What is the purpose of a company’s data strategy?
Data: it’s the best of times and the worst of times. Over the last two decades, there’s been a huge surge in interest, innovation, and investment in big data. It’s hard to find a company that doesn’t want to be more data-driven or a CEO who isn’t interested. Data professionals have never been in higher demand, and new ideas, technologies, and career paths are hitting the market at warp speed. Sounds amazing, doesn’t it? But remember: the data industry is really good at gloss.
The reality for most companies is very different. The chasm between the hype and the reality has probably never been wider. Many businesses are still grappling with the basics. What can data do for us? Where does this data come from? Can I trust it? Why do we never have the data we need? combine this with the deafening call from the C-Suite of we need to automote, how do we introduce artificial intelligence, where is the machine learning?
We’ve seen many examples when expectations are inflated, or investments are made in tech or talent without anyone stopping to think why. It’s a dangerous path, and it won’t take long for the excitement to give way to frustration. The reality is, that you won’t get a return on investment unless you’re clear about what you want to achieve.
Which is why you need a Data Strategy.
If you get it right, a Data Strategy will unify data activity behind a clear vision and case for change which are grounded in the business goals of your whole organisation.
What is the purpose of a Data Strategy?
In simple terms, a Data Strategy sets a vision, detailed strategy, and roadmap which explains how an organisation will use data and analytics to realise its strategic objectives.
A successful Data Strategy is not about drawing a different future for your organisation, or a standalone strategy that is purely about data. Therefore, a Data Strategy is really about explaining how data can play a key role in the achievement of the organisational future envisaged in the business strategy, and what changes, investments, and capabilities are needed to make that happen.
For some businesses, their organisational strategy is not well documented or in a state of flux, which can make the process of defining a data strategy a more iterative process involving discovery and re-confirmation of organsational 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 behind a clear set of business goals, with a compelling vision and case for change to drive engagement and adoption.
This quickly becomes a complex exercise covering a huge range of topics and themes, but when you’re writing a data strategy, it’s vital to stick close to this simple purpose. Remember, it’s data for strategy, not a ‘strategy for data’!
Do we need a Data Strategy?
Until recently, many businesses didn’t consider the need for a data strategy. However, the explosion in enterprise data and processing power has created an impetus for organisations to ‘up their game so they can make better data-driven business decisions.’
A recurring theme is organisations skipping over data strategy and leaping straight into technical implementations. 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”: very often organisations have a sense that they are missing opportunities to use data better but are struggling to identify how. A structured data strategy design will uncover and prioritize the relevant opportunities.
“We need to set a vision”: one powerful benefit of a high quality data strategy is that it creates a clear future to unite efforts and guide decision-making to give organisations a competitive advantage. Sometimes, this needs creating from scratch, in other scenarios, we find 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 disperse or budgets and decision-making rights are withheld or non-existent. A data strategy will align priorities, resources, and roadmaps behind a clear vision.
“Where’s the ROI?”: a very common challenge. 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 a frustrated bunch. Availability, access rights, quality, and timeliness of data are all perennial frustrations. An effective data strategy harnesses these pains, identifies root causes, and mobilises investment in solutions.
“Our house has no foundations”: a frequent refrain from anguished data leaders. The key challenge is helping link customer frustration (“the dashboard is wrong”) to root causes (“nobody owns the data”) to solutions (“we need data governance”). This is about linking data management best practice with a narrative that is grounded in day-to-day data customer challenges.
“We’re stuck in a reactive cycle”: many data teams become trapped in urgent demands, re-work, and improvisation. This is a draining environment and can prevent any long-term capabilities or enduring corporate knowledge from emerging. A collaborative process to define and implement a data strategy helps break this cycle.
Every business will have their 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’s next? Well, starting with the right mindset and approach is crucial.
Creating Your 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 organization 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 through the realisation of the data strategy? What will be different when the implementation has occurred. Will this improve your customer experience ?
Objectives – What things 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?
The last year has seen incredible advancements in AI, with every company looking at its use cases and how it can help business operations and generate business value. We see many similarities to where the concept of data science has been for the past 7-10 years.
Without a successful data strategy bringing in AI tooling or hiring a team of data scientists with the vague hopes that these tools will “make magic happen”, generally does not go well.
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.
To sum up
A Data Strategy is not about giving your business a whole new set of priorities to worry about. It is about explaining 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’re thinking 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
👉 If you’d like to learn more download our ultimate guide to data strategy by click here… Data Strategy Guide From The Oakland Group
Author: Joe Horgan