How to Clean Up Your Data Storage When You Have Too Much

The steady stream of unstructured data now available to enterprises has a potential negative side effect. Namely, the possibility of having too much data, which in turn makes it easy for that data to become disorganized.

While this may not seem like a big problem, in reality, the consequences can be extremely detrimental—not just for an enterprise’s ability to gain usable insights from its data, but for its overall productivity as well.

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How do you know if your data is disorganized to the point where it’s a problem? There are some telltale signs, such as:

• Conflicting information, like one department relying on datasets that are different—or in some cases, directly opposed to—datasets used by another department
• Difficulty answering questions using data that your organization knows exists but can’t easily surface
• Data maintained by more than one area of the organization, like competing email lists in marketing and sales departments

At best, each of the signs will lead to headaches and missed opportunities for your organization down the road. At worst, they will eventually allow for data breaches and permanent damage to your organization’s brand.

Reining in your data

So how do you get in front of these potential issues? How do you clean up—and keep tidy—the millions of bytes of data moving through your organization?

The first step is to realize that it will take a coordinated effort. Cleaning up and organizing such a large amount of information is not something you can simply place on the IT desk and hope for the best.

Once everyone is on board, your next move should be a forensic examination of all your data. You need to know:

1. Where all your data is coming from
2. What type of data you are capturing and storing
3. Which areas of your organization need access to your data
4. How your data is being used by your teams

This forensic examination needs to extend beyond your storage platform. Even the individual machines of your team members should be assessed, since it’s not uncommon for data to be stored locally on a machine in an attempt to increase efficiency.

Separating the good from the bad

After you’ve put the time and resources into gaining a thorough understanding of your data, you need to dig deeper into the actual quality of your information.

This means determining which data is essential, which may be of use down the road, and which can safely be discarded.

As you’re doing this process, it’s critical that you plan out—and implement—systems to centralize, classify, and tag all your data. Not just the data you already have on hand, but future data that will be streaming into your organization.

Doing so will help ensure your data remains more organized going forward. It will also help you monitor and recognize errors or potential lapses in security much more quickly.

Good data organization

In order to keep your data organized and secure while still being able to put it to work, you generally need to hit three goals. These are:

1. Consolidate & centralize

Create a single repository such as data lakes for all your raw and unfiltered data to land in. Once there, it can be used for data science in order to experiment with various data sets to find new insights, correlations, and potential areas of growth.

2. Investigate & eliminate

This is where the forensic examination mentioned above comes into play. It’s also when you sanitize your data to remove unnecessary information, identify sensitive data, and determine where tools like tokenization should be used to obfuscate that sensitive information.

3. Standardize & democratize

Implement strict governance on all your data by building out a data catalog, including tagging and categorizing, in order to put constraints on who has access to what. Then implement measures to automate the capturing and cataloging incoming data so that your entire organization has proper access to the datasets it needs.

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