The importance of momentum when implementing a data strategy
When setting out to implement a data strategy there are many ‘must-win’ battles. Worrying about not winning them leads to hesitancy. This can turn into inertia, which can root an organisation in long term inaction.
We know that some financial services firms have been slow to implement a data strategy because they worry about doing it wrong or have not gone far enough in embedding AI into their organisational structure.
Inertia, delayed action, inconsequential decisions or uneasy pivots can all serve to derail a business and prevent momentum – the opposite of inertia and what drives a data strategy from theory into practice – from building.
There are often many seemingly solid reasons for not implementing a data strategy quickly or robustly enough from underlying structural or process issues, legacy architecture and software, differing agendas and more. But the AI revolution in business is here, it has momentum and organisations that delay in letting data dictate their agenda, not just be a part of it, risk losing vital ground in quickly fragmenting marketplaces.
But building and implementing a data strategy needn’t be a source of frustration for organisations.
The value of value
As with achieving any goal, motivation is key. The first consideration when building the foundations of a data strategy is to ask why an organisation wants to build one in the first place. This means constructing a comprehensive data landscape through the identification of true business value use cases.
If this data landscape isn’t constructed, and value cases not identified, when things go wrong along the way (as they inevitably will) theory remains as theory rather than being put into action. Hesitancy and inertia creep back in and momentum is lost.
By building use cases, an organisation can build a transformational data strategy based on value for the wider business. This helps to maintain momentum throughout implementation, grounding the data strategy in a positive data culture with full buy-in from stakeholders. By beginning with what an organisation values, leaders will be empowered to prioritise valuable data delivery, understanding how this will achieve the organisation’s target data landscape.
Building from this solid foundation whilst achieving buy-in from leadership, an organisation will quickly achieve results in implementing their data strategy. Crucially, they will also be able to push home those results over time and continue implementing what they set out to do.
Momentum in action
As French poet, journalist and pioneering aviator, Antoine de Saint-Exupéry once said:
“A goal without a plan is just a wish.”
We know this to be true from personal experience. We want to run a marathon but we don’t know how to train. We want to buy a new car but we don’t have a clear idea of how to budget. Goals and ambitions are one thing – putting a plan in place that delivers them quite another.
The same applies if you want to implement an effective data strategy. Organisations often say: “We’ve defined our 2025 target state – but we don’t know how we’ll get there.” Whilst this is not a bad place to start, as they have a goal in mind, now they need to put in place the plan that helps them to achieve the target.
Many organisations start with strategies which define future concepts but lose momentum when they move to the execution phase. There isn’t an effective process in place. Too often, a goal is found to be too difficult or a plan not robust enough. This leads to hesitancy, inertia and failure.
It must also be said that failure is only failure if you start there. Aversion to risk is not an excuse for not implementing a data strategy.
Executing on lofty ambitions is never easy. That’s why a robust, modern data strategy must allow for flexibility. Without a flexible plan that sets out how an organisation is going to execute and deliver on its goals over time, most organisations never get started. Equipped with such a plan, an organisation has momentum and can handle risk along the way as long as incremental learnings from supposed failures are built into the process as a whole.
How we deliver momentum to our clients
To help our clients to achieve momentum, our approach focuses on 3 core objectives:
- Set the vision and constraints
As mentioned, we always start by building a value-focused vision for data within the organisation. We then work through the implications in terms of the target architecture and organisational design. This allows us to create a lightweight and flexible target framework to prioritise our use cases with the business.
- Build the business value case
We put users at the heart of the strategy by uncovering what challenges they face and the opportunities they are targeting. In capturing these use cases, we assess which are of high value. This provides us with an engine that allows us to aim at lofty ambitions.
- Start now and prototype
Our information designers and visualisation engineers bring the strategy to life through prototyping high priority value use cases and visualising the data from the strategy to encourage data-led decision making. This means that we ‘live the culture’ we are trying to build. We don’t just rely on theory – it’s vital that we put our theory into practice.
By following the process above, we deliver data strategies in a matter of weeks that are executable across an entire organisation. We also bake in risk and expected failures into the process that are learned from and acted upon to maintain the all-important momentum which drives value and results.
Momentum leads to maximum impact
If inertia creeps in, it’s all too easy for an organisation and its stakeholders to revert to old habits, trusting what they know as they’re unsure about whether a new strategy is going to work.
By generating momentum, we hope to deliver genuine behavioural change in the way an organisation approaches data. Of course, data-led decision making needs to start with data teams. But with a clear path and some momentum, an entire organisation, from senior leadership down, will see and feel the benefits that data can bring.
A process, not a concept
It’s crucial to understand that our approach to implementing a data strategy is not a concept but a process.
We recognise that there can sometimes be a disconnect between business theory and practice, and that’s why all of our work is designed to help organisations put the theory into practice as quickly as possible. We believe this is what leads to organisation-wide behavioural change.
The best thing about momentum is that once you have a little of it, you can soon have a lot which is why, often, the concept of the flywheel is used to describe AI’s potential impact on your business.
Putting the beginnings of a plan into action today can lead to a solid data strategy being in place in the not too distant future. All you have to do is start.