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The goals of the digital ecosystem for businesses vary and depend on their specific business objectives. For some enterprises, the goals include increasing efficiency, accelerating delivery of services, and better automation of business processes. Common data challenges include faster results for customers, improving data scalability, consolidation of database systems, and lowering costs.
In the complex data universe, data is recognized for its importance in facilitating fast business decisions. Quick data-based decisions increase efficiency and response times for customers. Data and the systems that manage data can accelerate or impede the decision process. Data management determines how fast the system can provide insights.
Scale performance is a key aspect to accelerating data analysis. The popularity of scale out data management systems has been increasing over the years. Scale out data systems address growth and performance by adding nodes to a pool. Nodes are servers that have the data management software installed. Each node adds both compute and storage resources which address both data processing and capacity requirements. Scale out data management systems provide a pathway to incremental expansion that can keep data analysis and growth costs very manageable.
Scale out systems can also enable data consolidation. Traditionally, data management systems were specialized based on the type of workload. For example, many enterprise applications workloads are transactional, meaning the target management system was a database that could accelerate online transactional processes (OLTP). Over time, this tactical approach created a digital ecosystem populated with many disparate databases – leading to sprawl. Data across the enterprise was fractured into data silos, each independent of one another.
Today’s data management systems are transforming the digital landscape by consolidating different data types and workloads to one system. This unifies data, streamlines management, and enables faster analytics. While the benefits of consolidating workloads might be powerful, the action of data consolidation could be fraught with difficulties including rapid ingest of data. Thus, when consolidating a database management system, it must have the capability to quickly and easily ingest large volumes of data.
The ability to unify data and reduce the underlying systems to support multiple workloads must include migrating all the reports and data analytics code from the source systems. Depending upon the data consolidation system, the path of least resistance would be supporting that code in its native format, not requiring changes to it. This provides the fastest path to productivity.
Data management systems are an investment for businesses. Optimizing total cost of ownership (TCO) should be part of their development. Specifying the right infrastructure for transforming your data during the design phase is critical to efficient operations. Dell Technologies has a validated multi-workload solution with SingleStore that provides businesses with a new generation data platform capable of data acceleration and massive scale while optimizing the TCO.