Why Dirty Data is Pitfall and How You Can Clean It

We’ve already discussed how important marketing research can be to any business, but how can we make sure we’re getting the most out of our work?

We know data collection can be expensive, not just in terms of the financial investment, but also in terms of the time and other resources spent to gather the data. Therefore, we have a basic understanding of just how valuable data can be. Today’s most sophisticated businesses have all bought into the benefits of data collection and analysis, but the most effective of those businesses succeed in large part because of their ability to identify dirty data as a pitfall.

This week, we’ll take a look at how you can spot dirty data in your data management systems and some basic steps you can take towards cleaning that data.

What is Dirty Data?

Dirty data can take several different forms, including data that is:

  • Inaccurate
  • Incomplete
  • Inconsistent
  • Duplicated

These errors can stem from both system and human error, owing to complications either in the data collection itself or the management of the data after it has been collected. Dirty data is useless because it paints an inaccurate picture of the results of a research study.

Any of these kinds of errors individually can have massive impacts on the results of any data analysis or usage. Duplicates can throw off averages and weights in analysis and incomplete data can create gaps that can make any kind of analysis impossible. Inaccurate or inconsistent data can prevent any analysis from becoming generalizable to any level of the business, erasing any value the raw data holds.

How Do We Clean the Data?

The most important steps in cleaning data are the optimization of data collection protocols, data systems, and ongoing maintenance. These steps ensure that you can maintain the value of the data in which you are investing time and resources in order to collect by establishing standards for accuracy. Ongoing active management of your data makes it possible for you to confidently extract the most value possible from your data.

For existing dirty data, the only recourse is to dive in and begin scrubbing out errors and inaccuracies. Begin by eliminating duplicates, crosschecking all your data management systems for consistency, and removing incomplete or inaccurate entries. While this will cut the amount of data you have, it will allow you to salvage the value of the remaining data.

Next Steps

Once you have optimized your data protocols and scrubbed your existing data of errors, you can begin reaping the rewards of clean data.

These benefits include:

  • Increased efficiency in data collection and analysis
  • Lowered costs of data collection and analysis
  • Reduced risk of inaccurate decision making or flawed strategic development
  • Maximization of the value of your data

Last week, we also took a look at the value and importance of user experience research.

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