Four critical data management attributes for AI and digital transformation
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.
2. Enterprise ready
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. It’s therefore paramount for an enterprise to consider a DMS that provides availability, and has in-built disaster recovery and fail over, to mitigate the impact of disruptions.
Database performance is yet another crucial component of a DMS. Organizations need to consider attributes such as the startup time and overall uptime, as well as the speed of query execution, technological advances in querying - such as ML SQL optimization, which are especially vital. With the increased accumulation of data, a DMS’ capability to sufficiently and adequately deliver performance, provide adequate security, and be continually available are fundamental and critical considerations for an organization.
3. Modern Development
The rapid growth of digital transformation has given rise to innovation in technology, causing an increased demand on data management systems to support new complexities in the various workloads. In addition to the traditional considerations, organizations should examine whether their DMS supports:
- Popular programming languages and integrated development environments for developers.
- The building and maintenance of applications with programing interfaces
- The development of workflows by embedding ML into the DMS.
- Application development through multimodal functionality such as graph analytics in a relational database.
A robust data management strategy will establish an appropriate DMS that can bolster innovation in development. Also, as enterprises move to innovate faster, roles that previously were not involved with data management, are now engaged in leveraging a DMS in their day to day work; as an example, developers, and in some cases data scientists, utilize a DMS in carrying out their work. It is therefore imperative that a good data management strategy take into consideration a DMS’s capability to support multiple roles that are involved in modern development.
4. Consumability and resilience
Cloud infrastructure has become an integral component of the IT architecture for many enterprises. Simultaneously, enterprises continue to utilize on premises infrastructure to run their critical workloads. The emerging approach that is optimal for many enterprises is to adopt a hybrid model that integrates the two environments. An ideal data management strategy includes adopting a DMS that is easily consumable by users, yet resilient enough to support an architecture that is always required to be available and responsive. Therefore, enterprises should incorporate a DMS that is deployable and operates on both cloud and on premises environments, and more importantly, enterprises should adopt a DMS that allows for multi-vendor deployments, to avoid vendor lock-in.
A solution that excels in all four attributes
Modern day data management demands the selection of a DMS that includes these key attributes. With the newest release of version 11.5.4, Db2 keeps evolving to support organizations striving to adopt AI and support digital transformation, while operating efficiently.
Db2 11.5.4 meets all the requirements of a contemporary database. As the AI database, it enables clients to not only increase optimization and usability through AI, but broadens the use cases that can be implemented with Db2, while adhering to the data management strategy of cost-effectiveness, enterprise readiness, applicability for modern development, and easy consumability.
For a deeper look at these attributes and Db2 11.5.4, join Carl Olofson, Research Vice President, Data Management Software, IDC and Piotr Mierzejewski, Director Db2 Development, IBM for their webinar, Optimizing your data management infrastructure with Db2.