Engaging People in Enterprise Data Management Transformation

Only available on StudyMode
  • Download(s) : 132
  • Published : April 3, 2013
Open Document
Text Preview
Engaging people in Enterprise Data Management Transformation

Santhakumar Rajendran, Senior Information Management Consultant

Introduction

Large scale Enterprise Data Management (EDM) transformation initiatives demand significant changes in the organizational structure and policies. Fundamental changes in the organizational culture and employees’ perception of ‘data’ are vital for the success of the transformation effort.

This paper sheds light on the key components of enterprise data strategy, employees’/people’s data management maturity levels and the process of implementing people-specific organizational changes as a part of the transformation initiative

Business Case

Every organization, regardless of its size or wealth, has challenges with its data. At the same time business value of well managed data is widely recognized by all. Until a few years back, when the focus was primarily on implementing process automation systems, organizations managed to handle data issues with tactical solutions as and when required. These temporary fixes do not work anymore due to multiple reasons:

• Volume of data to be managed by the organizations has increased enormously in the past few years, making manual handling of data virtually impossible or very expensive at the least. For instance, earlier we used to have clerical users manually copying sold quotes from the pre-sales information systems to the post-sales systems even in large organizations. This no longer holds good.

• Data fixes need to be applied on multiple systems for the same data item, since the systems are closely interconnected and data gets transmitted across systems much faster and very often in real time.

• Data structures designed in the past have exhausted their scalability. Increasing complexity & dynamic nature of business and aggregating regulatory requirements demand more reliable and agile data infrastructures.

• Modern applications of data like business optimization, predictive analytics, etc. demand data with consistent and improved quality.

We cannot just assign a team in charge of "fixing the data" and expect the data to remain clean. Everyone in the organization needs to understand how data is created, integrated, transformed and used. Without putting the current data setup in order and establishing a common protocol for data management, it will be unwise for enterprises to keep adding new information systems.

Enterprise Data Management Strategy

Organizations need to have a minimum set of data management capabilities to be able to achieve their business goals. Enterprise Data Strategy (EDS) defines this targeted maturity level. An associated implementation roadmap details out how organizations plan to transform their data management setup from current level of maturity to the target maturity level.

Typically EDS can be categorized under three streams, with actionable implementation roadmap for each of them:

• Technology: Overall technical infrastructure including common database management system, tools for data integration and reporting - standardized across all the departments, etc.

• Business Process: Data gets created/ manipulated during the implementation of business processes. Well defined and integrated business processes would contribute to creating data with higher integrity and quality.

• People: After all, it is people who create, use and maintain data.

− Data creation: People on the ground, like the underwriters, tellers, customer service agents, etc. create majority of the business data. In the current ‘self-service’ era, even the end consumers create significant part of the data owned by organizations. Middle managers and to a small extent senior managers contribute to the creation of business metrics and key performance indicators.

− Data usage: Data then gets used by employees at all levels – by the associates for...
tracking img