As businesses embrace the use of data-driven processes to transform virtually every aspect of their operations, the integrity of that data and the data management infrastructure have come under closer scrutiny. Data is more abundant for businesses than ever before, but organizations are facing greater limitations in regard to data quality.
According to Gartner, the average enterprise organization believes that poor data quality cost their company roughly $11.8 million in 2018 alone. Effective data management is one way to implement procedures and infrastructure that improve data quality, but this business function isn’t without potential pitfalls and challenges of its own.
Here’s a look at some of the common challenges your own data management project may face—and how to take corrective action.
If the data maintained in your data management system isn’t reliable, your overall data management is doomed from the start. Data can be filled with inaccuracies or duplicate information, or it can be structured in a way that your management system isn’t set up to properly manage. A lack of standardization of data can also make it difficult to consolidate data from different sources, which hurts your overall dataset.
What you can do: Use a business rules and workflow solution to implement validation processes that clean data before it’s used in any business processes. Forms and addresses can be validated through data verification tools after they’ve been supplied by a user. Install an AI-powered solution that identifies duplicate or inconsistent data, as well as other data anomalies, and either resolves these inconsistencies or flags them for human review.
Effective data management requires a master set of data that functions as a single source of truth. When your data infrastructure lacks this feature, it can affect strategic decision-making and other data-driven functions of your business, because different departments and leaders may reference different versions of data that tell different stories. The result is poor strategic alignment that counteracts the goals of company-wide data management.
What you can do: Establish a master system of record or centralized data storage location that organizes and protects data after it has gone through validation and cleaning processes.
Automated workflows can support data management by streamlining data processing and other tasks related to maintenance and management. Without constant updating to your company’s dataset, the system of record will always offer lagging data insights to guide decision-making.
What you can do: Implement a solution that processes data automatically and in real time rather than relying on batch processing that creates data bottlenecks and degrades the accuracy, relevance, and value of your data.
When data is input into your data management system, it often comes with a structure—or lack thereof—that differs from how you categorize and manage this data internally. This data needs to be accurately transferred into a new management structure before you can use it to coordinate decisions and other tasks.
What you can do: Use a visual data mapping tool to create an easy template for manipulating gathered data and transforming it into a more useful structure. No-code development tools can help you quickly create a mapping solution that takes data gathered through forms and other sources and adds it to the relevant fields in a database.
Data management depends on strong lines of communication between your central management solution and the different technology inputting this data. The best way to support fluid, efficient data-sharing between these different solutions is to implement a software solution that integrates seamlessly and offers an easy platform for setting up rules to manage this data.
Eager to see how Decisions supports your data management projects? Schedule a demo today.