Big Data Requires Big Cleansing: 7 Tips for Better Data Cleaning Habits

We’re squarely in the age of big data, which means most companies are collecting and analyzing more data than ever before. This is largely a good thing because it means companies have access to more objective information they can use to make better products and provide better services, and in many cases serves to improve transparency.

However, there’s a downside to having these volumes of data as well: ongoing management. Not only will you need to invest more in the security and integrity of your data, but you’ll also need to invest in periodic “cleaning,” to make sure your data tables aren’t overrun with duplicate entries, bad entries, and other points that could corrupt your overall calculations.

Fortunately, there are some cost- and time-efficient practices you can use to improve your data cleaning habits.

Tips for Better Data Cleaning

These are some of the best strategies you can use to clean your company’s data:

  1. Back everything up. Before you get too hasty with a new method of data cleaning, take a moment to check your data backups. If you don’t have a backup system in place, now’s the time to get one. If you do, make sure you’re automating your backups at regular intervals, and check to ensure those backups are accurate. That way, if something goes wrong when you’re making adjustments to your data tables, you can revert back to an old copy without fear.
  2. Split the work into chunks. If you try to clean all your data at once, like some massive, company-wide “spring cleaning” initiative, you’re going to fail. The tasks will be too overwhelming, the goals will be too unfocused, and people will likely be working past each other. Instead, try to split the work into discrete, manageable chunks. For example, you could split the work into several sub-tasks, or segment your efforts based on the platform or type of data you’re working with.
  3. Prioritize sections based on bottom-line value. If you haven’t yet, try to split the different tables of data you’re working with into sections based on how you use their data in your business. Then, prioritize those sections of data based on the bottom-line value they grant to your organization. Incorporate factors like how many departments in your organization use them, how much they contribute to your sales, and how much they can improve your efficiency.
  4. Automate what you can. There are many data tools designed to automate the process of cleaning your data. If there isn’t an existing tool that can handle your needs, you may be able to write a custom script to take care of things for you; for example, you might scout for duplicate entries, formatting inconsistencies, or records that aren’t congruent across multiple platforms. In any case, manually scrubbing records is too time- and cost-intensive to be practical for most purposes with hundreds of thousands (or more) records. Just make sure you have checks and balances in place to ensure your automated tools are working as intended.
  5. Distribute authority to individuals and teams. If you’re the one leading the charge for better data integrity, don’t try to take on everything yourself. Instead, delegate authority to various teams and individuals working under you. For example, you could task the heads of each department for ensuring the execution of data cleaning for tables related to their daily processes. That way, you can split the workload evenly throughout your organization, and you can assign the most relevant experts to each data table.
  6. Polish your collection and organization practices. After you’ve spent the effort cleaning your data tables, you’ll need to instate new practices to keep them that way. Revisit your data collection and organization practices to maximize your data integrity; for example, you could create new policies to ensure the proper formatting of new entries, or retrain your data specialists to ensure they keep your most important tables in order.
  7. Improve upon your first efforts. If this is your first time overseeing a data cleaning effort, you’ll likely make several mistakes and suffer from inefficiency. Take note of these flaws, and come up with a way to improve upon them the next time you scrub your data tables.

Toward Better Data Management

There are several important goals to consider in the realm of big data analytics (and data management). Obviously, you’ll want to utilize and manage data in the way that best benefits your company overall, but what should you be emphasizing? Cost? Accuracy? Integrity? Consistency? Scalability? There isn’t a single right answer for how to manage your ongoing practices, but you should be thinking carefully about your priorities, and gradually adjusting your efforts to align with those end goals.

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