Around the world, data serves as an essential resource for companies to utilise throughout their organisation. From tasks such as economic forecasting to website optimisation, data can provide a vast array of insights and advantages that can cater to any company’s needs. But to extract such insights, what you need is “good” data.
Data ranges significantly in its quality, and this is significant because the quality of your data directly corresponds to the quality of insights your company will be able to extract. In simple terms, bad data leads to bad insights. Thus, in this blog, we will take a look at a process known as data cleansing, which aims to transform unusable data into quality data that your company can use.
What is data cleansing?
Before going into the specifics of data cleansing, however, it is important to distinguish between good and bad data. After all, if your company never realises that your data is of subpar quality, nothing would be done to address the issue. Bad data is data that is corrupted, poorly formatted, possesses duplicates, incomplete, or incorrect in some form or another. Good data, on the other hand, lack these qualities.
So what is data cleansing then? Data cleansing is the process of fixing or removing bad quality data into useful and actionable data. If a portion of data is missing or a duplicate exists, data cleansing aims to resolve these issues. But how does bad data even come about in the first place? Many companies nowadays are pursuing the practice of data integration, which in short, is the process of consolidating data from multiple sources into one location to gain a more descriptive display of the data they have collected. If you would like to know more about this topic, please take a look at our prior blogs about data integration. Anyhow, consolidating data into one location can often allow for the possibility of mislabeling or duplication to occur. This ultimately compromises the accuracy of your data and can lead to incorrect insights, poor decisions, and more.
However, while data cleansing may seem like a needy and fairly easy task, there is one challenge that has discouraged many companies from even considering the process. That challenge is time. According to a study done by Forrester Research, 80% of a data analyst’s time is spent on data cleansing and preparation. With such a lengthy amount of time spent on this one process, other more important tasks such as scanning the data for insights are compromised. Because of this reason, it is understandable why many companies choose to ignore this process. Still, for every company, it is important to establish some cleansing routine to ensure that your data maintains its integrity. One of the ways that can be achieved is via data automation.
How to perform data cleansing
As we mentioned previously, the specific steps for this process vary from company to company, but there are some basic steps outlined below you can use as a foundation.
- Search for and remove duplicate observations
This step should occur during the data collection process. When consolidating data from more than one location, duplications may occur. By eliminating such duplications, the amount of unnecessary data storage is reduced, customer service becomes more effective, negative targeting implications are reduced, and more.
- Correct structural errors
In addition to instances of data duplication that may occur during the collection process, structural errors such as misspellings, mislabelling, or incorrect naming conventions may occur. Though not as important a step as the others, by addressing this issue, you increase the consistency of your data and the thoroughness of the cleansing process. During this step, you also try to examine your data for any potential outliers or improper data entries.
- Address any missing data
This step is arguably one of the most important steps in the data cleansing process. When missing portions of data are detected, the function and utility of your company’s algorithms are compromised as most algorithms are unable to accept empty values. Though both options are not ideal due to their potential to compromise the integrity of your data, you can either fill in the missing data values via extrapolation or remove the missing entries from your data set entirely.
Conclusion
While the time required to complete this process may seem daunting or discouraging, it is still important to consider the merits of data cleansing. If you feel that the data your company has collected requires maintenance and that the benefits outweigh the lengthy process required to complete this task, then data cleansing is certainly something you should consider. On the other hand, companies who can manage sufficiently with the current state of their data should seek other less extensive solutions that can still offer some sort of benefit to their data quality. Nevertheless, Ei Square still recommends establishing a manageable routine revolving around all of your data, including data cleansing, data collection, data insights and reporting, and others.
About the author: Mark Roychowdhury is a Copywriter Intern at ei² niche consulting for #data #insights #performance www.eisquare.co.uk