Data governance is the process of data assets being generated, enhanced and modified in an organization without losing value. Data governance is most simply defined as managing data with guidance.

These definitions enable the question: Do you prefer that your most durable strategic asset – data – be managed without guidance?

 It is hard for most organizations to answer yes to this…

Once organizations recognize that they already have some level of data governance good or bad, the conversation can address how best data assets are governed.

The people, process, policy and technology guidance that an organization provides to managing data is that organization’s data governance.  The quality of guidance an organization provides to managing data most often determines the level of success in retaining and growing the business and strategic value of the organization’s data.

Key Components of Effective Data Governance

Data Discovery

Data discovery allows organizations to audit and profile business and departmental data used by their organization in routine way. Organizations through data discovery should be doing an ongoing survey of their situation using key data.

Data Discovery needs to track to data production, master data management and organizational process assets:

• Discovering changes – tracking the upspring of new data and identifying data changes including anomalies and trends
• Measuring changes – data profiling, data usage, process asset usage and data or process growth/drop offs.

Leadership, Data owners – custodians/stewardship/evangelist

Identify leadership within the organization that is accountable for data governance. Political and business leadership are both critical to the success of data governance. Organizational readiness should be addressed and established.

Once leadership is in place, organizations will need data owners to steward and also evangelize data discoveries, definitions and usage. Data owners are the glue to an organization data governance. They help define how the data bridges departments, customers (internal and external) data touchpoints, and data set relationships.  It is their responsibility to facilitate the creation of a data dictionary and metadata for the data they own. Data owners work together determine the value, retention, refresh rate and audit needs of the organization’s data.

Data Production

The production of metadata and the data dictionary needs to be done in a way that can be universally accepted and accessible by the organization.  The organization as a whole needs to use and accept the data definitions and metadata. The data dictionary and metadata need to accommodate the user needs. Data production should grow to support a fully defined conceptual model for the organization starting first with the highest quality, most actionable, and strategic data.

Master Data Management

The goal of MDM is to improve the usability and integration of the most valuable data used across the organization. The need for MDM exists primarily because of legacy. It is rare thing when an organization can start out and know everything needed to support only a single definition of data and a single data dictionary within the organization. Organizations already have data and it is not consistent in its definition across the organization. Add to this growing access and need for including external data integration or use and we see elevation of MDM needs to critical very quickly. MDM supports the integration of disparate versions (both internal and external) of the same organization data, facilitates easier data production and improves ease of data usage. MDM provides the ability to integrate data typing and definitions and determine how these versions are used by the organization without losing value. It can best be thought of as a framework to facilitate data usability and integration.

Organizational Process Assets

Organizational processes assets often exist with varying degrees of data capture governing them.  As organizations grow the need to define and govern existing and new processes into functions and services grow. Data governance can provide guidance on the creation, capturing and managing of data around these function and services. For example guidance on managing data around a service level agreements (SLAs) or for the business function of recruiting looking at the bi-products of that workflow to capture data for business analysis such as conversion rates from prospect to recruit, retention rates etc.

Data Management and Data Governance

Data Management includes more than Data Governance. So it should be noted that Data Governance is a subset of Data Management. Data governance is impactful to all the other areas of data management and is at the heart of data management goals.

Data management includes

• Data Quality
• Privacy and Security
• Communications and Awareness
• Policy and Standards
• Technology and Infrastructure
• Compliance
• Data Governance

Data Strategy and Data Governance

To what level and how data assets are governed should be driven off of an organization’s data strategy. Data strategies are the upper most organizational guidance on data assets.  The data strategy should ensure the organizational data assets are optimized so as to produce the most value in support of organizational strategy. Business goals in essence can then be a means to shape data governance and ensure that data governance aligns with data strategy.

Aligning data governance to data strategy should mean data owners understand and own certain kinds of decisions and efforts needed to meet the organization’s data strategy.

Data Strategy is motivated by

• Improving organizational data
• Improving people’s use of organization data
• Improving organization data for business and strategic use

Determining Data Strategy effectiveness should include

• Measuring change using data
• Managing change using data
• Motivating change using data