Data is the fuel that drives the IT world. Companies of all shapes and sizes rely heavily on data to achieve their business and operational targets. While data is one of the most essential assets to the IT world, very few measures are being taken to get the most out of the data. Having clean and actionable data enables companies to make confident business decisions and to achieve their business goals. Data cleaning, as the name suggests, is a method of cleansing data to make it even more valuable and actionable. For data to be valuable and clean, there are several criteria that need to be met. But before any data can be cleaned, it needs to be accurate, valid, complete, consistent, uniform, and unambiguous.
Here are some of the best practices of data cleaning that need to be adopted by companies for more valuable data.
Data auditing means evaluating and assessing the data and its quality for a specific purpose. Data auditing provides a number of advantages to organizations and ensures end-to-end integrity of data activities.
Data auditing can also help in detecting and analyzing data breaches. Data auditing serves as an easy and cost-effective measure of data cleaning and can save the data from human errors, compliance, storage-related hassles, and can also be used in the customer service domain.
Avoid data duplication
Duplicate data doesn’t just consume storage space, it also leads to multiple other issues. Manual or configurational errors lead to duplicate data and duplicate data leads to expensive mistakes.
Implementing data deduplication is one of the effective ways of dealing with duplicate data. Data deduplication, as the name suggests, refers to a method of eliminating data duplication or redundancy. In data deduplication, extra copies of the exact same data are deleted, which results in only unique data being stored in databases. These duplicate data entries that are deleted are replaced with a reference to point to the original data entry. Deduplication is an effective means of dealing with duplicated data entries, without actually creating voids in the databases. The reference points can be used to map to the original data for future references, which avoids data linking issues.
Data deduplication also helps to reduce the load on the central database and the server and also reduces bandwidth requirements. Some other advantages of data deduplication are faster recovery, efficient storage volume replication, and reduced maintenance costs.
Have a data quality plan
Every company needs to set certain expectations for the data — and, of course, every company’s expectations will be different. Organizations should dedicate resources to actually plan and develop a personal data quality plan. This can be achieved by implementing key performance indicators (KPIs), setting milestones, and time constraints by which these milestones need to be achieved.
Every company deals with specific data and it is the company’s responsibility to set the metrics accordingly. Having a data quality plan of your own will help to analyze where most data errors occur. It also helps to identify misleading or incorrect data and the root cause of the issues. The data quality plan will also ensure the health of the data, which is very important to extract any valuable insights from the data.
Validate the accuracy of data
Not validating data is one of the fatal mistakes companies tend to make. No matter how many data cleaning measures are taken, without proper validation of the final data, one can never be sure that the data is clean.
Data analysis can be made to the fullest only when quality data is being used to analyze and extract information. Therefore, data validation is a very important aspect because it ensures that accuracy and reliability are maintained across the data being processed. Following an effective and efficient data validation involves performing data tracking, certifying the data, managing the workflows, and checking for the consistency.
Monitor and keep a log of errors
Logs are an absolute essential in every domain of the IT industry. Having data logs help companies and organizations have a structured approach to deal with faults or errors. Today, even a small organization processes huge data every day. And naturally, there is always a possibility that these huge amounts of data processing results in errors. For the data to be valid and clean, these errors need to be properly monitored and stored in logs.
Having errors logged will enable a company to deal with similar kinds of errors and issues in the future. These logs can be weaponized to deal with data manipulations, corruption, and other forms of cyberattacks that affect data. Although monitoring and having a log of errors is not a direct measure of data cleaning, it is still a vital enabler of the data cleaning process.
Data cleaning: Good hygiene keeps your data usable
While it can be impossible to completely eliminate all data quality-related issues, they can be minimized to a good extent by following these methods. There are several other methods of ensuring better and cleaner data. Data appending, avoiding compliance issues, ensuring a single customer view, and having data integrity are equally vital in data cleaning.