If you want to quicken your company’s digital transformation, you need to start working on your data integrity. This involves data and analytics leaders taking targeted actions to improve the quality of the data you collect and analyze before making any business-related decisions. We’ve put together some of the improvement strategies you can implement to help you achieve that.
Set and maintain standards.
Define your data quality standards as an organization and stick to them for every piece of information you capture. Standards are more or less acting as the benchmarks toward which your data should move. To set and define these standards, you and all stakeholders involved will need to set some goals concerning how you want your data to be used to grow your business. You’ll have to tap into your vision for the business and use it to guide your standard setting.
Identify data-related issues.
Once your data has been processed into usable information, it becomes metadata, which may or may not come with some errors and issues, depending on how it was analyzed. To ensure you’re always increasing your data quality, you need to keep records of all data quality problems, especially those centered on data quality and integrity.
Finding and recording these issues will require a framework, and there are two major ways to do this. The first is via creating a data literacy program, which gives you a great platform to set up a reporting system whereby end-users from all departments can report their data quality problems. The goal of a literacy program is to ensure that all data-related issues are centrally communicated, no matter their source. This level of streamlining makes it easier for data governance groups to address all issues at once.
To ensure that your data literacy program collects all issues with as much detail as possible, record things such as where the problem exists, the business value, what the problem is, and its priority, which will be taken from your customers’ viewpoints.
Prioritize your issues.
Now you know all the data quality issues, you’re ready to start working on them. The next step is to create a priority list of all the issues gathered. This list must be efficient in that it must consider how every issue affects the organization and its productivity and arrange them accordingly. Data governance managers often undertake this task, and it’s the most important task they’re responsible for.
When prioritizing issues, the data governance manager must consider the primary root cause analysis of the issues, the business value, change management, and all efforts made to fix the issue at hand. The process must also involve these governance managers and leaders from all departments in your company to prevent any bottlenecks.
Fix your prioritized issues.
There are four ways your data quality issues can be resolved:
- You can solve the issues manually by making relevant changes in the source code of your systems.
- You can develop code using an ITL pipeline, which processes data into suitable versions that meet your set standards. The code determines how to process this data by using a variety of integrations in your system, commonly known as ETL logic. This logic can take “U.S.” or “USA,” for example, and convert it to “United States,” which is your accepted data standard.
- Instead of leaving some data spaces empty for users to fill in, you can streamline their answers by giving them specific options to choose from. This involves changing a particular process in your data collection system.
- The last way to solve your issues is by managing your master data and reference data. Master data management is highly efficient but can be costly and complex to implement.
Every company collects large volumes of data from their customers daily, but very few utilize the collected data to its capacity. If you’re looking to make better data management decisions for your company, these tips should help.
