One of the key challenges faced by organisations of all shapes and sizes is ensuring that the data you hold is in good order. As data proliferates like never before, it’s easy to end up with a mix of legacy and strategic platforms, siloes of information, and a cottage industry of analysts doing their own thing.

Without consistent data definition and usage across the business, it can quickly become difficult to get a consistent view of how the business is performing.

Jaywing’s data management team can help you to define and refine your data strategy, ensuring that you have an efficient and consistent data repository with shared data definitions that are consistent across the enterprise.

We can work with you to develop warehouses, data marts, data lakes, visualization suites, and to build Single Customer Views which bring together a single view of the truth. We are immersed in banking-strength data practice, and can get your data in a state so that it’s fit for reporting, analysis, modelling, CRM, and a range of other uses.

Having spent over 20 years specializing in data management for banks, financial services and other high volume, highly-regulated environments, our team is adept at acting as the bridge between business teams and IT. We focus on how data is to be used, and take a pragmatic approach to recommending solutions that deliver early benefit. We’re technology-agnostic but have specialisms in key technologies such as Snowflake, Hadoop, AWS, SAS, GCP and Azure.

Data Management Consultancy

The data underpinning credit risk is one of the most important assets of any organisation that extends a credit line – and risk data management is one of the key tasks facing lenders across the UK.

When it comes to the data, the efficiency of storage, ease of access and logical definitions of the data are key to driving maximum commercial benefit from the information you hold.

However, many lenders need to balance storing as much data as possible against the need to use information in a simple and effective way. That’s why we make sure your data management for risk will support your key business requirements while also enabling opportunities to deliver benefits in the short term.

Effective data management for risk

We help our clients deliver risk data marts that provide one version of the truth, designed to ensure Enterprise transparency, auditability and executive oversight of risk.

Our data mart building process combines data accurately and efficiently – prerequisites for effective risk data management. To provide a platform for analysis and MI, we take steps to avoid data identification and reconciliation issues, including those that may arise from a merger or acquisition.

Our technical consultants can help bridge the gap between the IT and business, often removing constraints that otherwise slow down or prevent data issues from being resolved.

Here's a flavour of what we can help with


  • Data strategy –use, collection and storage of data aligned to business strategy
  • Interim CDO / Head of Data
  • Data interpreters – experienced technical/business hybrids
  • Flexible team – right people, right time, no wastage
  • Flexible approach – do it for you, do it with you, oversee you doing it
  • Independent review and audit of applications and data infrastructures
  • Data governance and data management best practice – logical and physical entity models, Target Operating Model (TOM), logical and physical entity models, data dictionary, data quality framework
  • Cloud first
  • Snowflake data infrastructure and ELT/ETL consultancy

Risk Data Management

  • Data infrastructure design, build and ongoing maintenance to support regulatory compliance including BCBS 239, IFRS 9, IRB and GDPR
  • Flexible implementation approach – do for you, work with you, oversee your resources
  • Data lineage, data auditability to enable historical analytics, rules based data quality framework configurable by business users rather than requiring IT developments
  • Specific applications include:
    o Reporting
    o Analytics and modelling
    o Model deployment
    o Model monitoring
    o Remediation
    o Collections
    o Provisioning
    o Pricing optimisation
    o Stress testing


  • Data infrastructures optimised for data viz tools including Power BI, Tableau, etc.
  • Strategic reporting
  • Operational reporting
  • Agile implementation (collaborative, sprint based, iterative) with early demonstrable deliverables
  • Technology independent
  • Data backfill
  • Automated data quality checks and proactive real-time alerting
  • Minimal requirements of IT resources