Manufacturers are constantly looking to create trusted master data, a key element of true business optimization. Organizational data, such as product, asset, location, supplier, and customer information, are only valuable when they are accurate, complete, consistent, and shared across an organization.
Achieving the trusted master data necessary to support today's business decisions is difficult, because most manufacturers deploy their data across multiple systems that continually aggregate, consolidate, store and maintain a tremendous amount of operational information.
Further complicating the matter is that this data has the potential to change frequently. In most organizations there are few clear-cut roles, processes or tools for protecting or enhancing that information as it moves across the enterprise.
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, manufacturers are embracing data governance (DG) strategies to protect the integrity of their valuable enterprise assets, and to optimize their master data management (MDM) and product information management (PIM) initiatives. Data governance is increasingly important as data volume grows and organizations of all sizes are challenged to maintain a single version of the truth for each of their critical data domains.
Signs of a Data Governance Issue
Data governance involves organizational change, and because of this, the value of the data and the level of risk a manufacturer is willing to take is likely to shift even after it is rolled out. In fact, many organizations only assess themselves once a year, which isn't frequent enough to be able to react to, and change, the organizational controls that need adjustment.
Organizations know that DG can add real value to their business, but it is often difficult to sell internally. This is because concepts like technical and business-related metadata, data quality, information models, data ownership, and the associated opportunity costs are not always immediately understood. As a result, they often lack funding for implementation.
Data governance has existed since the adoption of IT systems in the 1960s. But, where the initiatives were previously driven by IT with a technical focus based on programs, data governance is now typically approached in a more structured manner with a dedicated team and a high degree of interdisciplinary involvement. Today, data governance is no longer just hype, but an established term in the executive lexicon.
However, that doesn't mean that data governance programs are always successful. On the contrary, a lot of programs have failed. There are several key reasons:
Digital Transformation. Practically every successful business has built its success on data. Digital tools are disrupting the business environment and require significant changes in operations, communications, and supply chains; and these changes will only accelerate.
The adoption of new direct-to-consumer sales channels, the abundance of product options and the emergence of mass personalization have increased consumers' expectations and price sensitivity, leading to less brand loyalty than in the past. Data governance can make the digital transformation required to overcome these obstacles achievable.
The General Data Protection Regulation (GDPR). From May 25, 2018, the European Union is setting new regulatory standards for how businesses that process personal data of European citizens collect, store and use that data.
Obviously, it will require a high level of data quality and well-organized data processes to comply, meaning that GDPR will be nearly impossible to implement without an anchored data governance program.
Data Privacy. Data privacy is becoming a huge competitive factor and a major driver for organizations. Today, manufacturers are collecting increasing amounts of personal information about consumers, most of which is vulnerable to threats, accidental disclosure and theft, due to failure in appropriate design and usage.
This comes with a heavy price tag, as any negative publicity will have an adverse effect on the company's brand, not to mention the bottom line. Protecting an organization's reputation is the most significant risk management challenge these days.
Smooth Data Governance
Comprising both people and processes, a sound data governance program includes a combination of people, or in this case a governing body or council, a defined set of procedures, and a plan to execute those procedures.
A successful data governance strategy involves many factors, including careful, up-front planning combined with appointing the right people and the appropriate tools and technologies. This foundation of quality and trustworthy data, combined with an active and engaged group of users, is the hallmark of a company using a governed approach to MDM.
Start with people, as careful consideration needs to be paid to ensure proper data ownership, since inconsistencies will undoubtedly occur as data elements and types are shared among business users and across data silos.
Designated data stewards, who are the people responsible for the management of the data and the respective attributes, are vitally important to the long-term success of the data governance program. Be sure to create and leverage a data governance team to create a solid data framework that addresses inconsistencies across different departments and adheres to the data quality needs of the organization.
Next, determine a set of data governance policies and procedures. Start by building a clear vision and scope for the data governance initiative, to ensure that the organization is able to meet its expectations. Define standards and assign business rationale as to why each exists. Outline the benefits that can be achieved and what level of quality should be reached to realize the benefit. Create metrics that show whether benefits are being realized.
Finally, employ tools, such as MDM, to create higher-quality data and much-needed visibility into data lineage. According to an Aberdeen report, companies combining MDM and governance are 2.2 times more likely to see year-over-year revenue growth greater than 20 percent.
Contact: Stibo Systems, Inc., 3550 George Busbee Parkway NW, Suite 350, Kennesaw, GA 30144 770-425-3282 E-mail: email@example.com Web: http://www.stibosystems.com