Data Optimisation

Discussion table

Learn from
Jeremy Gould
Jeremy Gould

Deputy Director of Transformation, Defence Digital

Contributor

He has more than 20 years experience around digital government.
Jon Roughley
Jon Roughley

Director of Data Strategy and Innovation for Experian UK&I

Co-Host

Jon is Director of Data Strategy and Innovation for Experian UK&I.  He is responsible for ensuring the organisation has its finger on the pulse of the ever-evolving world of data and the opportunities it presents.  The team works closely with all parts of the Experian businesses to explore how innovative solutions can harness new data assets that deliver benefits to consumers, businesses, and society at large.


Jon joined Experian in 2013 to lead strategy for the UK&I business.  He led the implementation of Open Banking, played a pivotal role in several strategic acquisitions, and implemented the Innovation Pathways methodology across the UK.  More recently he led a global data innovation team that developed credit scores based on telco and mobile data, launched a support platform for disabled customers and innovated with global business information sourced from the web.


Before joining Experian Jon held senior strategy and strategic change positions in a number of UK Financial Services companies, with a focus on life, pensions and investments.

About the session

How should the public sector organise and enhance its data to improve its usability, accessibility, and value - from data collection and storage to processing, management, and usage? Ensuring data quality - accuracy, completeness, consistency, and timeliness - is critical because the decisions made based on this data directly impact public services and the wellbeing of citizens. Poor quality data can lead to inefficiencies, misinformation, and potentially harmful policy decisions. Therefore, for public sector organisations, data optimisation is not just about maximising the value of data in decision-making but also about maintaining public trust and improving outcomes.

  1. Data Fragmentation: How can departments address the issue of data silos and integrate disparate data sources for a more holistic view?

  2. Data Quality: What steps are your organisation taking to establish a comprehensive data quality management process, including regular audits, validation, and cleaning procedures? How should we handle inconsistencies in data format, structure, or definitions across different systems to ensure comparability and usability?
  3. Completeness: What are the biggest challenges in keeping data up-to-date, and how can we fully capture and record data to ensure completeness and prevent distorted analysis?