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Data & Insights Strategy

Set the vision for data in the organisation and define a pragmatic programme of change to deliver value by putting data to work.

Data to Insight

The purpose of a data strategy is to build the vision and plan to bring data to the forefront of the business, developing a strong business case and detailed plan of actions and capabilities. Creating this shared vision within a data strategy, enables greater internal business collaboration and leads to more effective use of data.  


We design pragmatic, commercially-driven, data strategies for our clients, that define how current and new data resources and technology should be used to drive better business decision-making and drive forward the strategic goals of the business.

A successful data strategy will consider all the data assets of a business to provide insights that enable more informed decision making and goal setting.  With these informed strategic insights, our clients are able to improve the internal mechanics of the business, as well as drive customer activity and develop new growth opportunities. 

Deliver operational efficiencies and improve
the workplace.
Improve customer understanding and trends and improve and optimise the customer experience.
Develop new products and services and create new revenue streams.
Image by Jesse Bowser

Data Strategy Roadmap

An effective data strategy roadmap details the vision, architecture, methods, tools, policies and processes for using data science effectively across the business.  Critically, the approach for developing an effective data strategy roadmap is dependent on the needs of our clients and their wider strategic business goals.  


Depending on our client’s requirements, we follow one of two model approaches to develop a data strategy, which enables us to develop from data to insight and empower strategic thinking.

Waterfall Data Strategy

​Our waterfall data strategy approach follows four chronological steps to establish an effective data and insights strategy roadmap for our clients. 


This is the primary approach we use with our clients, wherever they are in the data journey, as it thoroughly identifies all areas of the business where data-driven transformation could occur and identifies the key aspects within the business to focus on to deliver significant change and growth opportunity for our clients. 

Discovery and Requirements Gathering

A natural extension to our Capability Assessment, we identify and get to understand our client’s current business objectives, challenges and the key data use cases for the business. 

This is the start of the collaboration journey and establishes the art of the possible within data science and analytics.


Vision Setting


What does success look like to you? 

By effectively engaging with our clients’ key stakeholders, we collaboratively identify the overall vision, objectives and success factors for the data and insights strategy.

Critically this aligns with tangible financial benefits or goals including customer and staff engagement.

Strategic Planning

Define the business case for data investment and all the business areas for quick wins to be implemented. 

This will drive support and momentum for the data strategy and areas where value can be quickly generated are agreed.


Capability Building


Longer term requirements are defined and set out in a 6 month or up to 1-2 year roadmap, which remains aligned with the company’s strategic objectives/quick wins.

It acknowledges the requirements for future adaptations and change management procedures as the strategy is implemented and evolves, while considering 4 key pillars.

  1. Organisational Alignment: Data Leadership​, Operating model and organisational design, Talent planning and recruitment, Communication strategy, Training and development plan​.

  2. Data and Tools: Architecture design​, Data governance design and implementation​, Data management processes and tools, Data warehousing design and development​, Data management and enrichment​​.

  3. Analytics: Requirements planning​, Toolset selection​, Analytics process development, SCV development​, Foundational analytics build, Automation of analytics routines​.

  4. Measurement and Reporting: KPI framework development​, Executive Measurement and decisioning tool​, Frontline reporting and visualisations​, Training and development.

Agile Data Strategy

For clients looking to make changes quickly, our agile data strategy approach compresses the traditional data and insights strategy plan into three weeks.  While we still aim to cover the same broad areas implemented in the waterfall data strategy roadmap, the effort here is on mapping out a pragmatic, agile plan and focusing on the value generation aspects of the quick wins.

Agile data strategy approach

Data Review: An assessment of the quality of our client’s data assets in terms of what data they have and the quality of this data. 

As-Is: Based on the data review, we identify the current success and challenges of the current data usage and establish the current state of play in relation to the perception of data analytics; people, company and data culture; as well as performing a data gap analysis.

Opportunity: With the data that the client already has and in its current state, we can identify what opportunities there are to implement an agile data and insight strategy that creates value for the business and importantly relative to the investment.

Quick Win: Over the course of the next few months, we are able to implement these quick wins, to demonstrate the benefits of a data and insights strategy and create change and growth opportunities quickly through data-driven transformation.

With a greater understanding of their data, we help our clients turn data into insight and establish a variety of growth opportunities for their business.

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