Why is Data Cleaning important?

 With most industries relying on data today for their business growth, especially data-intensive industries like banking, insurance, retail, telecoms among others, managing data to be error-free becomes important. It is known that one way of achieving maximum efficiency is to reduce all kinds of data errors and inconsistencies. If the company aims to optimize its working and increase their profits by using data, then data quality is of utmost importance. Old and inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems.

These problems can be solved by using various data cleaning techniques. Data cleaning is a process used to determine inaccurate, incomplete, or unreasonable data and then improve quality by correcting detected errors and omissions.

What are the benefits?

Since data is a major asset in many companies, inaccurate data can be dangerous. Incorrect data can reduce marketing effectiveness, thereby bringing down sales and efficiency. If the organization had clean data, then falling into such situations can be avoided. And data cleaning is the way to go. It removes major errors and inconsistencies that are inevitable when multiple sources of data are getting pulled into one dataset. Using tools to clean up data will make everyone more efficient. Fewer errors mean happier customers and fewer frustrated employees. Increased productivity and better decisions are other benefits of using data cleaning.

What are some common errors that could happen while dealing with data?

Some of the most common mistakes that occur in structured data are missing fields. Such errors can be fixed using tools like Google’s Structured Data Testing tool. This tool gives you a list detailing all of the errors, along with detailed information on the structured data Google currently detects on your website. Omissions, data that is duplicate, inaccurate or incorrect data can create expensive interruptions.If it is believed that any event does not represent a normal outcome, it needs to be filtered out from the analysis. Comparing different sets of population, segment or cluster can also result in data inconsistencies. So does drawing inferences on thin data. Another mistake that can happen is when wrong applications of the inferences are accepted. Data cleaning is an important aspect of data management which cannot be ignored. Once the data cleaning process is completed, the company can confidently move forward and use the data for deep, operational insights.

How to go about the process of data cleaning?

The manual part of the process is what can make data cleaning an overwhelming task. While much of data cleaning can be done by software, it must be monitored and inconsistencies reviewed.

Some general guidelines that all companies can follow to data clean include forming a data quality plan. By standardizing the data process, one will ensure a good point of entry and reduce the risk of duplication. Monitoring errors and fixing the data at the source can save both time and resources. Investing in tools that measure data accuracy is another wise way that can be adopted.  A reliable third-party source can capture information directly from first-party sites. It would then clean and compile the data to provide more complete information for business intelligence and analytics.

Certain data cleaning tools helps in keeping the data clean and consistent to let you analyze data to make informed decision visually and statistically. Few of such tools are free, while others may be priced with a free trial available on their website.  OpenRefine, formerly known as Google Refine is a free and open-source data cleansing tool. It cleans inaccurate data and transforms it. It can also transform data from one format to another, letting you explore big data sets with ease, reconcile and match data, clean and transform at a faster pace. Trifacta Wrangler is another free tool that cleans and transforms data. It takes less time formatting and focuses on analyzing data. It’s machine learning algorithms help in preparing data by suggesting common transformations and aggregations. Other tools include Drake, TIBCO Clarity, Winpure, Data Ladder, and Cloudingo, among others.