Navigating the Future Landscape of Data Management

Data management trends are constantly changing, and staying ahead of the curve is important. This article explores the recent trends in data management and what to expect in the future.

Fremont, CA: Developing a comprehensive data framework that can be accessed remotely, on-site, in the cloud, or from a data center revolutionizes data management. This data, whether organized or unorganized, must flow seamlessly across cloud, on-premises, and remote platforms while ensuring security. Additionally, it should be accessible to those who need it and restricted from others.

According to experts, in two years, 175 zettabytes of data will be generated globally, primarily from Internet of Things (IoT) devices. Enterprise resource planning (ERP) and other mission-critical enterprise systems have long been powered by system of record (SOR) databases, which may not be compatible with large amounts of data businesses of all sizes may encounter.

Many of the same guidelines that apply to organized SOR data should also apply to unstructured data. For instance, if the organization is to rely on unstructured data, it needs to be protected with the highest standards of data integrity and dependability. It must also be able to freely transition between cloud-based systems and apps, corporate data repositories, and mobile storage and comply with regulatory and internal governance norms.

Automation and software-based solutions need to be integrated into data management processes to meet the immense demands of managing large volumes of varied, high-velocity data daily. The significance of more recent automation technologies, such as data observability, will only increase as user citizen development and the utilization of localized data grow.

While developing its data management roadmap, enterprise IT must consider each of these forces carefully. Following are the seven emerging trends in data management:

Hybrid End-To-End Data Management Frameworks:

Businesses should anticipate receiving enormous volumes of structured and unstructured data from various sources, such as external cloud providers, Internet of Things gadgets, robots, drones, RF readers, MRI or CNC machines, internal SOR systems, and remote workers using smartphones and notepads. This entire data collection may be stored for a long or short period in an on-site data center, the cloud, or on a distributed or mobile server platform. Real-time data may need to be watched over and accessed while streaming.

Data managers will want data management and security software that can span all of these hybrid activities and uses because the data, its uses, and its users are diverse in this hybrid environment. This will ensure that data is safely and securely delivered and stored point-to-point.

The Consolidation of Data Observability Tools:

Observability—the capacity to track data and events across multiple platforms and system barriers with software—is a major focus for enterprises looking to monitor end-to-end movements of data and applications, as many applications today use numerous cloud and on-premises platforms to access and process data. Most enterprises that use observability tools today have a problem with end-to-end data and application visibility across platforms being affected by too many distinct tools.

Master Data Management for Legacy Systems:

Businesses have difficulty deciding how to deal with outdated technologies when they adopt new ones. However, some of those are still valuable as legacy systems, meaning they are out-of-date or still perform essential mission-critical tasks for the company.

For managing data on their cloud or on-premises solutions, some of these legacy systems—such as enterprise resource planning (ERP) systems like SAP or Oracle—offer complete, integrated master data management (MDM) toolkits. Businesses utilizing these systems are adopting and implementing these MDM toolkits more frequently as a component of their overall data governance plans.

MDM tools provide easy-to-use methods for importing data from external sources and managing system data. No matter where the data is stored, MDM software offers a single view, and IT establishes the MDM business standards for data protection, consistency, quality, and governance.

These are some of the data management trends. Apart from these mentioned trends, there are many others that may include automating data preparation, prioritizing data security, and data management using AI and ML.