Emerging technologies are transforming organizations of all sizes, but with the seemingly endless possibilities they bring, they also come with new challenges surrounding data management that IT departments must solve.
Technologies such as AI, cloud computing, and the Internet of Things (IoT), require the right infrastructure to support moving data securely across environments. This means if an enterprise wants to leverage these and other new technologies, it must incorporate strong data management practices to know where data is, and whether it should move into a cloud setting or stay in the mainframe—a task that presents new, unique, challenges.
Let’s look at some of the most common obstacles teams face in ensuring secure data management and where advanced data discovery and integration solutions come into play, offering a strategic edge by enhancing visibility, security, and cost-effectiveness.
Data discovery
To leverage modern technologies and properly allocate data to the right locations, IT teams need a good understanding of where their data lives and where it’s moving to. This is why data discovery and data transparency are so important. Unfortunately, data discovery within mainframe systems can be extremely challenging without the right modern solutions to enhance visibility.
Identifying what kind of data exists and determining where it’s going becomes far more challenging when introducing new endpoints for data to travel to in an environment. Without this ability, an organization could end up moving sensitive information to an unsecured location or providing access to people who should not have it.
IT teams need to capture metadata to know where their data comes from, allowing them to map out its lineage and flow. And since data does not exist in a vacuum, it’s critical not to treat data sets as lump sums. Doing so would be a deficiency in processes, making it more difficult for business operations to find and work with the most relevant data.
Modern tools with user-friendly interfaces and automation capabilities can simplify data discovery and help IT teams get a fuller picture of their data. This limits both time and cost while increasing productivity, allowing employees to make stronger analytical decisions.
Outdated integrations
Modern data integration approaches can save IT teams a good deal of money and frustration while providing greater security and improved agility.
Often organizations struggle with data replication, synchronization, and performance. They find they have limited bandwidth and an inability to perform multiple replications for a variety of data sets both in mainframes and the cloud. These issues add up and lead to unreliability.
However, enterprises with integration solutions that coexist with native IT architecture have scalable data capture and synchronization abilities. They also reduce storage and maintenance costs while integrating seamlessly with cloud platforms to simplify data management.
Ultimately, a modern approach to data integration allows enterprises to reduce costs and drive innovation by bringing data to where it’s needed – including the cloud.
Rising storage costs
Both the need and cost of enterprise data storage are growing exponentially, which makes data management and compliance more difficult. The key is to keep data on mainframes while taking advantage of the ability to move non-sensitive data through the cloud.
In an era where data is a cornerstone of business operations, leveraging modern data discovery and integration tools is no longer optional—it’s essential. By adopting advanced solutions, enterprises can achieve comprehensive data visibility, streamline integration processes, and manage growing storage costs effectively. This not only ensures robust security and compliance but also drives innovation and agility, positioning organizations for sustained success in a data-driven world.
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