Four critical data management attributes for AI and digital
Many enterprises have a tangled data management system, comprised of an assortment of products assembled together, in an attempt to meet the complex needs of modern day data management. The labyrinth of convoluted data management systems often evolves as a natural response to data growth, diversity of data types, and varying needs based on business objectives. Furthermore, the adoption of artificial intelligence (AI), and the expansion of digital transformation leads to further complexity in data, an explosion in data quantity, and a heightened velocity with which insights need to be obtained.
Finding a strategy to effectively and efficiently access and leverage data easily is often challenging. The result is that organizations are unable to fully exploit their data for insights to resolve business problems. To help make it a bit easier, below are four attributes to consider when implementing an effective data management strategy.
For many IT executives, the adoption of AI and digital transformations have meant exponential increases in data, additional resources to store, manage, analyze and utilize the data, which results in an increase in associated costs. A scenario that best illustrates this outcome is an organization that handles disparate databases to support multiple data models. As enterprises evaluate their data management strategy, they should consider a multimodal database that is capable of handling mission critical applications, and simultaneously performs operations for other use cases, such as supporting key value pair models and relational data stores for document (JSON, XML), graph, and time-series models.
In addition to multimodal databases, enterprises should examine time saving factors, which in turn save costs. Some examples include selecting databases that embed Machine Learning into the query process, resulting in faster real time execution. Automated resource tuning for workloads is yet another time saving factor. Virtualization saves time and cost that would have gone toward data replication and migration. Finally, cost savings from storage functionality should round out the strategy that enterprises implement such as using compression functions that save disk space.
A survey commissioned by Cohesity found that 87 percent of senior IT decision makers believe that their organization’s secondary data is fragmented across silos, and is, or will become nearly impossible to manage long-term. This survey captures the importance of an enterprise-ready DMS. Enterprises need to deploy data management systems that transcend their data silos, all the while ensuring that appropriate security capability is built in to the DMS to safeguard data, in flight and at rest. An enterprise ready DMS’s security is also embedded into the operation and functionality of the DMS.
Closely related to security is availability. The nature of business today requires enterprises to have a DMS that supports continuous and ongoing transactions, at all times. IT downtime is not tolerated and is enough of a factor to push clients away from a vendor, given the foundational role of data in supporting digital and AI workloads.
