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Is your organisation suffering from “Data Management Deficit Syndrome”?

Effective data management is essential for organisations to operate efficiently, explore new market opportunities, and maintain regulatory compliance. However, there is plenty of evidence of ineffective data management across the financial services sector, and this can be very costly.

Here are some common examples of issues we see:

business terms mean different things

Business terms mean different things to different people across the organisation

minor upstream changes are time consuming

Minor upstream changes are time consuming due to the complexity of impact analysis

analysing interesting relationship between data sets

Analysing interesting relationships between data sets is not possible without IT intervention

issues with externally distributed data - - data management

Clients find issues with externally distributed data before it is discovered internally

data quality measurement - - data management

It is unclear whether data quality is improving or worsening because it isn’t being accurately measured

significant manual effort - - data management

Significant manual effort is spent on data reconciliations due to unreliable data distribution

addition of new products

Addition of new products is slowed by the need to set up the same reference data in multiple systems

extending data infrastructure is costly

Extending existing data infrastructure to support new business initiatives is slow and costly

meeting new regulatory requirements

Meeting each new regulatory requirement requires yet more data infrastructure with seemingly little reused from before

exploit latest data technologies There is no ability to exploit the latest data technologies to gain competitive advantage e.g. machine learning

Personal identifiable information

Personal identifiable information is stored in spreadsheets on shared drives raising the risk of sensitive data leakage

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In our experience, many of the above issues are caused by inadequate data management which leads to inefficient business processes, poor customer service, high IT cost base, restrained product innovation and potentially significant regulatory fines.

To help organisations install effective data management practices, Citihub Consulting has developed a Data Management Framework, focused on Data Architecture and Data Governance.

  • Citihub Consulting’s Data Architecture Model

citihub data architecture model - data management

  • Citihub Consulting’s Data Governance Model

citihub data governance model - data management

We use this framework to assess an organisation’s maturity across its data management activities, prioritising areas of weakness and specifying appropriate improvement plans.

Achieving an optimal level of data management is a journey which involves gaining an understanding of business data entities, implementation of data governance to control those entities, and the pursuit of a target data architecture which meets both operational and strategic goals.

In a forthcoming series of blogs, we will dive deep into the data management space and use this framework to explore challenging data management topics: data governance, metadata management, business intelligence, data in the cloud, data privacy, among others.


Find out how we’ve helped a global trading venue develop a strategy and roadmap that would deliver their data strategy programme objectives


Author

Simon Woolhouse

Simon Woolhouse

Associate Partner, London

An IT Change Specialist with 20+ years’ experience, Simon has led global development teams for tier 1 banks and other financial services organisations from the UK and Japan. His business knowledge around equity derivatives & structured products has most recently been applied to the analysis and project management of cross-functional data management programmes for many of Citihub Consulting’s clients.

simon.woolhouse@citihub.com