Data strategy is growing in importance in commercial businesses and within the public sector.
An increasing number of organisations recognise that data is a core asset that has the potential to unlock significant amounts of value.
A data strategy determines how this will be achieved.
It involves creating a limited number of use-cases for data that aim to solve problems and promote opportunities that will allow resources to be deployed for maximum gain.
Each use-case expands into a definition for the data to be used, where it is to be found, how it will be used whilst being kept secure, and the skills and technology required.
It’s a business planning framework
Sitting alongside the data strategy are the major planning frameworks:
- The strategic plan
- The long term financial plan
- The budget.
The strategic plan sets out the vision and mission for a period into the future along with the performance measures that will be used to measure and guide progress.
The long term financial plan and the budget set targets in order for the mission to be delivered.
The long term plan is typically for the next 3 to 5 financial years, the budget is for the forthcoming year.
Growth in value is a requirement from both the long term plan and the budget. The value will be financial in nature, but it may also be in terms of investment in environmental sustainability, the people in the organisation, philanthropy and local communities – often referred to as Corporate Social Responsibility (CSR) growth.
Because data can unlock substantial amounts of value, the data strategy has a critical role to play in the determining how the long term plan and the budget will deliver the growth in value required.
It is likely to be administered by the data specialists in the business. Ownership however should sit at the very top of the organisation.
Planning and uncertainty
The importance of the long term financial plan and the budget cannot be over-stated.
The financial plan determines how the strategic plan will be resourced and what the return from the plan will be. Without it, the ability to deliver the strategic plan may be severely undermined.
Nonetheless, it has to be based upon assumptions about the future that may be far from certain – e.g. for a commercial business, that customers will still keep coming despite the many influences that might take them elsewhere. For a public body, that funding at the level required will be made available.
The budget, on the other hand, is akin to an engine room, designed to move the business forward towards the strategic and the financial goals.
It has a complex role to perform, because the targets set out within it should relate to the measures stipulated within the strategic plan and the goals set out in the financial plan.
It is through the process of striving for and meeting these targets that the business steps forward in the right direction and at the pace required.
For a public body the role played by the budget is very similar. It has to deliver sustainable services at a cost that is in line with available funding and help to ensure that funding continues at the levels required into the following years.
Data strategy roles
The data strategy therefore has two roles to perform:
- Deliver forecasts that will be used to develop the targets set out in the long term plan.
- Deliver the growth in value required by successive budgets in order to deliver the strategic and the long term plan.
Forecasting for the long term plan
The important forecast is that for revenue or funding.
Forecasting involves a core set of ‘value drivers’ e.g. volumes of transactions, numbers of clients to be served, likely pricing levels, contract lengths. These are then overlaid by influences such as macro-economic trends, major events, shareholder expectations.
Forecasting for the long term plan may feel like guesswork, however the data available for the task can be surprisingly powerful.
E.g. through a mix of historical data, predictions for customer needs, anticipated competitor activity, own planned events – and other potential sources of data – a forecast can be built that delivers a range of expected values at each key date out to the plan horizon.
The range of values is important. It serves as an indicator of confidence in the use made of the data, and as a guide for the setting of goals.
With the ‘top line’ forecasted, an expenditure forecast can follow – based upon a range of ‘cost drivers’ of differing degrees of significance and variability.
The role of the data strategy is to ensure that this forecast is based upon the best use of the data available. The better the forecast, the greater the understanding of the work required to achieve the targets set.
Delivering value into the budget
Similar to the long term plan, the targets set out in the budget for revenue, funding and expenditure also involve a range of value drivers and cost drivers. In addition, however, the budget must also deliver growth in measures stipulated within the strategic plan e.g. those set out within CSR policy.
The role of the data strategy with respect to the budget is therefore to ensure that the assumptions that underpin the budget are based upon the best use of the data available.
It is also to reveal opportunities to build value into the budget e.g. identify ways to transform revenue or costs, or any of the CSR measures, which can be incorporated into future budgets.
Developing the data strategy to drive the budget
A data strategy does not have to be prescriptive in its approach. The seven steps below, however, ensure that all the relevant planning has taken place before any investment takes place in resources or technology.
