We have helped a multitude of organisations harness data and analytics to create value for their business and their customers. Based on our experience, we believe that many organisations would benefit from a clearly defined data strategy. Read what data strategy is, why it matters and how to get started.
What is a data strategy and why do you need one
What is a data strategy and why do you need one
Bridging the gap between business strategy and practical data development
The purpose of a data strategy is to build a bridge from the organisation’s strategic choices into the discovery of opportunities for creating value with data and the development of practical data capabilities. The data strategy should cover the entire data value chain from obtaining and managing essential data assets, to generating actionable insights from analytics and business intelligence, and eventually delivering tangible value in the form of business use cases.
Why having a data strategy is so important right now
Several factors have converged to create a pressing need for a dedicated data strategy that augments the overall strategy of the organisation:
- Rapid technological development has opened up a host of new opportunities for creating value from data and analytics, while at the same time rendering much of existing infrastructure obsolete and unfit for competition.
- Consequently, the utilisation of data and analytics has become a crucial competitive factor, transforming it from the sole responsibility of IT into a central concern for every business function.
- Often, the various parts of an organisation seem to be on different data and analytics maturity levels, leaving many people unsure what data and analytics means for their role, and what is expected of them in the data-driven future.
Data strategy is a vital management tool
A well-formulated data strategy will equip the management with the right tools to tackle the aforementioned challenges by:
- Ensuring that the organisation focuses its scarce data and analytics resources on those initiatives that yield the greatest strategic impact.
- Telling a compelling and inspiring story that helps everyone within the organisation understand why data is important, what the organisation is trying to achieve with it, and what is their own role in it.
- Building confidence among stakeholders that the organisation knows how to create value with data and is determined to make it happen.
Elements of a data strategy
Based on our learnings from the numerous clients we have worked with, we have composed a data strategy framework that presents a structured, tried-and-tested approach to developing a successful data strategy. It defines the main elements of a data strategy, describes how they are connected together, and provides practical guidance for conducting the strategy development process. Here is a quick overview of the main elements:
Data strategy elements
The data strategy must take into account significant changes in your organisation’s business environment that will affect its ability to create value from data and analytics. Such trends may arise in multiple different domains:
- Changes in the technological environment, such as the rise of cloud computing platforms, or the application of blockchains to business context
- Changes in the economic environment, such as the emergence of new platform business models, or the availability of funding for AI initiatives
- Changes in the social and cultural environment, such as attitudes toward sharing personal data, or the expectation for supply chain transparency
- Changes in the regulatory environment, such as the requirements for privacy protection or the explainability of AI solutions
These changes may open up new opportunities for value creation, but can also make existing solutions obsolete.
The data strategy must consider your organisation’s role in the wider business ecosystem. Of particular interest are the flows of money, services and data between the players. More often than not, some of the key data assets needed by your organisation are owned by some other player in the ecosystem, and you must try to find a way to access them.
We frequently see new data ideas stemming from the questions “what data do we have?” and “which exciting new algorithms could we apply?”.
This kind of ideation may sometimes uncover significant business opportunities, but more often the outcome is fancy one-off gimmicks that sit outside the core business processes and fail to generate long-term value. This is not a solid foundation for a data strategy. Instead, we recommend starting from the important strategic challenges that your organisation has identified, and finding ways in which data can help to solve them. This way, the data strategy can build a bridge from the overall strategy into practical data development.
The data vision tells a compelling and inspiring story about what your organisation is trying to achieve with data and why. This kind of storytelling is essential for facilitating organisational change by inducing alignment, engagement and commitment to the shared vision. To enhance the effect, we recommend inviting the key stakeholders to participate in shaping and formulating the data vision. Furthermore, insightful visualisations help to crystallise the data vision and communicate it more effectively throughout the organisation.
A common pitfall is ending up with a vague and watered-down data vision that tries to encompass every possible data opportunity and please every business function. Instead, we advocate making tough decisions about prioritising the most important focus areas, to ensure that they receive consistent attention and investment in the future.
