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Four Steps to Drive Digital Transformation in Your Bank

Four Steps to Drive Digital Transformation in Your Bank
If your Bank has been in “transformation mode” for a decade or more – and talking about cost control for longer than that - you must be asking yourself, why are these projects still costing so much?

Significant sums have often been spent on establishing best practice KPIs without corresponding attention to analytic capabilities and reuse of data. Teradata’s experience suggests that there are several steps and traps in driving transformation:
 
  • Applications are acquired to deliver profitability calculations.
  • A fortune is spent on reconciliation and there are still gaps.
  • There is no business case for fixing all the underlying data in one project.
  • Prior big enterprise data projects failed to deliver value.
Does this sound familiar? Let’s look at each point in a bit more detail.

Buying software is an easy solution

Organizations must be able to understand the root causes of their business challenges via repeatable analyses – not only one-offs which may take months to deliver. This sort of problem is hard to solve, and software vendor promises are seductive, so most Banks have an application driven approach to reporting and analysis:
financegraph.png
The problem with the ‘quick fix’ of a software package is that all the white boxes above represent manual and often duplicated activities, which are repeated for every reporting and analytical activity shown in orange.

This can’t continue: new requirements drive so much additional manual work that it becomes impossible to deliver reports and analysis in a timely and efficient manner.

Reconciliation is killing progress

There is something of an obsession with reconciliation in many Banks that interferes with both execution and subsequent change.

The existing analytical architecture for Finance, such as that above, require more and more time and effort related to reconcile both horizontally and vertically to ensure compliance with regulatory requirements (e.g., BCSC239).

Teradata has deep experience helping Banks and their Finance departments improve analytical capabilities. Our experience is that it requires a mindset change at the CFO level to get beyond an application-driven approach.

The business case for fixing data

A digital-driven Finance department must have a different view on how to make data available for reporting and analyses. The main principle is data re-usability.

This drives a new requirement: not for massive, expensive and – frankly – boring data transformation programmes, but something more like a ‘data levy’ on each new project that allows investment in the existing underlying data foundation. It cannot be optional and dropped when project finances are overrunning.

Targeting a firm data foundation

There does, however, need to be a vision to drive this more incrementalist approach. The figure below shows what a Finance Foundation includes from a conceptual point of view.
financepic2.png
“Data” means all relevant data at the most granular level for customer, product and activity profitability must be available approaching real-time.

“Validate & align” requires that all the activities done per “silo” is now done increasingly across all the data based on commonly defined definitions and business rules.

This is not enough: a customer (or multi-dimensional) profitability model must be defined and developed. With a multi-dimensional profitability model based on the most granular data, we now target a completely aligned data set with the following characteristics and benefits:
 
  • All data reconciles back to source, in real time and in an automated way.
  • All data transformations and reference data are defined and documented.
  • The Foundation provides data for other reporting and analysis activities across Finance and Risk.
  • All data is ready for any type of forward-looking activities (e.g. forecasting, modeling, what-if, etc.).
A digitally-driven Finance team holds to this vision, but pragmatically recognises that this is a journey that matches incremental value with incremental investment. No single project should be tasked with delivery of the whole.

Teradata has worked with many customers who have changed their mindsets and made progress on a data driven foundation. This experience reinforces progress which is closely aligned to the original value objective of the transformation programme – and we’d be excited to help you to deliver success.

Without the vision that we spell out here, the next regulatory requirement will once again demand reconciliation across your silos and you’ll simply never deliver within a pragmatic budget. Forward looking CFOs are implementing this today and focused on supporting the transition to the Digital Bank of the Future – more of which we'll cover in our second blog.
Portrait of Michael Ingemann

(Author):
Michael Ingemann

Michael Ingemann is the Practice Partner for Financial Management at Teradata International. Michael started out as an auditor in Coopers & Lybrand (now PwC) in the early ‘90s and has ever since worked in a variety of senior finance positions in different industries (Oil & Gas and Airline). Since 2002, he has been working as a senior management consultant delivering all kinds of finance transformation projects for companies in Europe and Asia, where optimization of the Finance department and enhancing their analytical capabilities, via usage of all data, have been the key elements. At Copenhagen Business School, Michael has earned an HD in management/financial accounting (HD(R)). In addition, also from Copenhagen Business School, he has also earned an Executive MBA in General Management.

View all posts by Michael Ingemann
Portrait of Stewart Robbins

(Author):
Stewart Robbins

Stewart is an Industry Consultant with Teradata with about 30 years’ experience in analytics and insights activities, driving commercial value from data. He has spent several years consulting, whilst previously running insight and analytics teams at Barclays Bank and E.ON. He has extensive experience in data analysis, modelling, segmentation, market research, analytical implementation, test and learn, local marketing and data warehousing and has worked with large Banks, a major car manufacturer, public sector organisations and even with a facilities management company.
  View all posts by Stewart Robbins

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