Most data strategies fail to make it from reading material to change accelerant. This means that they don’t create tangible outcomes. Worse, they fail to address gaps in understanding and expectation. Simply, they don’t achieve relevance. When this happens, data and analytics remain an isolated technical domain focused on core activities’ ‘numerical exhaust.’
Why does this happen?
One thing failed data strategies almost always have in common is this: they were written as a ‘strategy FOR data.’ I know this sounds like an odd thing to criticise. Why wouldn’t a data strategy be a ‘strategy for data’?
Well, the problem is this: starting with the thought ‘I’m going to write a strategy for data’ is inevitably inward-facing. Start with this, and it’s difficult to avoid slipping into:
- talking about the technology you want to buy,
- or explaining how the literacy of your internal user base is underwhelming
- and/or peppering the audience with jargon
This isn’t a great read for the folks who don’t work in data. If you start with a data-centric perspective, you will write something that’s insular, tech-heavy, and leaves the audience cold. Then, your data strategy goes on a shelf. You might be able to tick a box and say, ‘we have a strategy for data,’ but none of the expected benefits will materialise.
It’s Data FOR strategy’
The point here is that your business already has a strategy, and it isn’t looking to the data team for another one. The last thing anyone needs is another department coming up with a shopping list. A Data Strategy is not about giving your business a whole new set of priorities to worry about. It’s 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”.
That is the subtle but vitally important rule to remember when you’re writing or refreshing a data strategy.
Putting it into practice
So far, we’ve focused on getting clear about the right mindset to bring to data strategy development (i.e. data FOR strategy). But it’s vital to translate this into an effective process for strategy development.
Many organisations struggle with this shift. Even when you have changed your mindset, you can easily get stuck. It’s easier when you have a method you can rely on.
At The Oakland Group we base our approach to Data Strategy development around four key principles:
- Data is business: Data must be re-cast as a means to meet organisational need. At a simple level, the starting point for your data strategy is to therefore understand:
- What outcome(s) does our organisation need to achieve?
- What should the role of data be in that journey?
- How will business value be created?
People, Process and Tech = Capabilities: the modern data strategy requires a balance across technology, people and. A powerful way of looking at this is through the lens of capabilities. What does your business need to be able to do with its data? Buying technology or talent is inherently transactional and narrow in scope. Creating capability requires carefully balanced orchestration of people, processes, and technology. It opens a wider perspective for a data strategy. This is because the capabilities required will be very different depending on the needs of the organisation. If you think about capabilities as the data ‘muscles’ of the organisation, you need to know what you’re training them for. Are you running a marathon breaking the 100m world record or strolling for the bus?
Stories not sermons: any effective data strategy must have a compelling narrative. Writing and implementing a data strategy is fundamentally a storytelling challenge. If you can’t persuade, you can’t get anywhere.
It’s easy to get lost in a maze of complex frameworks, jargon and detailed arguments. For the non-technical audience, a Data Strategy to tell three interrelated stories:
The value story: how will the organisation create concrete value from data against its strategic objectives?
The data management story: this describes how the organisation will collect, store, organise, curate and safeguard its data.
The data culture story: this is so often overlooked, but vital. What should people think, feel, role model and advocate?
Co-creation: too many data strategies are cooked up by a select few in the data team and then unleashed on an unsuspecting audience at short notice. These take ages to create and hardly ever land well.
A closeted approach might give the author control or make them feel clever. But it’s unlikely to get business buy-in. You’ll end up with a document that is too technocratic, too inward-facing and too peripheral to really make a difference. At Oakland, we think data strategy should be an outward-facing, whole-company effort. A data strategy is a shared document that sets out how the organisation plans to use one of its most important assets. It should grow out of workshops, customer engagement and deep analysis of the needs and personas of the host organisation. The final presentation should not be a ‘big reveal’, you MUST work collaboratively.
When you put those four principles together, a very different way of looking at creating a data strategy emerges. One that grows as a compelling set of co-created stories grounded in the beliefs and physical reality of your organisation.
The resulting strategy will be a careful orchestration of people, process and technology to create the capabilities needed to deliver your business’ strategy. It will be a lot more than a ‘strategy for data’.
Joe Horgan is a Principal Consultant at Oakland, he regularly posts on LinkedIn about all things data strategy and digital transformation. Follow him here….https://www.linkedin.com/in/joe-horgan/