Accurate data is the key to success. When data becomes corrupted or faulty, it can impact business decisions and an organization's overall performance. Hence, it’s necessary to clean the data from time to time to remove this “dirty” data. Below is a brief overview of what data cleaning entails, what types of data are cleaned, and why it is such an important step in the data preparation process.
What is data cleansing?
Also known as data scrubbing or sanitization, data cleansing is the process of removing inaccurate, duplicate or corrupt data from a data set. It also involves editing incomplete or poorly formatted data to better meet standards. Regardless of the method by which the data is modified, the goal remains the same: to ensure that the information is as consistent and accurate as possible. This way analysis results count, and you get the most reliable information for your company's decision making.
Various types of errors can be "corrected" by deleting the data. From simple inaccuracies like spelling and syntax errors to mislabeled or blank fields, the list of correctable errors that affect the accuracy of a record is long. From a marketing perspective, this can include removing duplicate contacts, correcting misspelled names or deleting inactive email addresses. All these cases can hamper marketing and sales activities. By eliminating this misinformation through data cleaning, strategies can be refined and many operational problems avoided.
What are the benefits of data cleansing?
Data cleansing has many benefits. For example, with more reliable data and the insights gained from it, a company can make more accurate predictions. At the same time, employee efficiency and productivity increase since dirty data can slow down many processes in a domino effect. Dirty data can even impact a company's revenue, with studies showing it can account for 12% of losses if left unaddressed.
Data cleansing is also important from the point of view of optimizing data protection and data security. In our world of pervasive data fraud, organizations of all sizes must make protecting sensitive data, both internal and external, from security breaches and similar threats a high priority. If you take this issue seriously and take similar steps to improve customer experience, you have the potential to increase customer satisfaction and, in turn, your bottom line.
Benefits like these and others make data cleansing a necessity in our modern data-driven world. Going forward, businesses should take the various dirty data threats seriously and invest in the right analytics software to clean and optimize their data.
See the following resource, by Association Analytics, for more information on how to cleanse data and the steps to follow.