Companies today have deployed multiple systems that continually aggregate, consolidate, store and maintain a tremendous amount of operational information. Yet in most organizations there are few clear-cut roles, business processes and responsibilities for protecting or enhancing that information as it moves across the enterprise from design to engineering, to procurement, to distribution, to marketing, to eCommerce, and eventually to service and support. As a result, information often becomes replicated and fragmented, which leads to duplicate, conflicting, incomplete and erroneous information that hinders business responsiveness and decision-making.
As the challenge to manage critical organizational data grows, businesses are increasingly embracing data governance strategies to protect the integrity of their valuable enterprise assets and to get the most from their master data management initiatives. Andrew White of Gartner recently blogged about the data governance challenge, saying, “In 2012 and 2013, a notable number of end users were struggling to embed the work of governance and stewardship in normal, day to day work of business users. Many firms are continuing to struggle with this. It is perhaps one of the major challenges of MDM and ANY information governance effort in this decade.”
Designed to give control processes for data stewards and data custodians, data governance is more of a methodology than a tool. So when designing a data governance strategy, it is critical that organizations use a combination of data quality measures, data management and governance policies.
The Key Components of Data Governance
Data governance combines people, processes and information technology to create seamless management of an organization’s data across the enterprise. It also puts formal management responsibilities in place to ensure accountability and reduce the likelihood of errors.
Many people believe that data governance is just a tool and often confuse governance with solutions such as data quality. Verifying data quality, which is the foundation of a data governance strategy, is not a new concept. Organizations tend to monitor data quality regularly; however, they must be proactive. More often than not, organizations become aware of data quality issues after a problem has emerged, like shipping to an incorrect address or shipping products that are different from the product description on the website. Measuring the quality of operational data is already part of most data management processes, but the area of interest generally reflects only quantities and values. An area that receives fewer quality checks is the master data that fuels many business processes, driving the need for a formalized data governance strategy.
So while tools enable governance, it is really about the processes in which these tools can be deployed that count most. Master data is the heart of business optimization and refers to organizational data, such as product, asset, location, supplier and customer information. This data has the potential to change frequently; therefore, data governance and maintenance are required. Data governance is particularly essential as data volumes continue to grow, and organizations of all sizes are challenged to ensure a single version of the truth exists for their product and service information. What companies need is a centralized approach to their data governance processes and the ability to manage a central repository of master data with real-time integration and synchronization between business systems.
Data Governance Initiatives and Activities
Data governance initiatives improve data quality by assigning a team that is responsible for data accuracy, accessibility, consistency and completeness. This team usually consists of executive leadership, project management and data stewards. Many initiatives are derived from previous attempts to improve data quality at the department level, which leads to redundant and contrasting information spread across multiple applications. Data governance initiatives should be targeted at increasing visibility of data across the enterprise, offering improved visibility to internal and external customers as well as compliance with regulations. A successful data governance team should be responsible for two complex activities: change management and compliance.
- Change Management - Enterprise data is aligned to define standards. Next, the team must ensure that standards are maintained and changes are controlled. If a change is deemed necessary, those changes must be managed across all affected areas of the business.
- Compliance - The data governance team must regulate the organization’s compliance to any standards that it governs and act to improve the level of compliance.
Six Steps to Data Governance Success
Once the data governance team is in place, it must determine the current state of the organization’s data governance program and deliver a future state plan. After the assessment is complete, the team is ready to create a strategy for improving the company’s data governance practices and calculate the organizational risk probability. Knowing how data has been used, and possibly abused, in the past can help prevent data compromises. Every organization has this data readily available in loss and business reports. Collecting it, relating its meaning and studying loss trends can help transform risk management into a fact-based method for analyzing past events, forecasting future losses and changing policy requirements to improve mitigation strategies.
After all, data governance is about organizational change, and since companies are constantly experiencing change the value of their data and risk level is likely to shift. Many organizations only assess themselves once a year, which isn’t often enough to be able to react to and change the organizational controls needed for weekly or even daily changes to the data. More specifically, data governance can be achieved using the following six steps:
- Build a clear vision and scope your data governance initiative so you can ensure that your organization is able to fulfill it.
- Define standards and assign business rationale as to why each exists, define benefits that can be achieved and what level of quality should be achieved to realize the benefit (not always 100 percent) and create metrics that show whether benefits are being realized.
- Design a data governance organization that is suitable for managing the defined standards. This includes roles and responsibilities for processes used to manage activities (such as change management for standards) and changes to any external process that affects the organization’s ability to govern (such as the IT project management process).
- Engage a “data owner” to own standards and build a data quality roadmap.
- Build a data quality roadmap that documents current quality level, measures it against the requirements and proposes actions to bridge the gap and/or maintain good quality.
- Populate remaining data governance roles to operate ongoing compliance measures and manage activities identified in the data quality roadmap.
To drive operational success, organizations must manage their business information proactively. By taking the time to integrate master data management with sound data governance processes, companies can create a positive ripple effect on all downstream systems that rely on complete, usable and high-quality product, customer and supplier information.
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