Data continuously evolving at a rapid pace is generating data management complexity. As it can get in the way of digital transformation initiatives of an organization, CIOs should strive to identify ways to effectively tackle data management complexity.
Enterprises are continuously taking initiatives to innovate their tech stack. With the acceleration of the digital transformation journey, CIOs across the enterprise landscape are constantly finding ways to make the business operations more efficient, increase productivity while enabling their organizations to increase their ROI. However, the disparity between where the organizations are in their transformation journeys and where they wish to go is increasingly growing. This is due to the complexity which spans processes, people, and technology deeply embedded in data and how data infrastructure is managed. According to ESG’s “Why Data Management Complexity is Inhibiting Innovation,” 93% of IT decision-makers think storage and data management complexity is affecting their digital transformation.
While complexity is nothing new for organizations, in today’s competitive times, it is what affects the position in the marketplace. For IT departments, the data complexity comes with a tradeoff of resilience, efficiency, and performance in favor of countless hours spent on tuning, maintaining and upgrading storage fleets. Moreover, it also makes it difficult for data innovators to seamlessly get access to the data. Hence, it is critical for CIOs to rethink their data and infrastructure management approach and its overall impact across the enterprise and its customers.
They should embrace data, cloud, and AI to build a new data experience. Here are a few ways that CIOs can tackle their data management challenges:
Building data-centric policies and automation
Since data has a continuous lifecycle spanning production, test/dev, production, it should be managed from creation all the way to the deletion process. Opting for software that can manage individual parts of the lifecycle is inefficient while creating visibility gaps. Therefore, CIOs should apply holistic data-centric policies and automation that eliminates silos while unifying workflows across the data lifecycle. They should ensure policies that manage how data is stored, protected, accessed and mobilized are automated and data-centric.
Leveraging cloud operational model
The cloud environment has set the standard for agility. Its operational model allows line of business (LOB) owners and business users to build and deploy the latest applications, projects, and services at a rapid pace. This results in the underlying data infrastructure becoming invisible and shifts the operations to be app and not infrastructure-centric. Additionally, CIOs should leverage the cloud operational experience wherever possible that their data and applications workloads live, from edge to cloud.
Harnessing AI-driven insights and intelligence
To truly get the most out of their digital transformation initiatives, CIOs should integrate AI deeply into their data operations. This will enable them to make network setting changes, improve their app performance while balancing workloads and resources in a specific way. Additionally, AI will enable CIOs to provision applications across their entire fleet without going through intensive planning or calculations.
The new paradigm shift towards data will enable CIOs to transform the data experience across the organizations. This will allow them to create value for everyone, including IT managers to data innovators.