The rise of data strategy
There’s a renewed interest in reflecting on what can and should be done with data, how to accomplish those goals and how to check for data strategy alignment with business objectives. The amazing evolution of technology, cloud and analytics—and what it means for data use — changes quickly, which makes it easy to fall behind if your data strategy and related processes aren’t frequently revisited.
From multicloud and multidata to multiprocess and multitechnology, we live in a multi-everything landscape. Luckily, today’s data management approaches aren’t limited by traditional constraints like location or data patterns. The right data strategy and architecture allows users to access different types of data in different places — on-premises, on any public cloud or at the edge — in a self-service manner. With technologies like machine learning, artificial intelligence or IoT, the resulting insights are more sophisticated and valuable, especially when woven into your organization’s processes and workflows.
The evolution of a multi-everything landscape, and what that means for data strategy
As ecosystems transformed over the last few years and simultaneously increased the opportunities to improve results driven by data, a few main contributing factors drove major change in how you should think about your data strategy:
- The reality of hybrid multicloud and its accelerated adoption has created new possibilities and challenges. According to a recent Institute for Business Value (IBV) study, 97% of enterprises have either piloted, implemented or integrated cloud into their operations. But not all data is best suited for the cloud. While the share of IT spend dedicated to public cloud is expected to decline by 4% between 2020 and 2023, hybrid and multicloud spend is expected to increase up to 17%. Moving, managing and integrating data in a hybrid multicloud ecosystem requires the right data strategy, design and governance to eliminate silos and streamline data access.
- The diversity of data types, data processing, integration and consumption patterns used by organizations has grown exponentially. These data types require open platforms and flexible data architectures to ensure consistency with an appropriate orchestration across environments and strong re-approach of traditional capabilities and skillsets.
- The business areas need more value, faster — it’s a fact that the multi-everything landscape has triggered a more demanding world. Competition plays harder, and every day, new business models and alternatives driven by data and digitalization surface in almost every industry. Lines of business have increased pressure to speed go-to-market of innovation through new data-driven solutions, products or businesses. IT works to manage the underlying risk, security and performance through governance, without limiting flexibility. This balance between innovation and governance leads to new ways of working, like how the portability of data and analytics solutions has become a way to anticipate and adapt to change by enabling high flexibility to run in different environments and avoid vendor lock-in.
5 recommendations for a data strategy in the new multi-everything landscape
When it comes to getting a data strategy right, I like to apply some of the basic principles of a successful business model — scalability, cost-effectiveness and flexibility for change — and extend these concepts to technology, processes and organization. Organizations with data strategies that lack these factors often capture only a small percentage of the potential value of their data and can even increase costs without significant benefits.
In addition to the traditional data strategy considerations, such as recognizing data as a corporate asset or shifting to a data-driven culture with multi-functional teams, here are five recommendations for a data strategy that takes advantage of the multi-everything landscape:
- Give data assets and accelerators top priority: Develop a process and culture around data that enables true standardization, re-use, portability, speed to action and risk reduction across the end-to-end data lifecycle. From the inception of use cases through the development, deployment, operation and scale of your assets, your data strategy should be supported by the right technologies and platform to enable fully operational and scalable solutions.
- Establish a true enterprise-centric operational model: It’s critical to have the right operational model that’s fully aligned with the organization’s business objectives and its partnership ecosystem. This requires a deep understanding of the organization’s strengths and weaknesses. Embrace best practices but run away from pure academic approaches. Think big, but prioritize and articulate realistic and actionable plans, establishing the right partnership models along the way. That said, adopt and extend agile techniques as soon as you can.
- Revisit the extent and approach for data governance: In this multi-everything landscape, data governance functions, processes and technologies should be constantly revisited 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 foster trust in your data by creating transparency, eliminating bias and ensuring explainability for data and insights fueled by machine learning and AI.
- Don’t lose the basics: To improve business results, leverage data in a sustainable way and prioritize projects that are scalable, cost-effective, adaptable and repeatable to deliver both near- and long-term results. In all cases, the data strategy should be tightly aligned with your business objectives and strategy and built upon a solid and governed data architecture. It may be tempting to jump quickly into advanced analytics and AI use cases with the promise of astounding results without having considered every implication in the equation, but remember there is no AI without IA.
- “Show and tell”: Take advantage of proven experiences, new technologies and existing assets as much as possible, and don’t forget to show results quickly. With the capabilities offered by hybrid multicloud environments and innovative co-creation and acceleration methods like the IBM Garage, you have the tools to design, implement and evolve your data strategy to continuously deliver on business outcomes. By showing tangible outcomes, fostering adoption and operationalizing at scale, you can reduce risk and accelerate the journey to a long-lasting, data-driven culture.
While the core principles of a data strategy remain the same, the ‘how’ has dramatically changed in the new data and analytics landscape, and the most successful organizations are the most adaptable to change when revisiting the data strategies. Today approaches and architectural patterns like data fabric and data mesh play an increasingly relevant role through enabling technologies and platforms like hybrid multicloud. As you look ahead, review your data strategy based on the opportunities presented in the new multi-everything landscape, and get ready for change.
To learn more about how to design your data strategy, check out The Data Differentiator.
To learn more about our IBM data and analytics solutions, visit our Data and AI-Driven Analytics page.