1. The use-case
It is very easy to throw resources at data in the hope that insights will be discovered that will lead to some form of benefit.
Use-cases ensure that work to unlock value from data takes place only when the objective is clear and that it has a high chance of success.
A use-case should identify a person or people with a task to perform – and a set of questions to which they need answers – with questions evaluated in terms of the potential value that the answer will bring.
For example, a use-case related to the budget might involve budget holders being tasked with reducing the cost base by a certain percentage.
Several potential questions present themselves:
- What spend can we reduce without undermining the business?
- What tasks could be automated?
- What additional efficiencies could be introduced?
- What services could be stopped?
The evaluation is performed by a team of people who know the business well who reach a consensus about the questions to pursue taking account of all the objectives that the budget has to meet.
E.g. the choice to stop a service might lead to increased carbon emissions elsewhere and therefore a clash with a key CSR objective.
Or the choice to reduce a cost item associated with, say, shipping products by air, might undermine services to customers.
That said, if it is known within the team that air shipments happen whenever production delays occur they might add a further question:
What spend can we reduce through the repair of process failures – such as those that delay production?
2. Translating use-cases into a data definition
This step is best performed using a structure that is similar to a decision tree. This is because the translation process needs to pass a use-case through a number of steps to identify:
- The measures to be improved (e.g. costs);
- The objects to which each of the measures relates – which will therefore become the focus for the use of data and be the basis for definitions for the data required (e.g. tasks caused by failures);
- The triggers that drive each of the measures (e.g. failure events);
- Features that relate to each of the measures (e.g. time per task) .
The outcome from this process is a list of objects and a set of metrics associated with each object which can then generate the list of data items required.
3. Data governance
The lists of data items to emerge are likely to overlap – with some items playing a pivotal role within several of the use-cases.
Once consolidated into a master list the strategy must then determine how the data will be sourced, where it will be stored, whether any data quality issues will need to be resolved, how to achieve secure and reliable access for the right people, uses to which the data can and cannot be applied, adherence to governance principles such as the General Data Protection Regulation (GDPR).
4. Processing requirements
This step identifies the tools, infrastructure and methods involved in sourcing and processing the data in order to deliver answers to the use-case.
At this point it should become clear how much of the data processing task can be performed on existing platforms – and what investment, if any, is needed in new technology.
The choices made so far determine who will work with the data and the skills required. It’s an opportunity to equip people who have a deep knowledge of the business with skills that they might not have otherwise acquired.
In the case of the budget example, if Finance staff perform the processing required, they will develop a deep understanding of the budget that can be carried forward into the forthcoming year and on into successive budget periods.
6. Establish who is responsible for success
The person responsible for the application of the data strategy will probably be from amongst the use-case sponsors. In the case of the steps relating to the budget use-case, this might be the budget holder.
7. Define how success will be measured
The measures of success should be apparent from the development of the use-cases. E.g. answers presented to the questions posed.
What should not be within the data strategy
The sequence of steps outlined above have a very clear purpose. They are to ensure that the task to be performed is a clear as possible before any investment in skills and technology takes place.
They are to also ensure that efforts are focused towards the major opportunities to unlock value – which are very likely to require a cross-departmental approach.
Therefore every effort should be made to discourage use-cases which are limited to specific departments, and choices made about technology without there being a clear definition for the role that the technology will play.
Finally the data strategy is a business planning tool that is there to help deliver the business strategy. It should not be seen as the preserve of a particular department e.g. IT, simply because it refers to data.
What the data strategy means for the planning processes
At first sight, a data strategy appears to introduce another layer of administration into the planning processes. Also, given that use-cases will change from year to year, the task of updating the data strategy will become yet another annual process.
But it will encourage people from a wide range of disciplines, who know the business well and who understand the business strategy, to roll their sleeves up become engaged in working with data.
This should also include members of the senior management team who will need to design the planning processes to make full use of the data available.
It will also mean that data structures fall into place that are designed to answer the key questions about the business. Also, in the hands of people who are increasingly at-ease working with data, planning tools such as the budget will become increasingly effective at driving value into the business.
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