Everyone talks about data strategy but what exactly is it, why do you need it, how do you define, execute and measure the effectiveness?
Organizations are overwhelmed by the data tsunami, even more so since the arrival of the pandemic which has accelerated transformation and data driven initiatives. And whilst organizations might have an understanding of their goals and priorities, having a well-defined and understood data strategy is crucial to the overall success of digital transformation programs.
But how do you justify investment in a data strategy if all of your organisational transformation programs are progressing well and there are no issues with data? You may hear the question: “We know what we are doing. Why do I need a data strategy?”
The value of data has evolved dramatically in the past five to ten years as organizations shift from data as a byproduct to data as product. The mindset is fundamentally shift to treating data as a first-class citizen to not only avoid the inherent dangers (such as ethical and responsible data uses, privacy, security, data silos and data swamps) but to also enable the organisation to rapidly realize the value from the data assets and compete in digital markets.
Based on recent experiences, organizational transformation journeys are founded on a combination of business priorities (for example, sustainability or safety), competitive environment and localized employee or customer needs. This is typically the starting point for programs of transformation (which incidentally has can been accelerated by the recent pandemic). But how does a data strategy support the business strategy and enable value for the organization?
At the core, the data strategy is an agreed plan (and roadmap) to improve the business and technology processes for data management, to ultimately enable the business strategy. The data strategy enables the foundation for execution of the business strategy (and it should be directly connected to and be informed by the business strategy and business model). The combination of technology (to provide the core infrastructure capabilities) and data (to enable business insights and outcomes aligned to strategic goals) provides the real value to organizations and path to success.
But where do you start? A typical path to data strategy definition and execution is illustrated below in Figure 1. Please note this is just an example and approaches may vary.
* ACM (Adoption and Change Management)
The definition (output) of the data strategy will vary but approaches observed include a combination of viewpoints as a result of the process flow illustrated above. Typical work products could include:
- Business canvas incorporating benefits dependency network
- Objectives and Key Results (OKR’s) incorporating both aspirational (north star) and committed OKR’s (e.g. use case implementation and outcomes) – this is important to review and measure progress.
- Current state assessment covering people (organizational capabilities), process (flows) and technology (data estate, systems, platforms). This may include a maturity assessment such as EDM DCAM or CMMI DMM to inform data readiness (across various dimensions data quality, metadata, data governance etc.).
- Detailed implementation roadmap, with prioritized use cases typically mapped to horizons.
There are also a number of crucial success factors which should be addressed during the discovery and planning phases.
- A well-defined adoption and change management plan is a critical capability needed to ensure successful execution. The adoption and effectiveness of the data strategy will be directly impacted by the organizational culture – more specifically, the beliefs, behaviors, and mindset of the organisation. As Drucker states, “culture eats strategy for breakfast”, but it can also eat agility for lunch and innovation for dinner, simply meaning that any strategy can be inhibited if people do not share the same vision, values and understanding.
- A clear and shared understanding of the data strategy is essential as a foundation for becoming an intelligence driven organisation. Engage a broad community of stakeholders early in the process work with the change management team to drive adoption and awareness to minimize resistance.
- Start small and grow, you don’t need to start with a huge monolithic program with multiple parallel work streams. Prioritize use cases, review/evaluate and adjust approach as needed. If you haven’t done so already, embrace an DevOps culture (across people, process and technology) to support incremental and continuous delivery value across your use cases.
- It is also important to remember that the data strategy does not have to be static, it can be dynamic and adjustable to accommodate learnings/feedback.
Finally, the Center of Excellence or ‘island of excellence’ model has also become very popular as an enabler to support data strategy execution. This is particularly useful in large complex organizations with multiple lines of businesses, and low data and cloud maturity. Centralized data and information management COE functions have proved very useful as a frictionless mechanism to execute and accelerate the data strategy across teams or business units for example, technology standardization, data readiness, data architecture blueprints, use case implementation.
- In closing, I would like to emphasize the following:
Each organizational data strategy is unique but is typically tied to the business strategy.
- The data strategy provides a shared understanding and vision enabling everyone to relate technology and data management activities to business strategy.
- The data strategy may vary at different levels according to the organisational complexity. For example, data strategy may be defined within specific lines of business, but should ultimately have some commonality and connection with the organizational data strategy.
- Consider a Center of Excellence to provide a frictionless engagement model to accelerate execution of the data strategy across different business groups.