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Christophe Marcant

With increasing demands to reduce time-to-market, while at the same time keeping up with suppliers, distributors and end users, manufacturers often find that their growing amount of business data is working against them rather than for them. The challenge to manage critical organizational data is growing fast, and manufacturers are embracing data governance (DG) strategies to protect the integrity of their valuable enterprise assets and optimize their master data management (MDM) and product information management (PIM) initiatives.

Data governance is the overall management of the availability, usability, integrity and security of the data employed in an enterprise. It is increasingly important as data volumes grow and organizations of all sizes are challenged to ensure a single version of the truth exists for each of their critical data domains. Comprised of both people and processes, a sound data governance program includes a governing body or council, a defined set of procedures and a plan to execute them.

Understanding the Value of Data Governance  

As Michael Lock of Aberdeen Research points out in this blog, data governance and strong oversight isn't just about checking a box on a corporate compliance report, as trust among end users leads to a higher overall level of satisfaction. 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. According to Lock, the process carries a variety of benefits for manufacturers, such as:

Tracking: Even data that is considered to be somewhat stable such as names, lead times and product specifications are subject to change over time. Manufacturers need to understand how data changes over time as this is critical in the event of a product recall, or to comply with pending mandates such as the General Data Protection Regulation (GDPR). This new regulatory standard defines how businesses that process personal data of European Union citizens collect, store and use that data. Obviously, it will require a high level of data quality and well-organized data processes to comply. In fact, companies combining MDM with strong governance policies experience 2.3 times fewer instances of inaccurate or incomplete data, according to a 2016 Aberdeen report by Lock.

Usability: By creating a trusted data foundation, manufacturers can improve the speed in which the data is processed to improve decision-making. According to the Aberdeen report, companies using governance and MDM are 65 percent more likely to see an accelerated time-to-decision.

Effectiveness: The ability for manufacturers to apply data in a business context is one of the most impactful benefits of DG. In fact, the same Aberdeen report found that companies combining MDM and governance are 2.2 times more likely to see year-over-year revenue growth greater than 20 percent.

By applying DQ in concert with MDM, manufacturers can produce higher quality data, achieve much-needed visibility into data lineage and ensure data is delivered to the right people at the right time.

Ten Steps to DG Success

Determining the set of processes that ensures how important data assets are formally managed can be daunting and requires buy-in from various stakeholders. To help elevate potential pitfalls, manufacturers should:

  1. Develop a mission statement. This will act as the vision of the company, and ensure the whole organization understands the goal of the program and each person’s role in achieving success.
  2. Put in place a documented resolution process. Much like an insurance policy for the unexpected, having a documented resolution process in place helps the organization prepare for the unexpected.
  3. Control access to business data. While organizations are inclined to trust employees explicitly, it is advisable to control access to business data to protect it, granting access on a “need to know” basis.
  4. Implement a business risk register. Setting up a business risk register is a necessary step and a good contingency plan, as it means rational and strategic decisions are made, even in times of crisis.
  5. Establish good business processes. Ensure you build a solid data model and document business processes diligently. Keeping these up to date will result in consistency in serviced level provision.
  6. Perform data quality assessments. Data quality audits can be time consuming, but are a valuable activity to certify accuracy.
  7. Build a business case. Invest time in building a business case, as this will pay off in the long run should the unexpected occur.
  8. Integrate data governance in employee’s goals and objectives. This will help employees motivate themselves and propel the business to achieve wider targets.
  9. Train the workforce. Training is necessary to ensure that employees fully understand what is required of them. This is particularly important for those who have no previous knowledge or exposure to data governance and why it may benefit them.
  10. Educate the workforce on the data governance program. Knowledge is power, and people react best to a change in work practice when it is clearly articulated.

Never Underestimate the Importance of the People

A successful data governance strategy involves many factors, including careful, upfront planning combined with appointing the right people and the appropriate tools and technologies. Careful consideration needs to be paid to ensure proper data ownership, as inconsistencies will undoubtedly occur as data elements and types are shared among business users and across data silos. Designating data stewards, who are the people responsible for the management of the data and the respective attributes, will help here and is vitally important to the long-term success of the data governance program. Lastly, leverage the data governance team to create a solid data governance framework that addresses data inconsistencies across different departments and adheres to the data quality needs of the organization. 

In fact, one of the most critical and often overlooked considerations to a successful global implementation involves the team responsible for managing the initiative. Data governance initiatives improve data quality by assigning a team responsible for data accuracy, accessibility, consistency and completeness. If you already have a data governance program in place, you may have already appointed a chief data officer (CDO). The CDO has the ultimate responsibility for data within the organization, but he or she requires basic education about data protection. The CDO also tends to oversee the team responsible for data governance and is an active member of the team. Other participants usually consist of executive leadership, project management and data stewards.

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 will be responsible for two complex activities:

  • Change Management – Enterprise data must first be 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.

Calculating Operational Risk

Once your data governance team is in place, it’s important they determine the current state of the organization’s data governance program and deliver a future state plan. Once complete, the team is ready to create a strategy for improving 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.

More specifically, data governance can be achieved doing the following

  • Build a clear vision and scope for the data governance initiative, so you can ensure that the organization can 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.
  • Design a data governance program that is suitable for managing the defined standards. This includes assigning roles and responsibilities for processes used to manage activities, such as change management for standards, and changes to any external process that affect the organization’s ability to govern, including the IT project management process.
  • Engage a data owner to own standards and to build/oversee the data quality roadmap.
  • Build the data quality roadmap and document current quality levels. Measure it against the requirements and propose actions to bridge the gap and/or maintain good quality.
  • Populate remaining data governance roles to operate ongoing compliance. Measure and manage activities identified in the data quality roadmap.

Instituting a set of DG processes is increasingly important in today’s digital climate. By applying formal data governance, manufacturers can ensure that data is formally managed and trusted and that people can be made accountable for any adverse event that happens because of low data quality. Following these steps will help you turn your overabundance of data into an asset working for your organization’s benefit, rather than an obstacle to reaching your business goals. 

Christophe Marcant is senior vice president of strategy and communication for Stibo Systems.

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