With digital transformation impacting the business landscape at an astonishing pace and scale, data has witnessed a massive growth both in size and diversity. In order to deal with this data flood to drive growth, organizations need to have access to the right data that is accurate and trusted and can be mined for business value. In other words, for organizations embarking on a digital journey, their data has to be accurately used to produce better business outcomes for driving inclusive growth.
Integration platforms enable organizations effectively employ data by connecting data sources to respective data targets and delivering it to their ecosystem entities such as people, applications and devices. However, if parameters like accuracy and completeness get compromised, you’ll be beating your head against the wall.
Your data integration platform should support data management and data governance while promising comprehensive data quality. Here are four ways you can build data quality into integration.
Don’t tackle it on your own
Dealing with data management on its own is an act of folly. People still recall those grandiose data management projects from the past decade that failed only because organizations attempted to handle them in isolation.
A better approach is to make data management part of a different IT approach. Suppose, when an organization is planning to replace its on-premise CRM system with Salesforce, then the IT team could take care of improving the quality of CRM data as part of data migration. This way, the project would not only include Salesforce adoption as the enterprise’s CRM but also ensure that the data present in the Salesforce is accurate as opposed to the company’s current CRM data.
Start small and deal with one type of data or data used for one operation or with one application
Avoid trying to implement data quality on all the data at once. This can prove costly and tiresome. Instead, pay attention to a single type of data like customer data (or data for one application such as Salesforce or NetSuite or Workday). Handle a part of data and you’ll be able to build data management and data governance into data integration.
Determine data management goals and establish metrics for tracking progress
Maintaining data quality isn’t as easy it seems. One needs to know what would constitute data quality for the type of data they’ve thought to manage. In case it is customer data, your objective might be to make sure it’s complete. So, identifying your data quality and management goals is necessary.
Along with that, you need to set particular metrics for capitalizing on the respective goals. You might want to know whether people are using data management tool or are they responding to queries they receive or are there any discrepancies and are they resolving them?
Setting a set of metrics will help you establish accountability for data management and quality to allow businesses drive their business forward.
Ensure workflow automation into data management
Building workflow automation into data management practices can help you resolve data discrepancies and data omissions quickly and easily. Automating workflows using integration platforms can improve the quality of data and streamline data management process. When a process flags a data discrepancy, the information is put into quarantine followed by an email which is being automatically sent to the data steward or subject matter expert. The user can take a look at the discrepancy via this email and resolve it with just a few clicks.
Final Word
You can improve your data quality and management processes with the help of four best practices mentioned above. The key to organizational success, as per these practices, have much to do with proper data planning and technology. In summary:
- Use an agile data integration approach to build data management into projects.
- Handle data one type of data first rather than the whole data.
- Employ a data integration platform that includes data management and workflow automation capabilities to establish data quality and data management.