In today’s digital world, more data gets generated in day-to-day business transactions. This data helps greatly in making most business decisions, but the data will only be helpful; it’s of high quality. Minding the quality of your data also protects it from cyber-attacks. The big question is, what is data quality? Data quality is that kind of data that can positively and effectively influence major decisions in the business. The major attributes of such data include accuracy, consistency, orderliness, completeness, timelessness, auditability, and uniqueness.
To maintain high-quality data, every business needs data quality management (DQM), which is a set of practices that will help to maintain high-quality data. DQM applies from the phase of acquiring data to the processing of data, to usage of data management software and also to distribution. Therefore DMQ is essential and to ensure you maintain the high quality of data, use the following tips to improve data quality management.
• Have some defined rules for data quality
For you to have data with high-quality attributes, you will need defined rules to help you achieve this goal properly. There is no perfect way to do this, and since every business has different information and different transactions, you will need to customize your data fields based on your operations and then set rules for each. Decide on which data is more critical than the other and set the attribute you would wish to capture on the same. For instance, should you capture customers’ full name or simply two names? Must the date of birth be included, if yes, which is the best order for it? When setting these rules, remember the attributes that help in maintaining high-quality data and what threshold suits what attribute.
• Evaluate the quality of your data
After making the rules of your data quality, make a point of evaluating if your data meets each one of them as stipulated. To assess the quality of this data, you begin by profiling it, whereby you pick about ten records and check the rules you set for each data. If you had said that the name of the customer must be full names and N/A cannot be accepted, then ensure this rule in adhered to. If you had given the order for date of birth to be a date, then month and then year or month, then date and then a year, then this is how the ten records should read. Check against all the rules to ensure the threshold you set for all attributes of the data is met.
• Provide a solution for any issue identified
If when assessing your data, you find some issues or discrepancies based on the rules you have set, you will need to come up with solutions for the issues. You will have to think critically when formulating a solution. First of all, identify the root cause of the problem so as to be able to resolve it. If you encounter a problem with the date of birth, the employees keying in data into the CRM need to be keener and use any other databases available to confirm information acquired. It is also more effective if the business can add a validation rule in the system so that the only data that gets accepted is one that complies with the rules set. Do not continue to feed the system with information if you have already identified a problem.
• Data control
The quality of your data needs to be maintained all through, and this means that DQM is not a one-time thing but a continuous process. Even after identifying solutions to all the issues you could have identified, keep on reviewing your policies and rules to ensure that your goals for your data are met and standards maintained. Since the business environment keeps on changing, avoid being rigid with your rules, and always yearn to do things better when it comes to data management.
Were you wondering, what is data quality or quality data management? You should not wonder anymore; neither should you get stuck when using DQM. These tips will help you to improve the quality of data using DQM.
