A strong data strategy paints a clear vision for using data to achieve business goals and identify gaps. It outlines people, data actions, sets measurable outcomes; helping the organisation turn data into real, practical value.
Whether you’re designing a new strategy, or dusting off an old one, here’s what needs to be there.
The 9 Fundamental Components to Your Data Strategy
1. Vision
A data management strategic vision statement should clearly and concisely communicate the organisation’s goals and objectives for managing its data. The value of accessible, high quality and fit-for purpose data should align with the overall business vision and address the capabilities required to manage the data effectively. The vision is painting a picture of the desired future state.
2. Business objectives
Priority business objectives are what the business plans to do. There are two types of objective:
- Offensive – new opportunity, increased revenue
- Defensive – regulatory requirement, risk or cost reduction
Document each objective with a summary, including the problem or opportunity being addressed and the expected business value. Capture the relative prioritisation of each one.
3. Business need for data
Once the priority business objectives have been defined, the role of data management is to work with the business to identify the data required to meet the objectives. The data for each can be described as a data value use case.
4. Current state analysis
Assess your organisation’s current data management capabilities compared to its desired state and identify gaps. Prioritise the capability gaps based on the ability of data management to deliver the data value use cases required for the business objectives. A DCAM assessment is one way to evaluate data management capability gaps holistically.
5. Strategic actions
Strategic actions refer to data management’s specific tactics to achieve the priority business objectives. To define data-related strategic actions, consider these four areas:
- People – Become a data-driven organization
- Process – Build sustainable data management capability
- Data – Optimise data value for business outcomes
- Technology – Leverage automation for data at scale
6. Resource plan
Define the resource plan, including the specific people, process, data and technology requirements, costs, and timelines for each area aligned with the organisation’s overall business objectives.
Ensure the consideration of resources for engagement from the business, data, architecture and technical stakeholders. Without resource integration, shortages in any one stakeholder group will derail the entire strategy.
7. Business case
When the data strategy prioritises by data value use cases, the original cost of data management is tied to that use case objective.
- Offensive business objective (new opportunity, increased revenue)
– attach the cost of delivering the data required to the value of
achieving the objective - Defensive business objective (regulatory requirement, risk or cost
reduction) – attach the cost of delivering the data required to the
cost reduction or fine avoidance in a regulated industry
The BAU cost to maintain quality fit-for-purpose data is a cost of doing business and must be funded as an operations expense
8. High-level roadmap
Prioritising and sequencing strategic actions based on complexity, dependencies, resources, and time is important in developing a comprehensive data management strategy. Use a roadmap to represent the delivery visually. Keep the organisation of work aligned with the strategy.
- People – data-driven culture
- Process – data management capability
- Data – data value use cases
- Technology – data platform & data management platform
9. Metrics
Defining metrics is an important step in ensuring the organisation can measure the strategy’s success.
- Align the metrics with the data outcomes to ensure that they accurately measure success
- Make the metrics actionable and measurable to track progress and make data-driven decisions
- Set targets and baselines for each metric to track the impact over time
- Assign ownership and accountability for monitoring and reporting on the metrics to specific individuals or teams
Need help developing your data strategy? Talk to us at Ortecha.
Lawrence Hill
Senior Consultant, Data Strategy Lead at Ortecha
