No matter the industry, every company has its own goldmine of data which, when managed well, can be used to drive leads, increase profits, improve efficiency and more. Harnessing the power of this data is rarely as simple as grabbing a few off-the-shelf data management tools and hoping for the best, though. This is where data strategy comes in.
What is a Data Strategy?
Put simply, a data strategy is a long-term, detailed plan for how your company will use its data to accomplish business goals. This can include data optimisation strategies, tool implementation, the creation of new processes, and more.
Developing a corporate data strategy takes time; we know you want results now. However, managing your data without a plan doesn’t deliver the same benefits as working under a fully thought-out data strategy.
Just give our data strategy guide a read, and you’ll soon understand why a data strategy is something you absolutely need.
What is the Purpose of a Data Strategy?
A successful Data Strategy is not about drawing a different future for your organisation or creating a standalone strategy purely on data. It’s really about explaining how data can play a key role in achieving the goals envisaged in the business strategy and what changes, investments, and capabilities are needed to make that happen.
If a business’s organisational strategy is not well documented or in flux, defining a data strategy can be a more in-depth process involving discovery and re-confirmation of organisational goals. But crucially, this doesn’t change the overall purpose of writing a data strategy.
Done properly, a data strategy will unite data-driven activity throughout the host organisation with a clear set of business goals, a compelling vision, and a case for change to drive engagement and adoption.
This quickly becomes a complex exercise covering a huge range of topics and themes, but when writing a data strategy, it’s vital to stick close to this simple purpose.
It’s data for strategy, not a strategy for data!
Why Do You Need a Data Strategy?
Until recently, many businesses didn’t consider the need for a data strategy. However, the explosion in enterprise data architecture and processing power has impelled organisations to up their game and make better data-driven business decisions.
A recurring theme is organisations skipping over data strategy and leaping straight into technical implementations. Without considering how your data management will aid your company’s ambitions, the tech and tools you choose are unlikely to be the right fit. This results in technical and business upheaval as new technology gets introduced with a localised short-term mindset instead of a longer-term strategic perspective.
If you want to succeed, it’s vital you start by thinking about the challenges and opportunities you’re facing.
Do any of these statements sound familiar?
- “There’s an opportunity in here somewhere”: Organisations often sense that they are missing opportunities to use data better but struggle to identify how. A structured data strategy design will uncover and prioritise the relevant opportunities.
- “We need to set a vision”: One powerful benefit of a high-quality corporate data strategy is that it creates a clear future to unite efforts and guide decision-making, giving organisations a competitive advantage. Sometimes, this needs to be created from scratch; in other scenarios, it’s more about helping to better structure and articulate existing thoughts.
- “Everyone’s doing their own thing”: Many organisations suffer from silos, data quality issues, disjointed data functions, and activity. Accountability may be dispersed or budgets and decision-making rights may be withheld or nonexistent. A data strategy will align priorities, resources, and roadmaps behind a clear vision.
- “Where’s the ROI?”: Either investments have been made which are not showing the expected benefits, or data sponsors and leaders are struggling to secure budget and resources to execute their ideas. The root cause is often a lack of a clear underpinning strategy.
- “The customers have had enough”: Scratch below the surface and data ‘customers’ (internal or external) are often frustrated. Availability, access rights, quality, and timeliness of data are all common frustrations. An effective data strategy harnesses these pains, identifies root causes, and mobilises solutions.
- “Our house has no foundations”: The key challenge is helping link customer frustration (“the dashboard is wrong”) to root causes (“nobody owns the data”) and solutions (“we need data governance”). This links data management best practices with a narrative grounded in day-to-day data customer challenges.
- “We’re stuck in a reactive cycle”: Many data teams become trapped in urgent demands, rework, and improvisation. This draining environment can prevent the development of long-term capabilities or enduring corporate knowledge. A collaborative process to define and implement a data strategy helps break this cycle.
Every business will have its own reasons, but if some of what you’ve just read sounds familiar, there’s a good chance you need to consider creating or updating a data strategy for your organisation.
What are the Benefits of a Data Strategy?
Still not convinced? Here are some key benefits you’ll enjoy when you take the time to implement a data strategy.
Firstly, you’ll experience enhanced decision-making. With predefined processes and easily accessible data resources, making decisions becomes more efficient and accurate. Additionally, a cohesive plan that everyone can follow minimises mistakes across the board. Optimising all processes ensures that you get the very most out of every action performed, boosting overall efficiency.
Careful planning significantly increases security and privacy, safeguarding your data assets. Future-proofing your applications means they are prepared for future advancements, keeping your business up-to-date. With a unified plan, everyone in the business works harmoniously towards the same goals, fostering team cohesion. This, in turn, improves the customer experience, increasing loyalty to your brand. Finally, by using data smartly from each sale, transaction, or deal, you can effectively optimise costs.
What’s next? Starting with the right mindset and approach is crucial.
Creating Your Company Data Strategy: The Big Picture
A key component of your data strategy should be to provide a clear view of how your different types of data will become a key engine for the wider organisation as it drives towards its business strategy. It’s where you paint the big picture of a different, data-driven future for your company.
Key elements to include in your vision are:
Purpose – What place does data have in your organisation, and why is it vital you think about it differently? Finding ways to connect this back to your organisation’s broader business purpose and strategy will be critical, as it cements the foundation of data.
Scope – What’s the scale and content of your strategy? Your scope definition should also clarify what the broader organisation understands by the term ‘data’.
Future – What are the big-picture changes and benefits you want to bring about by realising the data strategy? What will be different when the implementation has occurred? Will this improve your customer experience?
Objectives—What will we be able to achieve as an organisation as a result of the data strategy? Goals should be tangible for the business and aligned with overall business objectives. Expressing (in simple terms) how the data strategy achieves each business objective will aid the communication and buy-in of the roadmap with your senior stakeholders.
Key results – When we have achieved (or are approaching) our objectives, what metrics will we influence, and what will those look like? As with objectives, it is best to express these with key business metrics.
Capabilities – What (at a high level) will we be building or enhancing in our organisation? This section sketches out the capabilities that need to be created, developed, or overhauled.
How Does Artificial Intelligence Fit into My Data Strategy?
We have seen incredible advancements in AI in terms of how it can help business operations like decision making and generate business value with impressive return on investment.
Bringing in AI tooling, such as Intelligent Agents or hiring a team of data scientists, won’t work as well without a solid data strategy to back it up.
Installing the latest tech buzzword tooling or hiring a team of really smart people to just “find value in the data” may seem like the right thing to do, but AI and data scientists will flounder without a good data strategy that is clear on what business problem you are looking to solve. Data is really a hierarchy of capabilities.
If you’re interested in further exploring AI’s role in your Data Strategy, see our AI guides.
To sum up
A Data Strategy is not about giving your business a whole new set of priorities to worry about. It explains how data can play a key role in achieving the shared future in your strategy and what’s needed to make that happen.
So when you think about Data Strategy, remember: it’s not strategy for data; it’s data for strategy.
At Oakland, we’ve built an approach to data strategy development built around four key principles:
🔵 Data is business
🔵 People, Process, and Tech = Capabilities
🔵 Stories, not sermons
🔵 Co-creation and discovery
Learn more about the ins and outs of data strategy in our strategy guide, or see our blog for details of our other data services.
