Data Governance is the exercise of decision-making and authority for data-related matters. Also, is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe the 6 Ws:
- who can take
- what actions
- with what information
- under what circumstances
- using what methods.
Data Governance programs can differ significantly, depending on their . They can focus on Compliance, on Data Integration, on Master Data Management, etc. However, regardless of the focus adopted on Data Governance, every program will have essentially the same three-part mission:
- to make/collect/align rules,
- to resolve issues, and
- to monitor/enforce compliance while providing ongoing support to Data Stakeholders.
Typical goals of a Data Governance Program
- Enable better decision-making.
- Reduce operational friction.
- Protect the needs of data stakeholders.
- Train management and staff to adopt common approaches to data issues.
- Build standard, repeatable processes.
- Reduce costs and increase effectiveness through coordination of efforts
- Ensure transparency of processes.
Maybe you probably have other goals. It depends on the focus of your program. Some of these goals will address your general infrastructure and culture, such as identifying data stakeholders and their specific value propositions for solving data related issues. Others may be very specific, such as including the business in validating a certain percentage of core business data definitions.
Who is involved with Data Governance?
Data Governance is of concern to any individual or group who has an interest in how data is created, collected, processed and manipulated, stored, made available for use, or retired. People are called Data Stakeholders. Often, Data Stakeholders are OK with letting various IT Management and Data Management teams decide how to do the tasks. But sometimes, these activities require decisions that really should be made by groups of stakeholders according to an agreed-upon process for making those decisions; that’s when Data Governance comes into play. Such decision-making (and other activities) are facilitated and coordinated by centralized resources.
When do organizations need formal Data Governance?
Organizations need to move from informal governance to formal Data Governance when one of four situations occur:
- The organization gets so large that traditional management isn’t able to address data-related cross-functional activities.
- The organization’s data systems get so complicated that traditional management isn’t able to address data-related cross-functional activities.
- The organization’s Data Architects, SOA teams, or other horizontally-focused groups need the support of a cross-functional program that takes an enterprise view of data concerns and choices.
- Regulation, compliance, or contractual requirements call for formal Data Governance.
Where in an organization are Data Governance Programs located?
Data Governance Programs can be placed within Business Operations, IT, Compliance/Privacy, or Data Management organizational structures. What’s important is that they received appropriate levels of leadership support and appropriate levels of involvement from Data Stakeholder groups.
Why use a formal Data Governance Framework?
Frameworks help us organize how we think and communicate about complicated or ambiguous concepts. The use of a formal framework can help Data Stakeholders from Business, IT, Data Management, Compliance, and other disciplines come together to achieve clarity of thought and purpose.
The use of a framework can help management and staff make good decisions – decisions that stick. It can help them reach consensus on how to “decide how to decide.” That way, they can more efficiently create rules, ensure that the rules are being followed, and to deal with noncompliance, ambiguities, and issues.
How does an organization “do” Data Governance?
First, they decide what’s important to them – what their program will focus on. Then they agree on a value statement for their efforts. This will help establish scope and to establish SMART goals, success measures, and metrics. Next, develop a roadmap for their efforts, and they use this to acquire the support of stakeholders.
Once achieved, they design a program, deploy the program, go about the processes involved in governing data, and perform the processes involved in monitoring, measuring, and reporting status of the data, program, and projects.
Data Governance programs tend to start by focusing their attention on finite issues, then expanding their scope to address additional concerns or additional sets of information. And so, the establishing of Data Governance tends to be an iterative process; a new area of focus may go through all of the steps described above, at the same time that other governance-led efforts are well-established in the “govern the data” phase.
How much Data Governance do we need?
As little as Data Governance helps you meet your goals. The DGI Data Governance Framework can be applied to pervasive, “big-bang” programs. But it was specifically designed for organizations that intend to apply governance in a limited fashion, then scale as needed. All the 10 components of Data Governance described in the framework will be present in the smallest of programs and projects; the level of complexity will grow as the number of participants or complexity of data systems increases.
By standardizing your teams on the terminology and concepts described in the framework, you’re training your Business, IT, and Compliance staff to communicate with each other in a way that leads to realizing value from your data assets, managing cost and complexity, and ensuring compliance. A “act locally, but think globally” approach to Data Governance means your teams will be ready when it’s time to tackle large or complex data-related issues.
How do we assess whether we are ready for Data Governance?
It’s important to assess readiness for Data Governance before you move from your current state to a more formal approach to governance and stewardship. Why? There may be a valid reason why the current model is in place. Likewise, there may be a good reason why change could be detrimental to the enterprise, a particular program or project, or even an individual’s career. Red flags include:
- Refusal of business groups to get involved
- Refusal of leadership to sponsor a Data Governance effort
- The decision to implement a bottom-upprogram when the decisions and rules that must be implemented clearly must come down from the top of the organization
- The decision to empower a group (an outsourcer, partner, or team) to make data-related decisions for a data-related effort where they would benefit from NOT:
- Considering an enterprise view.
- Involving data stakeholders.
- Correcting data issues.
- Acknowledging data issues.
Advantages of Data Governance
In today’s world many businesses are growing rapidly and each day systems process so many transactions and create vast amounts of new data, some as simple as adding new customer, vendor, material, payments, credits and debits. While entering the data manually or digitally there is always the chance to enter incorrect or duplicate data and that can lead to a big data disaster for decision making and implementing new business strategies.
Companies are starting to realize this and see that their data must be cleansed and enriched to compete and to get the full benefit of their historical and present master and transactional data. To get a better handle on data as a strategic asset, companies are empowering their people as well as technology and processes to manage the long term quality of their data.
Data governance controls the quality of the data and provides consistent and trusted data that business users can rely upon to make critical decisions. Below are some advantages of data governance:
- Making data consistent.
- Improving data quality.
- Making data accurate, complete.
- Maximizing the use of data to make decisions.
- Improving business planning.
- Improving financial performance.
- Maximizing profits of the company.
A good data governance process allows companies to know that whether the data they are accessing is current or historical data, it will be reliable and usable for analysis. The benefits of data governance, such as those listed above, are an ROI the company can realize well into the future.