Sunday, July 26, 2009

Data Governance

As per recently sponsored survey of 50+ Global 5000 size businesses regarding their investments in “data governance” and the challenges they are facing.

  • 84% believe that poor data governance can cause: limited user acceptance, lower productivity, reduced business decision accuracy, and higher total cost of ownership
  • Only 27% have centralized data ownership
  • Fully 66% have not documented or communicated their program, and
  • 50% have no KPIs or measurements of success

What is Data Governance?

As per official definition Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of poor data quality. It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient.

Data Governance is the application of policies and processed that:

  • Maximize the value of data within an organization
  • Manage what data is collected and determine how it is used

Why Data Governance?

“You can't protect data if you don't know what it is worth.”

To know what it is worth, you have to know where it is, how it is used, and where and when to integrate and federate it.

Lots of time data governance initiatives are driven by a desire to improve data quality. However, they are also more often driven by external regulations such as Sarbanes-Oxley, Basel II, HIPAA and a number of data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations.

Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. When management understands the value of data and the probability of risk, it is then possible to evaluate how much to spend to protect and manage it, as well as where investments should be made in adequate controls.

A best practice within companies successfully implementing data governance is the collaboration between IT management and business leadership to design and refine “future state” business processes associated with data governance commitments. Moreover, a strong data governance function is very important to deliver reliable and usable business information.

Such a corporate data governance function can help businesses avoid these symptoms of poorly executing IT organizations:

  • Overly complex IT infrastructure
  • Silo-driven, application area-centric solutions
  • Slow-to-market delivery of new or enhanced application solutions
  • Inconsistent definitions of key corporate data assets such as customer, supplier, and product masters
  • Poor data accuracy within and across business areas
  • Line-of-business-focused data with inefficient or nonexistent ability to leverage information assets across lines of business (LOBs)
  • Redundant IT initiatives to re-solve data accuracy problems for each individual LOB

With an operational data governance program, businesses are more likely to benefit from:

  • Uniform communications with customers, suppliers, and channels due to the accuracy of key master data
  • Common understanding of business policies and processes across LOBs and with business partners/channels
  • Rapid cross-business implementation of new application solutions requiring shared access to master data
  • Singular definition and location of master data and related policies to enable transparency and auditability essential to regulatory compliance
  • Continuous data quality improvement as data quality processes are embedded upstream rather than downstream
  • Increased synergy between horizontal business functions via cross business data usage – e.g., each LOB is able to cross-sell and upsell its products to the other LOBs’ customers

What are components of the Data Governance Framework?

  1. Organizational Bodies and Policies
  • Governance Structure
  • Data Custodianship
  • User Group Charter
  • Decision Rights
  • Issue Escalation Process

2. Standards and Processes Data Governance

  • Data Definition and Standard(Meta data management)
  • Third Party Data Extract
  • Metrics Development and Monitoring
  • Data Profiling
  • Data Cleansing

3. Technology

  • Metadata Repository
  • Data Profiling tool
  • Data Cleansing tool

The Data Governance Structure
A Data Governance (DG) structure is defined based on the following roles and responsibilities:

Data Governance Council

Membership of this council consists of executives from various divisions who have an interest in the management of asset data. They are responsible for endorsing policies, resolving cross divisional issues, engaging the IT council at the strategic level, strategically aligning business and IT initiatives,and reviewing budget submission for IT and non IT related projects.

Data Custodian

Asset data is managed by the data custodian on behalf of Company A. It is responsible and accountable for the quality of asset data. The data custodian is responsible for resolving issues raised in user group meetings. If issues become political and impacts stakeholders from other divisions, they are escalated to the DG council level. They are also responsible for endorsing data management plan, endorsing data cleansing plan, ensuring data is fit for purpose, converting strategic plans into tactical plans, change management, and stakeholder management.

Data Steward

Data Stewards have detail knowledge of the business process and data requirements. At the same time they also have good IT knowledge to be able to translate business requirements into technical requirements. They are led by the Data Custodians and are responsible for carrying out the tactical plans. They also act on behalf of the Data Custodians in stakeholder management, change management, asset related information systems management and project management. They manage user group meetings, train and educate data users.

User Groups

Data stakeholders from various divisions are invited to the user group meetings. These key data stakeholders consist of people who collect the data, process and report off the data. Technical IT staff is also invited to these meetings so that their technical expertise is available during the meeting. This is also a venue where urgent operational data issues can be tabled. The data users are responsible for reporting any data related issues, requesting functionality that would help them collect data more efficiently, and specifying reporting requirements.

The Data Governance structure should have the business engagement with IT at the strategic, tactical and operational levels. This level of engagement ensures that IT and business are kept informed and IT initiatives align with the business data governance objectives.

Other Related Terms (Source CDI Institute)

Data Governance

The formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.

Master Data Management (MDM)

The authoritative, reliable foundation for data used across many applications and constituencies with the goal to provide a single view of the truth no matter where it lies.

Customer Data Integration (CDI)

Processes and technologies for recognizing a customer and its relationships at any touch-point while aggregating, managing and harmonizing accurate, up-to-date knowledge about that customer to deliver it ‘just in time’ in an actionable form to touch-points.

Master Data Integration (MDI)

Process for harmonizing core business information across heterogeneous sources, augmenting the system of record with rich content by cleansing, standardizing and matching information to provide high data quality in support of a master data management initiative.

Good Articles on Data Governance:


  1. Hello,
    We facilitate the provision of independent analysis to support expert testimony, regulatory or legislative engagements. Frequently, this work includes economic, financial and statistical studies of varying data analysis, technical and

  2. The only way to align the interdependencies of disparate people, processes, lines of business, and technologies is through a well- orchestrated Data Governance program with a metadata repository at its core.