Accurate data is integral to success. When data becomes corrupted or is faulty through error, it could impact an organization’s business decisions and overall performance. As such, it’s necessary to perform data cleansing from time to time to clear away this “dirty” data. Below is a brief look into what data cleansing entails, the types of data it cleans up and why it’s such an important step in the data preparation process.
First, what is data cleansing? Also referred to as data cleaning or data scrubbing, it is the process of removing inaccurate, duplicate or corrupted data within a data set. It also entails modifying data that is incomplete or incorrectly formatted to better meet standards. Whichever method is being used to amend the data, the goal remains the same: to ensure the information is as consistent and accurate as possible. That way, analytic results are valid and the most reliable insights may be gained for organizational decision-making.
Several types of errors can be “fixed” with data cleansing. From inaccuracies as simple as spelling and syntax errors to mislabeled or empty fields, the list of amendable flaws disrupting the accuracy of the data set goes on and on. In terms of marketing, it could be removing duplicate contacts, correcting misspelled names or deleting inactive email addresses. All these instances are capable of hindering marketing and sale efforts. By eradicating this incorrect information through data cleansing, strategies could then be augmented and many operational issues avoided.
There are many other benefits of data cleansing. For instance, with more reliable data and the insights it provides, a company could make more accurate predictions. Along the same lines is increasing employee efficiency and productivity as dirty data may slow down a number of processes in a domino effect. Dirty data may even impact a company’s revenue with research showing it may contribute up to 12% of losses when left unaddressed.
Data cleaning is also important in terms of optimizing data privacy and security. In our world of pervasive data fraud, organizations of all sizes should place a strong emphasis on protecting sensitive data — both internal and external — from leaks and similar threats. Addressing this area of concern earnestly and taking similar steps to improve customer experiences has the potential to increase customer satisfaction and, therefore, the bottom line.
Benefits like these and so many more make data cleansing imperative in our modern, data-driven world. Going forward, organizations should take the various hazards of dirty data seriously and invest in the right analytics software to clean and optimize their data. For further information on data cleansing as well as a look into the steps involved, please see the accompanying resource.
