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:

Focus Areas for Data Governance: Policy, Standards, Strategy

This type of program typically comes into existence because some group within the organization needs support from a cross-functional leadership body. For example, companies moving from silo development to enterprise systems may find their application development teams...
Read More

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...
Read More

Focus Areas for Data Governance: Architecture, Integration

This type of program typically comes into existence in conjunction with a major system acquisition, development effort, or update that requires new levels of cross-functional decision-making and accountabilities.What other types of groups and initiatives might want...
Read More

Choosing Governance Models

It’s important to define the organizational structure of your Data Governance program. But before you can do that you have to define your governance model at a higher level. You need to consider what types of decisions your governance bodies will be called upon to...
Read More

Funding Models: Funding Data Governance

The DGI Data Governance Framework addresses funding two ways: Obtaining funding and support is a phase in the Data Governance Life Cycle Funding is part of one of the components of the framework. What type of funding is needed? Data Governance programs need to...
Read More

Setting Governance Roles and Responsibilities

Who does what in a Data Governance program? First, a group of individuals (or a hierarchy of groups) representing a cross-section of stakeholder groups makes a set of rules in the form of policies, standards, requirements, guidelines, or data definitions. (Or, they...
Read More

Governance and Issue Resolution

One of the three most important jobs of a Data Governance program is to help resolve data-related issues. These may be conflicting data definitions, data usage concerns, or problems with how data is sourced, how it is integrated, how it is protected, or a myriad of...
Read More

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.

Read More

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…

Read More

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...
Read More