Companies that rely on data to keep their business up and running should have in mind that ensuring data quality and data matching is what will keep their data accurate and reliable. This way, the data analysis for the strategic decisions will be assertive. So having good data quality practices in place will ensure error-free data.
Bad data has a big cost that can add up very fast. Most companies can’t afford to have errors on their data records – high-quality decision making may depend on this – and especially if the data is being generated and managed by people, which means that the data is prone to human error.
The quality and matching practices will keep the data on the right track and keep the critical errors away.
What Is Data Matching?
Data matching, along with the data quality practices, will help in the process of having only good data. Data matching is the process of comparing and matching data from two different sources or sets of data to see if they are duplicated or are coming from the same entity.
This process will sort the data according to what it’s needed, will match information, standardize the data the company already has and summarize everything. Data matching will keep the data accurate and not duplicated so the people looking at it can make high-quality analyses, better strategic decisions that will lead to growth and drive revenue.
The importance of data matching and the need of doing it with the data quality practices is to avoid errors and duplicated content.
The Best Data Quality Practices
Having good data quality practices will maximize the trust of those that need the data on a daily basis inside the company, keep the data accurate and up-to-date and formatted in the same way across all sources.
1. Have Established Metrics in Place
Before assessing the companies’ capacity of improving the quality of the data that is already recorded, it’s important to have metrics to measure how the quality is now and how much it’s improving.
Those metrics will measure the goals and all the business targets that need to be achieved and can include all the errors found, which data is incomplete, unformatted or redundant.
The importance of having these exact data metrics measured is to keep track of how standardized and accurate the data is, taking actions to improve the data quality and having the amount of data with errors or inconsistencies that need to be fixed.
2. Inspect Data Quality Problems
Sometimes, even the data quality practices and the data itself will face problems, it happens. But the important action to take here is to investigate the causes and the possible reasons to stop those errors from happening again.
When talking about data quality, if problems happen, the company cannot fix it without trying to address the cause of it in the first place and inspect in detail what happened– an error should only happen once, and then it needs to be fixed on the source.
3. Create Guidelines and Consistent Procedures
The data guidelines and procedures should be like data governance or the standard practices to keep consistency when inputting, storing, extracting and analyzing the data. This governance should be documented with clear steps for everyone to follow.
Everyone in the company should engage in the procedures and guidelines and be aware that following the practices is what will ensure high-quality data.
These guidelines need to be established by every company, but there’s not a single list of procedures that should be followed by all the companies. It’s something they must build according to their culture, structure, internal processes and people involved.
4. Make Data Auditing Part of the Routine
But before even inspecting the errors that the data quality practices can face, the best way to actively avoid them is by doing assurance audits on the processes to find out before if problems and errors can occur and fix them before it’s too late.
After the quality practices are implemented, they need to have good maintenance to ensure that they are working as expected and keep the trust in data quality high.
Auditing should become part of the routine as an ongoing process with automated systems and software programs, so no need to have manual work on this.
Those audits should look for incomplete data, any missing fields or fields populated incorrectly, duplicated data – that can be done with data matching – and entries of data that are no longer valid or needed.
Keeping a good frequency of audits will catch errors and problems right at the beginning, so it gets easier and faster to fix them. Audits done once every semester or once a year will give too much time for those errors to exist, and it’ll get harder to find, correct, investigate the sources and create ways to avoid them.
