Data is the fuel that runs your organization. However to ensure a longer run, the data, just like any other engine fuel, has to be of high quality. It has to be accurate and complete.
The integration connects data sources to data targets and delivers data to applications, devices, and people. However, to maintain data accuracy and completeness, you require a robust data management and data governance approach.
Not quite long ago, many organizations embarked on data quality management projects but failed to deliver quality they promised. Fortunately, with the rise of technology, organizations can achieve better accuracy and completeness without undertaking vast, all-or-nothing data management initiatives. In fact, they can build data quality management into data integration projects as they go. Here are some ways you can build data quality into integration processes.
A better approach is to make data quality part of another IT project. For example, if a company would plan to replace on-premise CRM system with Salesforce, then their IT teams could improve the quality of CRM data as a part of data migration. Then the project would not involve not only adopting Salesforce as the company's CRM but also ensuring that the data living in Salesforce is more precise and complete than the company’s current CRM data. So, it is better to make data management part of a larger project that involves integrating or transforming data.
After the first step, the focus should be on implementing data quality for all your data at once. Handle a single type of data – such as customer data – first, and tackle the next project and the next. This way you can ensure data cleansing, resolve discrepancies, and make life easier for end-users to become easier to do business with.
After cleaning the data, you must establish end goals and metric to evaluate its completeness and accuracy. To do so, you need to ask yourself a simple question: What would constitute data quality for the type of data you’ve decided to manage. In case of customer data, it’s important to ensure that it’s complete. Hence, establishing goals is important.
In majority cases, the goals set should not only cover the data itself but also the processes that play an important role in determining data completeness and accuracy. Ergo, setting metrics for capturing your data quality goals is equally important. This helps you craft a strict governance program to tackle the issue of soiled data from the source point. Plus, the people involved in dealing with source systems should be well-versed with technology so that they can incorporate data quality management in the processes effectively.
Data changes, and so should your data management and data governance strategy. So, think of it as an ongoing work, not a short-term project that will be done once and then forgotten. For that reason, it’s best to:
Establish expectations of your team about their involvement in the process over long term.
Set organization’s expectations about timing and budget.
Choose a platform that empowers business users to manage data, without requiring them to rely on only IT or learn new technical skills or adopt cumbersome processes.
The data must be kept in a secure environment where it is allowed to be used by authorized personnel only. Meaning that authenticated users are only allowed to access or use the data during the course of integration, thus keeping data quality intact.
You can take these five steps to improve data quality without needing to make investments in arduous data management projects. To summarize:
Adopt an agile approach while building data management into data integration projects.
Involve your business users and not just the IT and let them participate in the process.
Use platforms to integrate data without compromising quality.