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The Importance of Data Cleaning

One of the most important parts of living a healthy lifestyle is good hygiene. Brushing our teeth, using deodorant, showering etc. You know, taking care of our bodies. The more we do so, the better our body’s function, the more attractive we may appear and the better we feel about ourselves. We perform better when we feel good. Likewise, good data hygiene is just as important for business. How we take care of our data can be the difference between over-performing and under-performing ads. In the medical field, it can well be the difference between life and death. So how do we ensure that our data hygiene is well preserved? 

In this blog we’ll look into data cleaning, why it’s important and how you can go about doing it.

What is Data Cleaning?

Data cleaning refers to the process prior to the beginning of your core analysis. It involves validating your existing data while removing “rogue” or imperfect data along with duplicate data from your sources. Since data analysis is usually one of the main resources in making business decisions, the research that’s been done needs to be accurate. Which is why, many times, removing rogue data altogether may not be the best route. An incomplete data set can be just as detrimental as a dataset with a lot of imperfect data.

A good way to look at it is as if you’re preparing to smoke a brisket. You want to trim as much fat in order to ensure an even cook but not trim too much fat to where your brisket can dry up. Knowing when to clean and what to clean is an important part of the data cleaning process.

Why is Data Cleaning important?

A common acronym used in data analytics is GIGO (Garbage In, Garbage Out) which usually means that if the quality of your data is subpar, the results that stem from it will be too. Kind of like the saying, “You get what you pay for”, the same logic applies. If you put in little effort to clean your data, your results will be extremely flawed.

Some other ways data cleaning can be beneficial: 

  • Staying Organized – data cleaning information such as Names, Phone Numbers, Addresses etc. helps your information stay organized and easier to find when needed.
  • Avoiding Mistakes – many businesses come with a database of prospects and current customers. If this database is in order, business goes on as usual. If it’s jumbled and unclean, you run the risk of sending the wrong information to the wrong audience. 
  • Improve Productivity – having a data cleaning routine in place saves you the time and energy of searching for information that would be lost in the shuffle if things were unorganized. 
  • Save Money – Bad data can carry financial consequences as well. Such as causing you to turn off performing ads by mistake or paying for underperforming ads. Cleaning your data will allow you to spot inefficiencies quickly and help you make the necessary adjustments.

How to Keep your Data Clean

  1. Remove Unwanted Observations – Hone in to the problem you’re trying to solve and remove any observations that don’t contribute to your goal. For example, if you’re analysis is on the weight loss habits of men, you’d want to eliminate the data pertaining to women. This step also includes removing duplicate data, as many times multiple sources are used to create a dataset and these different sources can contain the same information.
  2. Fix Structural Errors – Structural errors can occur as a result of poor data cleaning. Typically, it’ll include grammatical errors like capitalization or punctuation infractions which can cause issues if not addressed. Many times, this can occur with words that are spelled the same but carry different meanings. Data cleaning will allow you to find these errors and organize your dataset so that it’s easier to navigate and find the information you need.
  3. Standardize Your Data – Standardizing your data refers to making sure your data is following the same set of rules. For example, using the same unit of measurement across the entire set. Using different forms of measurement can be confusing and easy to misread. 
  4. Fix Contradictory Data – This entails eliminating any data that is inconsistent with the rest. For example, if an employee logs 80 hours but his paycheck is equivalent to having worked 60 hours, that’s an issue stemming from contradictory data.
  5. Validate Your Dataset – all this means is to give your dataset one less review and make sure it’s ready for analysis. The same way one would proofread a story for grammatical errors or typos before publishing we want to proofread our datasets, so to speak. 

The Bottom Line

The bottom line goes back to Feel Good, Work Good. If your data is clean and is routinely kept up with, the results will show it.

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