High-quality product and customer data are the lifeblood of any business. Enterprise systems, including ERP, supply chain management, CRM, and e-commerce all rely on complete, accurate, and consistent data to drive operational performance, sales conversion, and customer service. But in most organizations today, data quality remains a major challenge. Gartner pegged the average cost of poor data quality to an organization at $8.2 million a year, with 22 percent of respondents calculating their annual costs at $20 million or more. In a similar study, Aberdeen Research Group reported that “best-in-class” organizations were “three times more likely than other organizations to adopt data quality tools” and that those tools led to significantly more accurate and usable information. One thing is clear: Integrating product and customer data from a wide variety of different formats and data structures without considering data quality not only is costly but also can have a disastrous impact on the enterprise systems that rely on that data.
Poor quality data can affect nearly every area of a business. For example, goods can be delivered to the wrong address or customers can receive goods that don’t match the description on the Web. However, there are other, more subtle effects of poor data quality that are far more damaging to the business: missed opportunities to upsell to a customer, not being able to negotiate purchasing discounts, and losing web sales because of inaccurate sizing data are just a few examples. In fact, Gartner reported that, “Information governance and master data management programs are central to an organization’s success in assuring business outcomes and increasing business value from reusing enterprise information assets.”
Companies are addressing the need for improved data quality by enabling line-of-business professionals to manage their business information proactively using a combination of Master Data Management (MDM) solutions with sound data governance processes. This creates a positive ripple effect and a distinct competitive advantage in all downstream systems that rely on complete, high quality, and timely master data.
Data governance continues to be a hot topic these days but, despite all of the articles and whitepapers dedicated to the subject, it seems that business leaders still are not clear on what data governance actually entails. Some of the confusion may revolve around the word “governance” itself. Instead of getting hung up on the word “governance,” one way to consider data governance is to think in terms of quality, which is the fundamental aim of any data governance initiative and exactly where business leaders should be focusing their attention.
Formalizing your governance is a process that’s often overlooked, as business users often perceive that other members of the organization are responsible for assuring data quality. Much of an organization’s operational data already are part of an active management process but, to a large extent, the focus is on quantities and values. Areas that tend to be overlooked the most involve the reference or master data that drive many of the organization’s actual business processes. Data governance aims to correct this by establishing formal management responsibilities for the quality of this data.
The key to establishing a solid data governance foundation is to shift from a reactive approach to a proactive approach. It’s common to adopt data governance after poor data quality results in a bad business outcome or when no one takes responsibility for an error. Having a formalized, proactive data governance approach ensures that somebody is clearly responsible not only for fixing the disasters but also for reducing the likelihood of one occurring.
Before embarking on a data governance initiative, it is important to understand the options and some common areas of confusion:
Are off-the-shelf data governance tools available? Many tool vendors offer data governance solutions, and there are certainly tools that can help you govern data. These include tools that can enable you to store and communicate defined business rules as well as tools to measure data quality, identify compliance issues, etc. However, governance is really about the organization and the processes and responsibilities within which such tools can be deployed. For instance, a solution may support full lifecycle control of data, metadata management, data quality rules, and monitoring, but without the correct organizational support, the benefits of these governance tools will not be realized.
Is data governance the same as data maintenance? The two are very closely linked through data quality, but they are actually independent functions. Maintenance organizations tend to be aligned with specific IT systems or with specific business units within the organization, whereas data governance is about a common set of rules to which business members should adhere. The key to understanding the difference is to understand the two parties’ relationship to “standards.”
As part of a data governance effort, an organization should create standards by defining a set of best-practices or principles that will ensure the organization creates and maintains good quality data. It is the role of the data maintenance teams to comply with these standards, but it is the role of data governance to define the standards and to ensure that they are being met.
What exactly is data ownership? Data ownership can be a very confusing term. For example, it is common for businesses to split data responsibility according to geography. For instance, members of the UK sales force may manage all customer data based out of UK, whereas the U.S. team takes responsibility for those data in the States. However, the optimal approach is to create a single group within the organization to be responsible for all customer data instead of having siloed, single “data owners.”
The term “data owner” is actually a misnomer because, in practice, what is owned is not the data but the standards that guide users in how to achieve good quality. So while many departments may lay claim to the contents of the data, it is the data governance group itself that owns the structures and the quality rules.
What constitutes a data-governance–oriented organization? When viewed at a high level, data governance professionals perform two activities. But, in practice, these two activities can be very complex and can require a network of resources to achieve them. Specifically, the data governance team is responsible for the following:
- Change Management. Once the organization has defined a set of standards and aligned its data to it, it is important that any changes to these standards be controlled. For example, if the company defined that all dates are stored in the UK format of Day/Month/Year, then it will be problematic if somebody wanted to change to the American format of Month/Day/Year. It is the job of the data governance team to assess the impact of any such change, confer with any relevant stakeholders, measure the costs and benefits of such a proposal, and then – if the change is deemed appropriate – to manage those changes across all affected areas of the business.
- Compliance. Wherever there are rules, there is a requirement for policing. It is the role of data governance to be that police force – to measure the organization’s compliance to any standards that it governs and to act to improve the level of that compliance.
Data governance is a methodology to exercise data control processes. It gets your team on the same page regarding data-quality issues and limits confusion and wasted time. Ensure that your data assets and MDM efforts don’t go to waste by taking the time to institute the proper standards and processes.
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