Improving data quality should be one of the top priorities for business organizations.

Currently, businesses are generating an enormous amount of data with the help of various analytics tools. It has become easier to get information about customer behavior and market trends. It has become so easy that there is a danger of dealing with too much data. But while working with data, one of the most important factors to consider is its quality. Poor data quality can have a variety of consequences. Therefore, businesses need high-quality data.

Any business with a website, a social media account, and performing online transactions of any kind is collecting data about customers like user habits, behavioral patterns, likes and dislikes, their needs and preferences. All this data is filled with potential to help make better decisions, solve various problems, and improve processes.

What Happens if Businesses Use Poor-Quality Data?

Poor-quality data leads to poor decisions. A data-driven decision directly depends on the quality of information used. Critical decision-making based on low-quality data can have serious consequences. Wrong choices will portray the business as inefficient. These inefficiencies may cause very costly rework efforts to validate and fix data errors rather than focus on core duties. Most importantly, poor data quality causes mistrust. In industries, such as finance, where regulations define relationships and trade with customers, using the wrong data can cost the company its money, time, and reputation and lower customer retention.

So, How to Improve the Data Quality?

• Thorough data profiling and control over new data: In most companies, the data is received from outside sources. The data might be sent from a different organization or collected from third-party software. Hence, excellent data quality cannot be always be guaranteed. In these cases, a good data profiling tool comes in handy, where the application should be able to analyze the data format and patterns, its inconsistency in each record, data value distributions and abnormalities, and other related factors. Automating data profiling and quality alerts is also essential for the incoming data whenever it is received.

• Set data entry standards: One of the key factors before initiating to improve data quality is to set guidelines before adding them to the CRM system, or any other system used by the organization. Setting a standard for what data looks like when it enters makes a vast difference. The set of guidelines will incorporate standards on how to use the data for various decision-making practices. The specifics of the actual requirements to complete a record system will differ for each business and should be established.

• Enforcing data integrity: A crucial feature of the relational database is its capability for the enforcement of data integrity by using different techniques like foreign keys, check constraints, and results. When data grows in volume, along with various deliverables and sources, all the data cannot be stored in a single database system. In these cases, the best methods of data governance should enforce the referential integrity of the data using the suite applications and processes.

• Gathering the correct data requirements: The primary purpose of possessing good quality data is to satisfy the customers. It is not always easy to properly present the data and truly understand what the client is looking for. To clear a clear perspective on these matters data discoveries, data analysis, and clear communications channels must be used. Clear documentation of the requirements, with easy access and sharing, is another important aspect to be considered by determining data requirements.

• Prioritize and rectify common errors: No matter how much precaution is taken, minor errors will remain. Detecting and correcting them is also crucial to improve data quality. Data quality control is often done manually but can be streamlined using data profiling tools. Organizations should review their data using summary statistics to help reveal potential errors that need correction.