Data opportunities can be the most exciting part of a data strategy, since they describe the concrete ways in which data and analytics will create value for the business and its customers – we often call these business use cases. We have identified four major areas of data-driven value creation, where data opportunities can be found:
- Improving the efficiency of core business processes
- Developing new customer-facing data-driven services and experiences
- Selling and exchanging data
- Using open data to catalyse a business ecosystem
When describing data opportunities, the trick is to find an optimal level of concreteness. A good approach is to start by generating a variety of high-level idea headlines, then proceed to iteratively select the best ones and flesh them out in a bit more detail, using a predefined template. You can prioritise your data opportunities using these four lenses (adapted from IDEO):
- Desirability: Does the proposed solution generate value for the customer or the end user? What evidence do we have for it?
- Feasibility: Can the proposed solution be implemented with reasonable cost and effort? Does it involve significant technological uncertainties?
- Viability: Will the proposed solution be profitable? What are the most important drivers of profitability, and how sensitive is the business case to them?
- Sustainability: Is the proposed solution compliant with data processing regulations? Is it in line with the policies and values of your organisation, for example, avoiding discrimination? What kind of impact does it have on society and the environment?
We recommend using service design methodology for discovering and refining the data opportunities. In particular, iterative hypothesis-based co-design together with the customers or end users can help ensure that effort is not wasted on building data solutions no-one will use.
In order to turn your data vision and objectives into reality, you will need various kinds of data capabilities. Organizations often focus on technological solutions, but we advocate taking a holistic view that fully incorporates the human perspective. To this end, we have formulated a framework we call The Data-Driven Business Puzzle.
For example, from the technology perspective, you will need to consider which solutions you should migrate to the cloud, or perhaps even redesign cloud-natively, in order to reap the full benefits of cloud computing. At the same time, you will need to carefully choose the algorithms that best fit your business needs, and integrate them seamlessly into your business processes. But since your algorithms are only as good as the data you feed them, you will also need to identify and acquire the most valuable data assets for your business, and nurture them with data management and governance processes.
From the human perspective, your organization will probably need to gain access to new data and analytics skills, either through training, recruitment or outsourcing. New ways-of-working are necessary in order to leverage familiar DevOps practices, such as version control, continuous integration and automated testing, in the arena of data and analytics (this is sometimes dubbed DataOps and MLOps).
The art and science of fostering a data-driven business culture may seem enigmatic to people coming from a technical background, but there are tried-and-tested methods for alleviating uncertainty, building confidence, inspiring engagement and motivating change. Furthermore, the positioning of the data and analytics practitioners within the organizational structure needs to strike a balance between ensuring strong data domain knowledge and avoiding counterproductive silos. Finally, the leadership team should play a crucial role by painting a clear vision, ensuring sufficient resources, and setting an example of data-driven decision making.
Once you have an idea of your data vision and opportunities, we recommend conducting a holistic data capability maturity assessment across all pieces of the data-driven business puzzle to identify the most important bottlenecks in your data capabilities.
Now that your organization has a good idea of where it’s going with data, you need to translate this vision into clear objectives, and define unambiguous metrics that can be used to measure progress.
We recommend developing a balanced KPI framework that covers the key focus areas for data opportunity and capability development. One tried-and-tested approach for this purpose is the Objectives and Key Results (OKR) method, made famous by Andrew Grove at Intel and John Doerr at Google.
The end goal of the organization’s data initiatives is typically to make an impact on financial business metrics such as revenue and profit. However, the feedback cycle between practical data development activities and financial outcomes is often too slow to enable quick feedback loops for iterative learning. Therefore, we recommend complementing those lagging indicators with leading indicators that provide near-instant feedback on development efforts, allowing your organization to continuously hone its plans as you learn what works and what doesn’t.
The final element of the data strategy is the data roadmap that outlines the sequence and timeframe for achieving the data objectives. The roadmap should cover both the most important data opportunities (business use cases) that will deliver tangible business impact, as well as the data capabilities (enablers) that your organisation needs to improve. Again, remember to take a holistic view to capability development, looking beyond mere technology. Both opportunities and capabilities should be clearly linked to the overall data vision and objectives that your organisation is striving to achieve.
Keep your data strategy up-to-date
It would be tempting to think that once you have outlined your data strategy, the execution is simple and straightforward. However, nobody has a crystal ball that can predict the future, and even the best-made plans will not survive their first contact with reality intact. Therefore, we encourage you to build continuous experimentation and learning into your data strategy execution. You need to identify the most critical assumptions that your plans are based on, and proceed to test them swiftly. We recommend establishing a regular schedule for reviewing and fine-tuning your data strategy at least annually, if not quarterly – it usually makes sense to synchronise the data strategy review with the overall strategy review process.