Accelerating Decision-Making

Discussion table

Learn from
Aravindhan Rajasekar

Head of Data & Principal Architect


Aravindhan is an innovative digital & thought leader and a skilled architect with proven track record of successfully leading and delivering various digital programmes and projects for the last 16+ years, adapting established and best-practices architecture designs & patterns and innovative solutions using various enterprise architectural & management frameworks and through the application of versatile digital tools and technologies across industries and business domains.

As Head of Data  within Government Business Services, Cabinet Office, he is leading the Data Convergence (Shared Services Strategy for Government) that delivers insight-driven decision-making from accurate data for the whole of the Civil Service by applying definitions and standards achieving common business glossary, improving data-sharing within departments through integration, and between departments with interoperability.
Tom Wilkinson

Chief Data Officer


About the session

How can data be used to accelerate decision-making processes within the Civil Service? How do we improve the level of confidence in data-driven decisions, as reported by decision-makers and stakeholders?

This discussion focuses on establishing and developing the capacity to analyse data at pace, and empowering organisations to generate real-time insights and act without delay.

  • How can data be used to support more evidence-based decision-making within the Civil Service?
  • What kind of data infrastructure and governance frameworks are necessary to ensure that data is reliable, accurate, and fit for purpose? 
  • How can data be used to support more agile decision-making processes within departments? What kind of organisational and cultural factors are necessary to ensure that data-driven decision-making is integrated into day-to-day operations and decision-making processes?
  • What kind of data visualisation and communication tools are necessary to ensure that insights and findings are presented in a clear and engaging way?
  • What kind of data analytics and machine learning tools are most effective, and how can they be used to identify insights and patterns in large and complex datasets?