{"id":3773,"date":"2020-11-17T01:00:00","date_gmt":"2020-11-17T00:00:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/what-is-data-cleaning\/"},"modified":"2023-09-14T14:33:16","modified_gmt":"2023-09-14T12:33:16","slug":"what-is-data-cleaning","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-cleaning\/","title":{"rendered":"What Is Data Cleaning and Why Does It Matter?"},"content":{"rendered":"

Effective data cleaning is a vital part of the data analytics process. But what is it, why is it important, and how do you do it?\u00a0<\/strong><\/p>\n

Good data hygiene is so important for business. For starters, it\u2019s good practice to keep on top of your data, ensuring that it\u2019s accurate and up-to-date. However, data cleaning is also a vital part of the data analytics process. If your data has inconsistencies or errors, you can bet that your results will be flawed, too. And when you\u2019re making business decisions based on those insights, it doesn\u2019t take a genius to figure out what might go wrong!<\/p>\n

In a field like marketing, bad insights can mean wasting money on poorly targeted campaigns. In a field like healthcare<\/a> or the sciences, it can quite literally mean the difference between life and death. In this article, I\u2019ll explore exactly what data cleaning is and why it\u2019s so vital to get it right. We\u2019ll also provide an overview of the key steps you should take when cleaning your data.<\/p>\n

Why not get familiar with data cleaning and the rest of the data analytics process in our free 5-day data short course<\/strong><\/a>?<\/p>\n

We\u2019ll answer the following questions:<\/p>\n

    \n
  1. What is data cleaning?<\/a><\/li>\n
  2. Why is data cleaning important?<\/a><\/li>\n
  3. How do you clean data?<\/a><\/li>\n
  4. What are some of the most useful data cleaning tools?<\/a><\/li>\n<\/ol>\n

    First up\u2026<\/p>\n

    1. What is data cleaning?<\/h2>\n

    Data cleaning (sometimes also known as data cleansing or data wrangling) is an important early step in the data analytics process<\/a>.<\/p>\n

    This crucial exercise, which involves preparing and validating data, usually takes place before your core analysis. Data cleaning is not just a case of removing erroneous data, although that\u2019s often part of it. The majority of work goes into detecting rogue data and (wherever possible) correcting it.<\/p>\n

    What is rogue data?<\/h3>\n

    \u2018Rogue data\u2019 includes things like incomplete, inaccurate, irrelevant, corrupt or incorrectly formatted data. The process also involves deduplicating, or \u2018deduping\u2019. This effectively means merging or removing identical data points.<\/p>\n

    Why is it important to correct rogue data?<\/h3>\n

    The answer is straightforward enough: if you don\u2019t, they\u2019ll impact the results of your analysis.<\/p>\n

    Since data analysis is commonly used to inform business decisions, results need to be accurate. In this case, it might seem safer simply to remove rogue or incomplete data. But this poses problems, too: an incomplete dataset will also impact the results of your analysis. That\u2019s why one of the main aims of data cleaning is to keep as much of a dataset intact as possible. This helps improve the reliability of your insights.<\/p>\n

    Data cleaning is not only important for data analysis. It\u2019s also important for general business housekeeping (or \u2018data governance\u2019). The sources of big data<\/a> are dynamic and constantly changing. Regularly maintaining databases, therefore, helps you keep on top of things. This has several additional benefits, which we\u2019ll cover in the next section.<\/p>\n

    Want to try your hand at cleaning a dataset?\u00a0<\/strong>You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.<\/p>\n