The rise of data strategy
There is a renewed focus on what can and should be done with data, how to achieve those goals, and how to ensure data strategy alignment with business objectives. The incredible expansion of technology, cloud, and analytics—and what it means for data use—changes quickly, making it simple to fall behind if your data strategy and related processes aren’t evaluated on a regular basis.
We live in a multi-everything landscape, from multi-cloud and multi-data to multiprocess and multi-technology. However, modern data management systems are not constrained by traditional constraints such as location or data patterns. The correct data strategy and architecture enable users to self-service access to various types of data in many locations – on-premises, in any public cloud, or at the edge. The ensuing insights from technologies such as machine learning, artificial intelligence, or IoT are increasingly complex and valuable, especially when integrated into your organization’s processes and workflows.
The evolution of a multi-everything landscape and what it means for data strategy
As ecosystems altered over the last few years, increasing the chances of better data-driven results, a few primary contributing variables generated a significant shift in how you should think about your data strategy:
- The reality of hybrid multi-cloud and its rapid adoption has opened up new opportunities and difficulties. A recent Institute for Business Value (IBV) survey found that 97% of businesses had either piloted, deployed, or integrated cloud into their operations. However, not every data is suitable for the cloud. While public cloud spending is predicted to fall by 4% between 2020 and 2023, hybrid and multi-cloud spending is expected to climb by up to 17%. To eliminate silos and streamline data access, moving, managing, and integrating data in a hybrid multi-cloud ecosystem necessitates the correct data strategy, design, and governance.
- Organizations’ use of data kinds, data processing, integration, and consumption habits has increased significantly. These data kinds necessitate open platforms and adaptable data architectures to assure consistency with appropriate orchestration across environments and a strong rethinking of existing competencies and skill sets.
- The business areas require more value, and faster – the multi-everything scenario has certainly resulted in a more demanding world. Every day, new business models and alternatives powered by data and digitalization emerge in practically every industry, making competition more intense. Lines of business have greater pressure to accelerate the time to market innovative data-driven solutions, products, or enterprises. IT works with governance to control underlying risk, security, and performance without constraining flexibility. This combination of innovation and governance leads to new methods of working, such as how data and analytics solution portability has become a way to anticipate and adapt to change by offering high flexibility to run in diverse settings and avoid vendor lock-in.
5 data strategy tips for the future multi-everything landscape
When it comes to developing a data strategy, I like to take some of the fundamental characteristics of a successful business model — scalability, cost-effectiveness, and adaptability — and apply them to technology, processes, and organizations. Organizations that lack these variables generally capture only a small percentage of their data’s potential value and may even increase expenditures without gaining significant benefits.
In addition to standard data strategy considerations such as identifying data as a business asset or transitioning to a data-driven culture with multi-functional teams, here are five ideas for a data strategy that capitalizes on the multi-everything landscape:
- Prioritize data assets and accelerators: Create a data-centric process and culture that enables true standardization, re-use, portability, speed to action, and risk reduction across the whole data lifecycle. From the development of use cases to the deployment, operation, and scaling of your assets, your data strategy should be supported by the appropriate technologies and platforms to enable fully operational and scalable solutions.
- Create an enterprise-centric operational model: It is vital to have the correct operational model in place that is well-aligned with the organization’s business objectives and partnering ecosystem. This necessitates a thorough knowledge of the organization’s strengths and shortcomings. Accept best practices but avoid pure academic methods. Think broad, yet prioritize and explain realistic and achievable goals along the way, developing the correct cooperation models. Adopt and expand agile practices as quickly as possible.
- Review the scope and approach to data governance: Data governance functions, procedures, and technologies should be evaluated regularly in this multi-everything world to manage data quality, metadata, data cataloging, self-service data access, security, and compliance across your enterprise-wide data and analytics lifecycle. Extend data governance to build trust in your data by increasing transparency, minimizing bias, and providing validity for machine learning and AI-powered data and insights.
- Don’t lose the basics: To increase company results, use data in a sustainable manner and prioritize projects that are scalable, cost-effective, adaptive, and repeatable in order to offer both short-term and long-term results. In all circumstances, the data strategy should be closely aligned with your company objectives and strategy, and it should be built on a robust and well-governed data architecture. It may be tempting to go straight into advanced analytics and AI use cases with the promise of astonishing outcomes before considering every aspect of the equation, but keep in mind that there is no AI without IA.
- “Show and tell”: Make the most of established experiences, new technologies, and current assets, and don’t forget to produce results promptly. You have the tools to create, deploy, and modify your data strategy to continually deliver business results with the capabilities provided by hybrid multi-cloud environments and unique co-creation and acceleration approaches such as the IBM Garage. You may reduce risk and expedite the transition to a long-lasting, data-driven culture by demonstrating tangible results, encouraging adoption, and operationalizing at scale.
While the fundamental concepts of a data strategy stay constant, the “how” has evolved tremendously in the modern data and analytics landscape, and the most successful organizations are the most adaptable to change when evaluating data strategies. Approaches and architectural patterns such as data fabric and data mesh are becoming increasingly important thanks to enabling technologies and platforms such as hybrid multi-cloud. As you look ahead, evaluate your data strategy in light of the emerging multi-everything scenario, and prepare for change.
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