Monday, January 23, 2012

10 Steps to Effective Data Governance for SMBs #datagovernance

by Maria C. Villar and Theresa C. Kushner

This article was originally published at Information-Management.com on January 11th, 2012
© 2012 Information Management and SourceMedia, Inc.

Data Governance is an emerging business  priority. In a recent survey sponsored by IBM, two-thirds of all companies are implementing  or planning to implement a data governance program in the next  18 months.  Seventy percent see data governance growing in importance in the next three to five years.

This growing importance is supported by the business problems companies are experiencing and the high value they place on data governance solving these problems. While large companies are leading the way with data governance programs, the survey found half of smaller companies are involved or plan to be.

Yet, there is not a one-size-fits-all governance program for all companies.  How should business leaders in medium and small companies implement a data governance program that is both effective and fits with the company’s size and resources?

The importance of data management and data governance  is the first item business leaders need to understand in order to convince themselves, their managers and their employees of the need for improvement. In fact, the inability to communicate the value of managing data is a major obstacle for most companies, as cited in the survey. As a business leader in a medium-sized company, there are many management topics to tackle to ensure the company is financially successful and that customers, partners, employees and investors are satisfied. That is enough to keep business leaders busy, so why should they also care how the company’s important data is managed? Is that not the job of the technical and operations staff? The technical and operations staff should be involved in data management, but business leaders should be setting the priorities, rules and metrics to ensure the data supports the business needs.

Data errors cause significant business issues each year. Billing data quality issues result in millions of dollars in lost revenue. Embarrassing company communications to valued customers are caused by incorrect contact information such as names or gender. Data errors in financial reporting can cause penalties, financial restatements and incarcerations. Data security breaches can cause loss of confidence and broad negative press coverage. Sales forecasting inaccuracies can result in the company setting the wrong earnings expectations to the board or industry analysts. Many companies start data governance programs when data errors lead to these serious business issues. But why wait? Can any of this happen at your company? How confident are you that the important data in your company is accurate, complete and reliable for all the business processes served?  

Similar to people management, capital management and product management, data management is a business driven function that manages another important asset: a company’s business data. Not all business data has the same value and, therefore, not all business data needs to be managed with strong business leader involvement. In practice, about 20-to-30 percent of a company’s business  data is critical and strategic. Data governance is the data management activity that ensures the company’s most critical and strategic business data is acquired, created, updated, deleted and stored with proper processes, policies and people.

The 10 steps below offer medium-sized enterprises a practical approach for getting data management started.    

  1. Take a business-driven approach to data management by tying your data activities to key business drivers and company goals such as mergers and acquisitions, business intelligence, business process re-engineering and addressing company process pain points. From the results of the previously mentioned survey, one of the biggest barriers to implementation is that data governance has a lower priority than other programs. By combining data governance with other key business projects, you circumvent this barrier.
  2. Gather the top business leaders in your company and agree on a collaborative, business-driven approach to the next four steps. Appoint a leader in your organization to lead the data governance program during the initial design phase and a forum for ongoing operational reviews. This is a great staff assignment for a talented manager in your company.
  3. Decide on the most critical and strategic business data to manage. For example, customer account and contact information may be more important in a service-oriented business and product price list and inventory may be prized by manufacturing while data that impacts financial reporting will be critical for all companies.
  4. Decide what is required from the data to support the business needs and the decisions to be made. How often will the data need to be acquired and refreshed? What level of quality is needed? Which fields are mandatory? How should the data be protected for regulation and privacy concerns? For example, medical offices have higher standards regarding client data than do restaurants or retail outlets.
  5. Name those accountable for ensuring the data selected in step 3 meets all the business needs identified in step 4. Accountable parties could be selected by either the function or department where the data is first created or the function or department that will get the most value from managing the data. For example, the marketing function can be selected to manage customer contact information, focusing on all business needs for this information, not just the marketing function’s needs.
  6. Decide how to measure success by meeting the data requirements in step 4. Possible metrics could include data quality levels to be achieved, timeliness of reports or compliance to regulatory standards. Consider also business benefit metrics such as goals for cost improvements, new revenue acquired from upselling or cross selling, manual steps eliminated and supplier efficiencies. Tying data governance improvements to business benefits keeps the business leaders engaged in the program because they see results that affect them.
  7. Review metrics on an ongoing basis with the parties who are accountable. Manage your data as you would your personnel with quarterly reviews and annual assessments. Better yet, if company leaders are already reviewing key business metrics in an ongoing forum, extend the forum to include the data metrics.  
  8. Be actively involved in approving the policies, rules and standards associated with data use and management. A reasonable set of policies, rules and standards needs to be established by the accountable parties. These policies, rules and standards document to employees how the requirements are to be met in their everyday activities. Appropriate employee communication and training should also be scheduled. Business leaders should watch that these policies, rules and standards are reasonable, easy to implement and easy to understand, otherwise company employees will not follow them. All that is needed are a  few effective data policies.
  9. Hold your management teams accountable for complying to the data policies, rules and standards. Communicating and training employees on their data policies, rules and standards responsibilities will only go so far to ensure they are followed.  Managers should hold employees accountable in their year-end evaluations, and managers should be held accountable for their team’s overall compliance. Accountable parties should report any exceptions to the department managers.
  10. Fund the data management projects and resources. The data governance program may require a few specialized skills and IT capabilities in order to automate and monitor for success. The data governance program benefits  outweigh the incremental cost of governance. Key business leaders should review the data governance program funding each year against the metrics achieved and metrics to be expected and fund accordingly.

With these 10 business actions, your technical team can enable data management policies, rules and metrics with technology. Data governance tools and technology can automate many of the data creation and update functions while ensuring data quality standards are met. Existing reporting tools can be extended to monitor and track key data fields and data governance activities. While data governance programs can be started with spreadsheets and manual monitoring, technology enables employees to be more productive and more predictable.

Once the initial design of your data governance program is established, business operation teams can define the operating procedures and staff the operating data roles. These same operation  teams can also establish the ongoing metric reviews with the accountable parties and track issues and work items. As the leader in  your company, these 10 practical steps will set the company on the path to effective data management and empower your employees to use their critical business data with confidence.

Maria C. Villar is a leader, consultant and writer in the field of enterprise information management, IT management and software development. She has held senior executive positions in both the technology and financial sector. Her information management accomplishments have been recognized by TDWI for best practices in data governance and business performance management. She has been recognized in Hispanic Business Magazine as one of the top 100 Influential Hispanics and received the Hispanic Engineer National Achievement Award. Maria has guest lectured on the topic of IT and information management in leading universities, industry conferences and Fortune 500 companies across the country and is an instructor at E-learningCurve. She also co-authored a book with Theresa Kushner, “Managing your Business Data: From Chaos to Confidence.”

Theresa C. Kushner is director of customer intelligence at Cisco Systems. She is a journalist-turned-marketer who has spent her entire career in high tech marketing with Texas Instruments, IBM and Cisco Systems. Her team is responsible for data quality and business intelligence governance for worldwide marketing. Kushner has worked closely with IT throughout her career.