– Christophe Marcant, VP Product Strategy at Stibo Systems, says:
Master data is a key enabler of business optimization, and refers to organizational data such as product, asset, location, supplier and customer information. Solutions such as master data management (MDM) enable organizations to create a single point of reference for these data assets by combining processes, governance, policies, standards and tools. However, even with MDM technology in place, this data frequently changes and therefore requires ongoing data governance and maintenance.
In fact, data governance becomes even more essential as data volumes grow. Regardless of size or industry, nearly every company faces challenges when trying to ensure that a single version of the truth exists for its product and service information. Businesses must take a centralized approach to their data governance processes that support managing a central repository of master data with real-time integration and synchronization between business systems.
A Data governance strategy also improves data quality. Typically, the organization assigns a team responsible for data accuracy, accessibility, consistency and completeness. In fact, many data governance initiatives were derived from previous attempts to improve data quality at the department level, which led to redundant and contrasting information spread across multiple applications. Instead, data governance initiatives should be targeted at increasing the visibility of data across the enterprise, offering improved visibility to internal and external customers as well as compliance with regulations. But remember, data governance is a methodology to exercise data control processes; it gets your team on the same page, limiting confusion and wasted time.
So who should participate in your data governance effort? A successful team includes participation from both business and IT professionals, and is often comprised of executive leadership, project management and data stewards. In order to streamline your data governance efforts and, more importantly, take a structured approach to building a tailored data governance infrastructure, organizations should consider the following steps:
- Build a clear vision: Take the time to develop a clear vision and scope for your data governance initiative in advance. This helps to ensure your organization is able to meet goals and expectations.
- Define standards: Each standard should have a business rationale as to why it exists. It should also include defined benefits that can be achieved, definitions of what level of quality is needed to achieve the desired benefit – as it doesn’t always have to 100 percent – and metrics that will show that the benefits are being realized
- Design a data governance organization: Identify the team that is most suitable to managing the standards that were defined. This includes the roles and responsibilities for those governing, the internal governance processes that will be used to manage activities such as change management for standards, and changes to any external process that may affect the organization’s ability to govern.
- Engage a “Data Owner”: This individual will ultimately own the organization’s standards and will be responsible for building the “data quality roadmap”.
- Build a data quality roadmap: The roadmap will serve to document the organization’s current quality level, measure this against the requirement defined in the data governance standard, and propose actions to bridge the gap and/or maintain good ongoing quality.
- Populate the remaining data governance roles: Round out the data governance program by engaging additional resources to fill roles which are needed to operate the on-going compliance measurements, and to manage the activities identified in the data quality roadmap.
Considerations for a Successful Data Governance Effort
One of the keys to a successful data governance effort is to establish “authority”. When building a data governance effort, a critical question to ask is “What can we do when someone in the organization refuses to comply with our standards?” When no authority exists, you may find an emerging number of “local” standards and complex interfaces designed to manage the transition between areas of the business and the differing standards. As the number of standards increases, you eventually come to the point where there is no standard at all. This tends to be an issue with businesses that have grown through acquisition but have decided to manage these subsidiaries at arm’s length. Conversely, the most successful data governance initiatives are in the pharmaceutical industries where compliance standards are enforced by external agencies.
Money is another key issue for data governance. The budget required to start a data governance initiative is often managed by the project team. However, an organization must also take into consideration the on-going funding required for such an initiative. After-all, data governance requires continued operational funding for the roles defined in the organization. It also requires access to funds for data quality improvement projects which may be identified over a period of time by the regular compliance monitoring.
As a practice, data governance is not problematic but its application can be become both complicated and political. Therefore, it is highly beneficial to the organization to seek expert advice when designing the program. Look for cross-functional team members who know the organization and all of its peculiarities as they are the ones who will understand the true data governance challenges and benefits that can be realized. Building a clear vision for data governance with well-defined business benefits will not only get the appropriate organizational buy-in, but it will help design a governance program that meets the current needs of the business while ensuring it can execute and sustain it moving forward.
About the Author:
Christophe Marcant is Vice President of Product Strategy at Stibo Systems, a global leader in multidomain Master Data Management (MDM) solutions. Contact him at email@example.com or visit www.stibosystems.com.