The importance of modern data management
In today’s data-driven world, you will hear buzzwords thrown around like MDM, big data, machine learning, data lakes, etc. But then you may think, how exactly do these concepts apply to my organization and my specific role? How can I deploy the most valuable data management processes? To answer these questions, it is important to understand what modern data management looks like at a high level, and then how are businesses are using modern data management to refine business processes and enable best practices. Understanding the concept of modern data management is the first step in deciding how to enable it in your organization.
Because of the massive amounts of data that companies today are collecting it is important to question how organizations are using this data for success. Unfortunately, not all companies are data driven and using the data they collect to benefit the business. Because of obstacles like collecting inaccurate data, or storing data in siloed locations, companies have faced a challenge in leveraging their data to take action and make business decisions.
Although companies today are still facing this challenge, the difference is the fact that there is a multi-industry push to become data-driven organizations and to be able to transform data into business insight. Because of this hyper focus, the market has been introduced to a multitude of ways to store, analyze, and act on the data they collect. For example, there are Master Data Management (MDM) solutions that have become more and more advanced over recent years. The purpose of MDM is to streamline the data sharing process across various departments of an enterprise. Exclusively using this kind of data management approach focuses on the singularity of data records, making sure that companies are only collecting, using, and working with quality data. It also emphasizes the focus on ensuring collaboration and sharing of data among a company, eliminating the issue of having siloed databases. However, by only looking at the singularity of data, then a company can neglect to see the bigger picture. On the other hand, by only analyzing the “big data” a company collects, then they are only highlighting the larger trends. This could mean they lack the ability to analyze their data on a singular level to make actionable decisions.
The key to balancing both of these industry essentials is having an all-encompassing data management tool that can feed into both of these solutions (MDM and big data analysis). This will enable an organization to collect trustworthy and actionable data that is able to be analyzed on a singular level, but can also feed into and fuel the larger goals of an organization. As an example, Experian Pandora helps to profile and discover relationships between siloed information and create business-specific data quality rules and dashboards. This helps to enable a modern data management strategy because it combines the idea of understanding your data on a granular level, to identify that singular customer view, but it also identifies tends to justify larger business goals by eliminating silos in the data. By looking at data in this way, businesses are able to take action and see positive results based on data they trust.
Today, in regards to modern data management, it is an expectation that companies utilize their data to improve revenue, strengthen reputation, or maintain regulation. Keeping in mind the current modern data management trends, it is important to be thinking about how your data management strategy stacks up. Are you enabling a modern data management strategy in your organization?
Think your data strategy could use some improvement? Unsure about where to begin? Check out our white paper, Getting ahead: Proactive data governance.