Data Transformation: Unlocking the Value of Data

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The focus on data has never been more prominent. Since the financial crisis, banks have been facing an equation of an increasing number of global, regional and local regulations leading to reduced revenues and increased pressure on cost and capital.

Banks are therefore striving to find internal opportunities to change the balance. Data as the underlying constant, comes to the forefront.

A more transformational data model presents the opportunity to increase efficiency, reduce operational risk, cut costs to ultimately enable better decision making.

Four factors are shifting the emphasis of transforming data from a traditional ‘cost’ into a ‘value add’ business asset.

  • Increased efficiency through an increase in the integrity and confidence of data through a clear and well communicated data strategy, with resources deployed to value-add initiatives as opposed to remediation activities.
  • Reduced operational cost through utilisation of more accurate, complete, timely and higher quality data with accountable data owners across existing processes and systems.
  • Reduced costs through data harmonisation of an agreed data sourcing strategy, robust data management policy and a controlled data distribution approach. Further reductions in reconciliation iterations, as a result of better data, will lead to longer term FTE savings.
  • Enable better decision making through an increase in control of processes, reporting and financial modelling by using understood quality data to make sound business decisions and identify risks.

Banks can set about unlocking this value from their data through progressing along the key phases of the data maturity curve. The approach is relatively straightforward however the execution can be complex given the far reaching nature of data underpinning every function and process. Unfortunately to realise the benefits, there are no shortcuts to this regard.

The challenge is how to start.

Phase 1: Definition, Design and Governance – an agreed data strategy supported by an executive sponsored data programme & operating model with mandated data responsibilities for data governance, data quality and data architecture.

Phase 2: Management, Development & Testing – decreased costs through data harmonisation and a reduction in reconciliation iterations with an increase in control in reporting and financial modelling with quality data upon which to base sound business decisions and identify risks.

Phase 3: Analytical, Predication & Technology – harness the power of data and drive revenue generating ideas based on client or industry trading patterns, leveraged market technology to unlock value.

Banks that follow a robust and accountable data strategy from the beginning will be able to realise expected value further along the curve not only faster, but deeper. Once the foundations are in place the potential data capabilities quickly grow leaving banks with greater opportunities with assets they already own.

Daniel MeereDaniel Meere is managing director at Axis Corporate UK. As an accomplished leader having grown several consulting practices and businesses in the Financial Services sector.
08 Nov 2016
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