Data Strategy Development

Many organizations — even large, established ones — aren’t getting enough value from their data. The problem is very rarely a lack of data; it’s usually shortcomings in one or more of the following areas:

• Understanding how data and AI support the company’s overall business goals

• A clear operating model for delivering value from data and AI

• The right technology and data platform to make data easy to access, work with and understand

• Effective management of data to ensure that it is high quality, reliable, well-governed and properly secured

A clear data strategy that addresses the above areas and clearly links to overarching business strategies can be an invaluable aid to directing investment in data and AI more effectively.

Our data strategy development process does just this: We will work with your senior business leaders and data stakeholders to understand their objectives and how data can support them, and we will assess the range and quality of data that is available to identify key opportunities for your business that you may have missed. We’ll deliver an actionable strategy and framework for execution, focused on the following areas:

• Data Maturity Assessment: Benchmarking your organization’s data maturity against best practices in your sector and your peers within your industry and identifying key next steps for evolving your data maturity.

• Data Quality & Availability Audit: Analyzing the current state of your data – its completeness, quality, connectedness/fragmentation and usability, together with key recommendations to improve across these areas to raise the general utility and quality of your data.

• Opportunity mapping: Identification of key unexplored data opportunities that will drive value for your business, together with a summary of the necessary investments to take advantage of those opportunities.

• Key use-case development: Development of key data use-cases that support your organization’s business objectives, together with clear and measurable success metrics, and creation of execution approaches to use your data to achieve these objectives.

• Skills/organization gap analysis: Identification of key organizational or human resource blockers that will need to be addressed to execute on the data strategy, such as a lack of data engineering or analytics talent, or a fragmented organizational approach.