Standardizing Data Management and Data Governance Services (A First Look)

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Click to learn more about author Tejasvi Addagada.

The needs of organizations mostly financial services and health care providers are seeing an increased necessity to manage the data as an Enterprise asset, as regulations and policy around data are quickly evolving. Although regulation is one of the primary drivers, firms have started to appreciate the importance of managing their data as an Enterprise asset. This is to embrace other drivers of growth, regulatory compliance and operational excellence. With the advent of pervasive disruptors such as Internet of Things (IOT), Machine Learning, Semantics, and Big Data, organizations have started to realize that fit-for-purpose and trustworthy data is required to derive powerful insights for decisioning.

Based on a recent survey, 33% of such firms are actively governing their data today. Most of these firms are also aligning directly to their corporate governance principles which brings a two-pronged benefit. Further, the business environment is undergoing constant transformational changes with impressive business models. This in-fact is enabling the objective of having to channelize growth in markets. Thus, Data Governance has come to be a new normal in most enterprises. With the set-up of chief data offices, organizations have come a long way from having technology ownership of data to business ownership.

I saw that in the firms that I worked with, the current needs and organizational structure plays a key role in accommodating a formalized data management function. A question often arises, “How serious are they in actively managing their data?” Some of the challenges that an asset management firm is facing while I was working with their Chief Data Office are:

  1. The organization had a unique challenge with trusting its data or leveraging data produced by a different division or a third party. The organization never had a Data Quality as a major challenge.
  2. There was a lack of Data Governance structure which aligns data ownership to business divisions while ownership lies with technology division.

And often, I give an analogy to help people understand the importance of governing the data as an Enterprise asset. In the 1800s, horses were trained to pull carriages from London to Farringdon, which is to say that these trained horsemen were in the business of transferring people from place to place. This active process should be managed so that passengers reach their destination on time, a service that also includes the proper management and care of the horses. The outcome of this process is evident: the passenger reaches the desired destination, but the benefit of the service is that the rider reaches the destination on time and remains safe and secure upon arrival.

There also needs to be an oversight over all the horses, drivers, and carriages owned by the horsemen (or perhaps borrowed from a third party). Next, there is the need to manage the risks, which includes safety and security issues as well as missed timelines, all of which affects the profitability of the enterprise. Think of all the details involved that the company running the travel business is responsible for: establish guideposts all along the way to assess, monitor, and guide the drivers in scenarios of severe weather, faulty carriages, and alternate routes to take when inclement weather interferes with the standard route.

My approach here was to analyze the data strategy that provides context in defining and analyzing the data management and governance services. The Strategy Analysis for Data Management and Governance emphasizes defining the current, transition and future states of Data Management and Data Governance. The current state analysis should cover the strategic thinking in Data and Business Analysis as well as the discovery of solution scope to enable the organization to harness value.

Along with that, I suggest performing a cultural assessment to:

  1. Identify required cultural changes apart from the required data capabilities.
    • Stress the need to understand current people capabilities including roles, responsibilities, structure and relationships.
  1. Identify whether stakeholders:
    • Understand the rationale for the required future state control environment.
    • Know the value delivered by the future state control environment.
    • Can identify the attitudes of stakeholders as they relate to the current needs.
    • Understand the existing capabilities and processes.

It was a great experience with a UK based bank, which on the flip side grew in-organically over the past few years through acquisitions and mergers that resulted in data redundancy from duplicated business processes. Their unique challenges are as below–

  1. Mortgage products, for instance, have distinct processes, leveraging the same data that is duplicated across the landscape while needing to maintain the products separately.
  2. The costs were plummeting in maintaining redundant not-fit-for-purpose data and improving its quality.
  3. At the same time, this was getting the attention of regulators and the customers due to interrupted services.
  4. The unique challenges of a complex data landscape, commissioning redundant spends to maintain this landscape and redundant data and not able to leverage data to the rapidly evolving business models.

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