How to implement the Data Management Flywheel methodology

Publish date:

Restructuring the scattered data landscape

Data is the lifeblood of modern financial institutions. However, the need for a uniform data landscape is greater than ever before. Ever increasing regulatory requirements pressure financial institutions to restructure their scattered data landscape. The Data Management Flywheel can help you.

The Data Management Flywheel (DMF) is a best practice methodology that can be applied to streamline and harmonize data sourcing processes, rules, interface formats and transform logics. Choosing the DMF methodology is the first step to overcome the functional and technical challenges related to data sourcing and transform your data landscape. From my experience however, the implementation of such methodology comes with major challenges. This article addresses how to overcome these implementation challenges.

Implementing the DMF methodology comes in three phases

Firstly, the as-is situation is analyzed in the pilot phase, showing the current state of the data architecture in the organization. A maturity assessment is thereby conducted. The three dimensions “Data Sourcing”, “Governance”, and “Data & Systems” are evaluated to make transparent how the architectural landscape looks like, the way the data is currently stored in different sourcing systems and accessibility and availability of data.

Secondly, the design phase is started, consisting of a series of workshops to detail the assessment and prioritize the improvements on the three dimensions. In this phase, a transformation map is created that shows the complete transformation in one overview.

 

The third phase is the transition phase, where the deliverables in the transformation map are being implemented. Transformation of the Finance and Risk landscape is a process that will take the necessary time depending of the data landscape maturity and can therefore vary from a couple of months to two years. In the end, financial institutions will have efficient data sourcing based on the Data Management Flywheel.

In our experience however, the actual implementation consisting of all these transitions comes with some challenges.

Close minded business requirements

Regulators are more and more interested in getting access within the data chain. Instead of receiving data at a certain point, regulators would like to have direct access to source systems. This means that organization needs to develop a new mind-set. Defining and communicating the business requirements is a very important early step of the implementation. Unfortunately, this crucial step is sometimes achieved rather quick and dirty. People defining the business requirements tend to think they know what they want. But knowing what you want is entirely different than knowing what you need. From my experience, business requirements are set up based on functionality that the business already has today. However, transformation requires forward vision. Expected requirements regulatory and the impact of the further increasing requirements for your organization. The financial institutions are required to govern and structure their data in a structural and accessible way, foreseeing what requirements are needed for the future by thinking out of the box.

Ambiguous data fields and functionalities

Different departments can have different interpretations of business concepts. The definition of certain financial products and their characteristics can vary depending on who you ask. This ambiguity does not only exist between business and IT, but can also exist between different departments in the business itself. A single source of truth is needed with standard formats for all business concepts. Firstly, a business glossary or data dictionary needs to be created with a global data definition for every piece of individual data. Once finished, the glossary will be the language in which the financial institution can communicate about their data.

Misalignment between business and IT

Mutual incomprehension between business and IT professionals is a common challenge for many organizations. Misalignment between both professionals creates organizational and technical risks. For implementation of the DMF methodology, I experienced that alignment alone is not enough. Organizations have been used to work in silo’s. In order to create the right mind-set, having a good understanding of why is crucial. The teams need to have a clear understanding of the vision behind the work. Bringing the right people together with the right content in important to create this understanding. To underscore the importance of the commitment, important stakeholders should have their commitment written down on paper. The Capgemini Accelerated Solutions Environment (ASE) is a custom-built collaborative workspace where this commitment can be created.

Gerelateerde posts

Business Analytics

A glimpse into the realm of unlocking self-service analytics for business

Peter Biltzinger
Date icon 2 maart 2018

Why self-service analytics? Well, enterprises are looking for efficient and effective use of...

cookies.

Door verder te navigeren op deze website accepteert u het gebruik van cookies.

Sluiten

Sluit cookie informatie