit unions about big data, Iinevitably get two reactions. On the one hand, they’re excitedabout what big data means for the future of banking. On the otherhand, they’re wary about the promised return on these investments.They wonder if the demand is simply ahead of the technology, or ifthe impact of big data simply isn’t as great as many people haveassumed.

These executives are often right to be disillusioned. Far toomany efforts with big data suffer from what might be termed a“garbage in, garbage out” mentality. Such efforts prove that it’snot enough to gather reams of data. The data also has to berelevant, cleansed, contextualized and well structured, even ifthat structure is a derivative of an unstructured data set. Withoutthese features, these companies end up with garbage. And gettingaccess to lots of garbage – which is what “big data” often equatesto – won’t help with a single business decision. In fact, in caseslike these big data can often just be a distraction.

How do you make sure you’re avoiding these problems and actuallymaking the most of the promise of big data and machine learning?Here are three suggestions.

1. Precisely Define Your Goal

It starts with defining your destination before embarking on aproject. In other words, you must work backwards from the end goalto define your data needs. Ask what you want to achieve, and gofrom there – without jumping to any conclusions about what data youmight need.

Do you want to increase brand awareness? Do you want to drive upthe number of auto loans at your institution? Do you want to expandyour credit card reach? Whatever it is, stay focused on that goalwithout worrying about the specifics of the data you’ll need.

2. Know How to Measure Success

From there it’s critical to clearly define how you will measuresuccess. If you want to increase brand awareness, how will you knowyou’ve succeeded? How many auto loans are you aiming for? Just asimportantly – what are the exact metrics you’ll use to trackyour progress and what are the actions you must take to accomplishthese goals? If you don’t know these metrics and processes, it’snot worth moving ahead with data collection.

This gets at the reason so many companies unwittingly have a“garbage in, garbage out” mentality when it comes to big data. Theydon’t know how to measure success. And since they don’t know how tomeasure success, it doesn’t come as a surprise at all when theydon’t achieve it. Start with a small, simple measurement if youmust, but start with a concrete measurement.

3. Assign an Owner and Hold ThemAccountable

Finally, the data must have a clear owner. I’ve seen far toomany companies gather reams of data only to have it essentiallygather dust because no single person is in charge of it. Don’t makethe mistake of paying for access to data only to have it be ignoredbecause no one takes ownership. In addition, make sure that thisowner is accountable to show how the data does or does not meet thestated business objective. Without accountability, you risk gettingsix months into the project only to realize that nothing hasreally happened in terms of meeting the businessobjective.

Only once you’ve walked through each of these questions – Whatdo you want to achieve? How will you measure it? Who will own it?– should you start looking at what data you will need and howyou will ensure that it’s the exact data you need.

Often this process will lead you to the conclusion that yourscope is far smaller than you may have initially thought. Insteadof needing access to petabytes of information, you only need accessto the right sliver of data. From there it’s far easier to makeabsolutely certain that the data is correct. In this way you canensure that you’re avoiding a “garbage in, garbage out” mentalityand instead getting real value from data and machine learning. Inshort, if credit unions stridently follow these three guidelines,they will see the results they want from leveraging big data intheir business.

Jon Ogden is the Director of Content Marketing at MX. Hecan be reached at 801-669-5500 or [email protected].