In a world that is increasingly powered by computationally-intensive information services, data is everywhere. The last decade has seen the quantity of data across the globe increase more than ten-fold, and the trend is only set to grow further with global spending on data-driven digital transformation expected to touch an astounding USD 6.8 trillion by 2023 . As enterprises embark on their digital transformation (DX) journeys, it is critical that they are guided by a well-rounded sustainable and secure data strategy. Future-readiness starts with identifying and unlocking the potential of insightful data.
Big data fuelled DX 1.0, but the unorganized data spaghetti that came with the phase impacted speed and created a data disconnect, resulting in 70-80% of projects falling short of their digital ambitions. DX 2.0, on the other hand, is digital-first, powered by AI, cloud and 5G technology, and it’s set to upturn business performance. DX 2.0 also relies on MLOps, which will accelerate initiatives that can better manage and extract value from data. And a sustainable data strategy is the key element to transform that is business-focussed, agile, and scalable.
A sustainable data strategy focuses on good data rather than big data. With good data at the nucleus of DX efforts, a clear data strategy can support growth catalysis within the business and among its employees, empowering customers and other stakeholders. A well-devised data strategy is all about bridging the gap between data that is critical to business decisions and its inclusiveness in the digital roadmap. It comprises a large collective of data-centric initiatives, requiring continued commitment for scalability:
By setting a strong data direction, enterprises can recognize and prioritize initiatives and use cases that can help them realize optimal growth. Having direction creates the roadmap to identifying those data sources that are likely to be most valuable. It also defines specific functional areas within the enterprise where data integration can generate the highest impact.
Establishing an enterprise-wide data pivot to securely manage and govern data in alignment with the overall business operations can democratize data and analytics (D&A) across the business, fostering a sustainable data strategy.
Enterprise performance is highly reliant on stable DataOps that not only identify business demand but also work towards continual enhancements. Assessing and charting data repositories and data flow are as important as understanding and simplifying the data environment. Appropriately designing a target operating model can provide the impetus for a digital platform mindset that is decisive for strategic scaling.
However, before enterprises chalk out their data strategies to glean business-critical insights from their data, it is essential to evaluate their data maturity and security. How competently, and how much, an enterprise is capable of leveraging the value of its enormous data can indicate how deeply data is ingrained in its digital decisioning and business empowerment practices. A holistic evaluation of this composite web of dimensions is a reliable indicator of where an enterprise is in its DX journey.
Data Lifecyle: Data-mature enterprises create significant efficiencies throughout the data lifecycle, from sourcing and aggregating, all the way to distributing and controlling, even resulting in overall cost savings as high as 30-40% for an enterprise. One of the most effective ways to reduce power consumption is storage tiering. While mission-critical and hot data are always stored at the top two tiers for quick access to high-speed applications, warm as well as cold data are older and can be moved to low-cost storage for infrequent access.
Data Security: Data security is a combination of procedures, policies, protocols, and sometimes technology, that is implemented and followed enterprise-wide. It goes well beyond simple file permissions, folder ACLs, or storage protocols, because these protocols are not completely secure and can be circumvented. While primary storage systems must be open and available to client systems, your backup data should be isolated and immutable.
Data Management Processes: Data managed as an enterprise asset, bound by data strategies that are connected to larger business goals and aligned with an expandable data platform, set the stage for efficient deployment. Enterprises that display high data maturity are those backed by stable data governance, intelligent decision support, continuous business evolution strategies, and talent management with cross-functional engagement.
Analytics and AI: For an enterprise at an advanced level of data maturity, data can be automatically consolidated, integrated, analyzed and interpreted using predictive analytics solutions and AI on a single analytics platform. An advanced data-mature enterprise empowers a simpler and faster analysis of data at various stages of its continuance through connected things and technology architecture.
Ecosystem: Most data-mature enterprises are able to instantaneously bring together insights from varied data types and sources from a distributed, yet hyperconnected ecosystem. This could include large amounts of data integration from partners, customers, the government and other organizations. In fact, secure data coopetition strategies can be a big boost to innovation.
Operations: A high level of operational management maturity is demonstrated in those enterprises that use data analysis for optimizing cross-enterprise processes, including Agile management, governance and resource automation, vendor management and intelligent service and process management. A worldwide survey of data management leaders revealed 98% of respondents agree that effective data strategies helped drive value for their businesses, with 64% reporting a reduction in the cost of operations by 3.31% across a 12-year assessment.
While DX enters an increasing number of enterprises, 80-85% still run into last mile problems with their deployments, owing to their failure in realizing the full value of their data. Those enterprises that are data-mature will be able to foresee and fix strategic breaches in their transformational frameworks to achieve their digital ambitions. Digital strategies are unique to each enterprise, based on their dynamic data structure and high volumes of data, but best practices are useful. Piping all data into a central cloud-based location can help speed up seamless analysis. A shift from the data lake to the data mesh approach, allowing for data to be treated as a product that is more interoperable, can also ensure more intelligible data consumption. Simply put, if enterprises are diligently focused on the data at the heart of their digital transformation goal, there is no stopping them.