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 to different people across the organisation
Minor upstream changes are time consuming due to the complexity of impact analysis
Analysing interesting relationships between data sets is not possible without IT intervention
Clients find issues with externally distributed data before it is discovered internally
It is unclear whether data quality is improving or worsening because it isn’t being accurately measured
Significant manual effort is spent on data reconciliations due to unreliable data distribution
Addition of new products is slowed by the need to set up the same reference data in multiple systems
Extending existing data infrastructure to support new business initiatives is slow and costly
Meeting each new regulatory requirement requires yet more data infrastructure with seemingly little reused from before
There is no ability to exploit the latest data technologies to gain competitive advantage e.g. machine learning
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 Consulting’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.