Best Practices for Big Data | TechWell

Best Practices for Big Data

Last week, I covered the fact that there are a lot of different definitions for big data. More importantly, I stressed that knowing what big data means to you and what role it plays in your business decisions is ultimately the most important definition.

However, my exploration into the definition of big data taught me that a lot of people don’t really know how to use big data, don't understand what it is, or grasp that it can do more harm than good if used irresponsibly. So, here are some best practices when it comes to big data.

Don’t have an end in mind: Don’t misread this. You should be trying to accomplish something with your data, but don’t try to solve it with a certain solution in mind. Data can be subjective when you have an idea of how it should—or how you want it to—flow. Leading yourself to an answer instead of letting the data guide you can be a set-back to your operation as well as a huge waste of time.

Know your time restraints: For some big data users, time is of the essence. The faster you can tie together the who, what, when, where, and how, the faster you can take an action that lends itself to positive results. However, for others, there is time to step back and take a more scenic approach to get the information you need. Understand how heavy the clock is, because there is nothing worse than analyzing a bunch of information only to have it be “out-of-date.”

Just because you can, doesn’t mean you should: The reality is that big data can be applied to nearly everything. When the sample size becomes big enough and the quantitative function is present, then you can implement big data. But, just because you can track how many times a day your employees wear blue ties doesn’t mean it’s going to help you develop better software. Also, you can unearth some information but you can’t really take action on it. You can track how many pots of coffee are being made in a month, but are you really going to mandate brewing less coffee?

Don’t be afraid to kick the tires: I know this is in direct contradiction to the previous best practice, but big data is tricky, and so are these best practices. Take a holistic approach to your information, and you’ll understand that the problem you are trying to solve with big data might have many smaller variables and components inside that are skewing your results. So, encourage yourself to travel down a road you normally wouldn’t. Just, whatever you do, don’t lose track of what you are trying to accomplish.

Other best practices exist out there, and this is by no means an exhaustive list. Just as there are multiple definitions of big data, there are numerous suggestions on the best way to go about using your data.

What are some of your best practices?

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