Demonstrating Value

Getting Started

Everything an organization does should tie to one of three universal value drivers

  1. Increase revenue and value
  2. Manage cost and complexity
  3. Support Risk Management and Compliance efforts, and increase confidence.

Data Governance efforts MUST tie back to one or more of these drivers.  And YOU must communicate how it does.

Demonstrating Value

Here are some of the ways a Data Governance effort can benefit you:

Increase revenue / value of assets

  • Improve the value of the company to those who would acquire it
  • Create “sellable” information products
  • Utilize information assets to make new sales
  • Utilize data to achieve new business capabilities
  • Better understand customers
  • Better understand product (and other) hierarchies

Reduce costs

  • Reduce duplicate data and its costs
  • Reduce duplicate data management processes (example: costs of data modeling, data administration, data quality)
  • Reduce likelihood of errors and associated costs (in software development, report development, information interpretation) due to lack of understanding of data or poor quality data

Support Compliance While Reducing Costs

  • Achieve compliance goals
  • Avoid cost of penalties associated with non-compliance
  • Avoidance of reputational hit (brand impact)
  • Avoid higher audit fees due to lack of confidence in “authoritative data”
  • Reduce management attestation/certification costs
  • Reduce costs of pre-audit testing

Support Impact Analysis

  • Increase ability to do useful impact analysis (by providing authoritative business rules, system of record information, and data lineage metadata)
  • Provide a capability to assess cross-functional impacts of data-related decisions

Help Align Efforts

  • Assist business teams (Business Continuity, Disaster Recovery, Security, and Privacy) to articulate their data-related business rules and requirements to IT, Architecture, and Data Management teams
  • Consider requirements and controls in an integrated fashion
  • Avoid “undoing” work or rendering controls invalid
  • Craft cross-functional accountabilities

Improve Data Repositories

  • Provide accountability and support for improving the quality of data in the repository so it can become an authoritative source of information
  • Reduce likelihood of architectural decisions that limit the organization’s ability to analyze its information
  • Increase ability to find authoritative information quickly

Improve Confidence in Data

  • Increase confidence in data-related decisions
  • Increase ability to make timely data-related decisions (this can affect time-to-market for projects and applications)
  • Increase confidence in data appearing in financial and management reports
  • Increase confidence in data strategy by providing a cross-functional team to weigh in on key decisions

Read Next:

Starting a Data Governance Program

A successful Data Governance program does not begin with the design of the program! Before you start deciding who goes on what committee, you should be clear about your program’s value statement. You should have developed a roadmap to share with stakeholders. Those...
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Focus Areas for Data Governance: Data Quality

This type of program typically comes into existence because of issues around the quality, integrity, or usability of data. It may be sponsored by a Data Quality group or a business team that needs better quality data. (For example: Data Acquisition or  Mergers &...
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Defining Organizational Structures

There is no single “right” way to organize Data Governance and Stewardship. Some organizations have distinct Data Governance programs. Others embed Data Governance activities into Data Quality or Master Data Management programs.

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Data Governance Program Phases

As you perform the activities needed to gain support and funding, remember that your program may plan to address multiple focus areas. Each new effort should be introduced using the seven steps of the life cycle. Even specific governance-led projects, such as creating a set of data standards, will want to follow the Data Governance Life Cycle steps.

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Establishing a Data Governance Office

Most organizations that begin a formal Data Governance and Stewardship effort need a support team to facilitate and coordinate activities of councils, stewards, and stakeholders. This support team may be individual contributors who have been doing this work informally...
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Engaging Stewards and Stakeholders

It seems like there are two types of Data Governance and Stewardship programs: Thriving ones, with highly-engaged stakeholders, and Ones whose futures are in question, since stakeholders and stewards are only sporadically involved or give only weak support to the...
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Implementing Change Management

Most organizations have string change management – or at least change control – mechanisms for technology. They usually have change management for software applications. They have change management for websites. And yet, many organizations do not practice structured...
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Assigning Data Ownership

One of the tenets of Data Governance is that enterprise data doesn’t “belong” to individuals. It is an asset that belongs to the enterprise. Still, it needs to be managed…

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Goals and Principles for Data Governance

What do you want Data Governance to accomplish?  Regardless of the focus of your program, chances are you hope to accomplish the following universal goals for Data Governance programs: Goal – Enable better decision-making Goal – Reduce operational friction Goal –...
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Focus Areas for Data Governance: Privacy, Compliance, Security

This type of program typically comes into existence because of concerns about Data Information Security controls, or compliance. Compliance, in this context, may refer to regulatory compliance, contractual compliance, or compliance with internal requirements.This...
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