Spring Cleaning: Data Edition

‘Tis the time of year for a deep clean. Is your data screaming for a cleanup too? 

What is Data Cleanup?  

Data cleanup refers to the process of preparing data for analysis. To make your data useful enough to drive decisions, you must remove or correct any data that is incorrect, incomplete, irrelevant, duplicated, or formatted wrong. 

Why is it Critical to Digital Transformation? 

Susan Walsh, the Classification Guru, joined us to discuss the importance of data cleanup. She also explained the role that people and technology must play to keep data tidy after the initial project to get things back in shape.  

Susan has seen businesses spend thousands, if not millions, of dollars on digital transformation only to find that failing to take the necessary steps to ensure data accuracy early on caused them to abandon a project.  

Even if you are using a modern solution, you could need a data overhaul. Here are some signs that your data may need some spring cleaning: 

Why Data Cleanup Matters in Digital Transformation

3 Signs You Need a Cleanup: 

1. You are Constantly “Massaging” the Data

Data-driven decisions are only as good as the data behind them. Is your team spending hours on end manipulating data to produce the reports needed to impact critical business decisions?

Time for a cleanup!

When you clean your data, your team can focus on interpreting data and putting insights into practice without worrying about incorrect data. Think of the time and productivity you could unlock!

2. You Make Most Decisions Off of Feelings

While business decisions will always carry an element of subjectivity, if you are basing most of your decisions off gut feelings, it is likely…

…Time for a cleanup!

Mine the gold that is your data! Clean it up and use it to drive your decisions.

3. You Lack Consistency in When, Where, and How Your Team Enters Data

Does your team enter data, save files with inconsistent naming conventions, or sometimes *gasp* skip data entry altogether? Part of the problem could be training, but it is most likely because you haven’t set up a consistent process for data input

Time for a cleanup!

Consider cleaning up your current data first and then focusing on building a consistent process for your team to follow. In the interview, Susan dives into the role that People need to play in your data strategy after a cleanup. Technology can help, but it is up to your team to support a consistent data strategy.

Make Sure Your Data Has Its COAT

In the interview, Susan shares her acronym, COAT, to help you easily remember that you want Consistent, Organized, Accurate, and Trustworthy data driving your digital transformation.

Be sure to check out the full interview and take a peek at Susan’s full article on the Data COAT.

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