In the Data Age, successful companies need a data governance strategy to ensure quality for all types of corporate data.
We're in the midst of a data revolution where nearly everything we do in work and play creates data to be collected, analyzed and acted upon. This in turn creates new data and the cycle continues. Here in the second decade of a data revolution that brings technological changes at the fastest pace ever seen by mankind, most companies in the electrical industry now live in a world of digital invoices and files, and the changes to the supply chain are dramatic.
Companies are dealing with terabytes of data from internal and external sources. To stay in control of this deluge, companies need a data governance program to strategically control, protect and manage this information. In broad terms, data governance “embodies a convergence of data quality, data management, business process management and risk management surrounding the handling of data in an organization,” according to Wikipedia. Most businesses have ongoing security and IT governance processes around customer and financial data, but many fail to recognize that they need procedures in place at their data-collection points that apply business rules to prevent errors from getting into their system. Many companies are not giving equal oversight to all company-created data, and they miss opportunities as a result, because they don't realize data about products, people and processes are enterprise assets.
Data is becoming a new exchange unit of value. It has been predicted that the value of data will be treated as an asset on companies' balance sheets and that financial officers will soon be expected to report on data quality performance measures to their boards or executive teams. Already today, business owners and executives should know the monetary value of all the different types of data the company handles. At the same time, it's important to know the potential value of a data loss or data breach — especially if it includes customer data where loss of trust could negatively impact corporate brands and reputation.
Protecting all types of business data is so crucial that it becomes a competitive differentiator: if there is misuse of data due to neglect, security breaches or under-utilization it may mean giving up market-share to a competitor using its enterprise data assets more wisely.
Like any system, data governance has a life-cycle process that serves as a structure for distributors and manufacturers setting up their own data governance programs.
Data Governance Life-Cycle
- Develop a value statement
- Prepare a road map
- Plan and fund (devise a revenue source)
- Design the program
- Deploy the program
- Govern the data
- Monitor, measure and report
Since data governance is fairly new, sometimes companies are unaware that they need to implement a data governance program. They may mistakenly begin by working on data without establishing their value statement, creating a plan, identifying who will be involved, determining what kind of data quality is required by the data stakeholders, identifying key performance indicators and determining how those indicators should be presented in metrics reports. Some companies will assign program implementation to one data steward without including a cross-functional team or giving the steward sufficient authority. Disadvantages here include insufficient time and technology resources being made available to the data steward, which could mean it will take longer to set-up the data governance program, get programming changes implemented, correct data errors, improve data quality or put into place the security protections needed to prevent a data breach.
Another disadvantage to assigning data stewardship to one individual is the isolation of the company's data knowledge. However, this risk is reduced if the data steward carefully documents the program and change-management procedures and makes these reports and metrics available to other stakeholders. The advantage of the cross-functional team is the blending of employee experience from different business functions and to use this team in the decision-making process for data governance.
One critical step in establishing a data governance program is identifying the owner of each type of data. Once you can link data quality to the processes and people that generate the data, you can get accountability for on-going maintenance.
Data Governance and Quality
Any data governance program should enable better decision-making, reduce operation friction and create sustainable processes that support company governance objectives while at the same time ensuring transparency and knowledge-sharing. Some companies are beginning to recognize that it helps to have a data governance officer with the authority to execute and enforce the strategic enterprise data requirements and lead development of the data strategy. The data governance officer can ensure better data consistency and quality during collection, manipulation and production instead of expecting IT departments or data stewards to try to push these initiatives.
For many electrical distributors and manufacturers, it's the IT department that carries the responsibility for governance. It takes a holistic view to ensure that corporate, IT and data governance are effectively managed. IT governance should align key business initiatives with IT resources, assets and capital expenditures. Some companies have found that approaching data governance in the context of more general corporate governance, which focuses on company objectives including protecting shareholder interests and supporting quality initiatives, can be effective.
Manufacturer CRC Industries Inc., Warminster, Pa., realized the importance of data governance when it started its corporate quality program 13 years ago. Like many companies that start a quality-management program, CRC first needed to establish the metrics on current practices, which required measuring processes where they didn't have all of the data they needed. They looked first at the manufacturing processes. According to Susanne Donovan, CRC's director of quality systems, “In establishing the measurement system for collection of cost-of-quality data, questions needed to be answered to determine how the data was going to be accurately defined and collected so that we could standardize our approach. After a great deal of work, we created a baseline for future improvements with clear measurement criteria and a refined measurement system. Now the system created in 1997 has remained the basis of our cost-of-quality measure, ensuring the validity of year-to-year comparisons, and we have the data to support this.”
Data governance has an important role to play in manufacturers' efforts to improve speed-to-market. In order to be responsive, manufacturers must establish a data governance strategy around product data management and adopt technology, business rules and metrics to manage product information and data quality so new product data flows effortlessly and accurately into the distribution channel.
“By doing all of this (data governance), it helps manufacturers be more responsive to the supply chain, but it's a huge challenge,” says George R. Droder, vice president of customer service & e-commerce at ERICO International, Solon, Ohio. “You must prioritize your goals because governance is not a ‘one and done’ event. Once established, it becomes an evergreen process to manage the business from the top of the corporation, through IT alignment, to pushing new products into the marketplace.”
Distributors use and depend on several types of data for operational excellence — customer contact information, sales history data, employee records, company financial information and inventory, as well as all the product and pricing data — and they must invoke policies that protect against data breach and ensure data integrity. Since the early 1990s, some distributors have centralized their data operations to establish a “single version of the truth” as a starting point for any data project. In support of this, data quality has become a key part of supplier evaluations.
Like many distributors, Colonial Electric, King of Prussia, Pa., has implemented supplier scorecards that measure key performance indicators (KPI) similar to those on the National Association of Electrical Distributors (NAED) Supplier Scorecard. These scorecards have helped suppliers provide better service. “We started using supplier scorecards years ago when we completed our ISO certification,” says Jay Bellwoar, Colonial's chief technology officer. “What we found is that the manufacturer executives really liked getting these scorecards, which identified criteria for purchase-order accuracy, number of items on backorder, fill rates, etc., because then the manufacturer had something concrete to hold their own people accountable and make sure things were done differently and better.”
“Also, (using) these metrics and trending over time helped Colonial Electric identify and prevent supplier problems to maintain our corporate governance quality program, providing high-quality service to our customers,” Bellwoar adds.
Any company implementing a new data governance program should establish a data steward with cross-functional support. In recent years, with the emphasis on data quality for electrical industry data, many manufacturers have designated a data steward with the responsibility of collecting new or changed product and pricing data from their centralized database (frequently called the master data management system or MDM) and preparing it for release to data service providers and to distributors.
EGS Electrical Group, Chicago, used a cross-functional EGS team when they decided to incorporate transactional updates into their centralized database. The company split governance responsibilities between IT governance for the centralized database and data governance managed by their product database manager. “When industry asks us to make changes to data requirements — which might include new attributes or capability to pass new scrubbing rules — because of our company's commitment to providing high-quality data to the industry, those changes are made in our master system,” says Mary Krauss, product database manager for EGS. “Once implemented, we have a change-management process in place to ensure that customers have access to life-cycle, attribute or pricing changes as soon as they are available for release.”
“Other benefits include faster and more frequent data changes with fewer errors because our data has already been internally scrubbed,” Krauss adds.
Data's role in any organization is largely shaped by the organization's use of technology. Once data risk is mitigated, effective data governance improves data quality, which has a direct impact on sales, expands company data knowledge, grows new business opportunities and provides concrete evidence of enterprise data value.
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