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:

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Business terms mean different things to different people across the organisation

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

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

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

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

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

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

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

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

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

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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 Digital has developed a Data Management Framework, focused on Data Architecture and Data Governance.

• Citihub Digital’s Data Architecture Model

• Citihub Digital’s Data Governance Model

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.