Financial Services

Initiating a Data Governance Program in Financial Services

Previously, I discussed the components needed to design a data governance strategy. This post will describe in detail, how to initiate the data governance program.

The first step in implementing an enterprise data governance program is conducting a comprehensive assessment of all data related policies, processes, standards, repositories, and usage. Determining the major pain points will lead to a determination of program priorities.

data governance

The assessment will also indicate any data management components already in place, in some form. The funding, and therefore and staffing of the program, will dictate the scope and the degree to which aspects of the governance program can be tackled in parallel.

An examination of a firm’s reporting processes will reveal any weaknesses in data currency and data content, both in “dirty data” (incorrect values in fields), as well as missing, but required, data values. Compiling these reporting anomalies, along with the manual entries required to rectify the issues, will result in the beginnings of an enterprise data standard, as well as the front-end edits and validations required for conformance.

Data Stewardship

The basis for any data governance program will be a thorough analysis of reference and transaction-level data. Data analysis and remediation efforts should include:

  • System identification: Look across the organization’s various functions, product areas, and business units to identify all sources of relevant data.
  • Data analysis: Catalog the various places that relevant data exists and determine the quality of existing data and gaps concerning data governance requirements.
  • Data remediation: Create a plan for addressing data gaps and quality issues, as well as defining survivorship rules in cases of multiple sources for similar data.

With an understanding of the company’s data in place and a plan for addressing any shortcomings, the organization will have a solid foundation for complying with its data governance objectives.

 Program Management

Depending on a company’s size, product mix, and complexity, its data governance may require a multi-year, multi-functional program that could encompass dozens of projects. Given the scope and scale of affected institutions, most will already have several layers of project managers responsible for different areas of the business.

A strong data governance program will require the integration of different PMO groups, but also have a separate and distinct focus on the new regulation. An attempt to decentralize the management of this program and focus on individual functions will invite a disorganized response, which will likely lead to missed deadlines and incomplete requirements.

Using Waterfall and Agile methodologies, a program management office dedicated to data governance should be responsible for:

  • Creating or adopting a program charter to guide transformation activities
  • Creating or collecting individual project plans and generate an integrated program plan
  • Coordinating existing PMO groups affected by this regulation
  • Managing program communications down to project teams and up to senior management, as appropriate
  • Managing deliverables associated with individual projects and the integrated program
  • Supervising cross-functional “war rooms” as necessary

Existing PMO groups at the company will have an important role to play in organizing individual projects or group contributions to projects, but a standalone program management office reporting directly to senior management will be required to oversee a truly effective data governance program.

Requirements Gathering

For each of the technical work streams involved in a company’s data governance, requirements will need to be defined and vetted with key stakeholders across the organization. Requirements gathering activities for data governance include:

  • Validating existing data governance requirements and objectives
  • Working with stakeholders across business, technology, and operations to create business requirements documents and drive signoff
  • Translating business requirements into both functional and technical requirements to enable a seamless transition to development activities
  • Creating traceability matrices between various phases of requirements definition
  • Managing versioning and storage to avoid a disorganized document lifecycle

Companies will likely have in-house resources who are tasked with creating some or all of these requirements in a normal project. However, having dedicated resources who can write and manage requirements for an entire program will help ensure continuity between different functions and phases. As requirements grow and change, trying to address them with possibly dozens of different business analysts across groups could bring a program to a standstill. Companies will benefit greatly from having individuals who are tasked with writing requirements at a program level.

Testing Support

Each phase of a company’s data governance will have to undergo rigorous testing to ensure that all the objectives are met without disruption to business as usual. Activities include:

  • Test planning and execution
  • Defect tracking and reporting
  • Requirements traceability
  • Continuous integration

As with requirements definition, the program should have testing support resources that are dedicated entirely to the data governance effort to ensure consistency of methods, documentation, and reporting across all of the affected silos. Integrating existing testing teams into an overall test support structure will allow the company to leverage its skills with proprietary databases and software, while simultaneously enforcing a unified approach to testing all aspects of the data governance.

To ensure the success of a data governance program, it’s important to work with a trusted advisor with experience in data governance. This will help avoid missteps, save time and money, and ensure the success of the program. It’s also critical that key stakeholders understand the end-to-end data journey, how it impacts an organization, and how to go about implementing and managing a governance program.

To learn more about the components of a data governance program and the steps to take to remediate any weaknesses that can compromise the quality and security of a firm’s data; download our guide here  or click the link below.

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David Willner

David Willner is a business-focused information technology executive in Perficient’s financial services practice. His specialty is in transformation and data strategy programs. Before Perficient, he served as a managing director at J.P. Morgan Chase, senior managing director and chief development officer at Bear Stearns, and chief information officer, corporate comptrollers, at AIG. When he is not improving our client’s operations, systems, and data, he can be found playing guitar in his blues/rock band.

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