Ingredients for Data Governance Success

DATA GOVERNANCE IS THE FORMULATION OF POLICY TO optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. By its very nature, data governance requires cross-departmental cooperation to deliver timely, trustworthy data for better decisions.

Many organizations have tried and failed to implement successful data governance programs. Having reviewed the characteristics of hundreds of data governance programs, we have developed five ingredients of a successful data governance program:

1. Strong business ownership with IT support

Successful data governance programs are owned by the business with strong IT support. Nascent data governance programs may start in IT, but they need to move into the business once they have achieved a level of maturity. Strong business ownership requires executive sponsorship around clearly defined business problems. For example, the chief risk officer in a bank may be the executive sponsor to improve the trustworthiness of data for Basel II compliance. The chief financial officer may also sponsor data governance to improve the quality of financial reporting. It goes without saying that strong executive sponsorship will ensure the appropriate level of funding and staffing for the data governance program. IT is also an important ingredient of data governance success. As we discuss in Chapter 8, technology plays a critical role in the successful implementation of a data governance program.

2. Focus on critical data elements

Because it is impossible to successfully govern all the data across the enterprise, mature data governance programs must focus on critical data elements (CDEs). These are a handful of attributes that have a significant impact on regulatory reporting, operational performance, and business intelligence.

3. Emphasis on data artifacts

Successful data governance programs generate a number of valuable data artifacts. These data artifacts include business terms, business rules, code tables, policies, standards, processes, data quality scorecards, and data issues. Over time, successful data governance programs generate a rhythm associated with the production and approval of these artifacts at meetings of the data stewards and the data governance council.

4. Alignment around metrics and policy enforcement

As with any program, data governance must focus on metrics that are important to the business. These metrics may be "the number of critical data elements with end-to-end data lineage" or the data quality index. Successful data governance programs also introduce mechanisms to enforce data policies, standards, and processes. These mechanisms include data governance scorecards, escalations, and data audits.

5. Celebration of quick wins with alignment around the long-term roadmap

Successful data governance programs are able to point to quick wins within weeks of inception. These quick wins may be creation of a glossary for key business terms or a data quality report. However, these programs are also tied into a long-term roadmap over 12 to 18 months.

As you can imagine, it is not easy to implement data governance successfully. Many organizations have tried, and many have failed. As we discuss in the next chapter, data governance councils often lack the clout to successfully align the different factions in an organization. The Chief Data Officer (CDO) is a recent innovation to create a strong executive focus on data governance.

Summary

In this chapter, we introduced the importance of the CDO to the successful implementation of a data governance program. The five ingredients of data governance success are strong business ownership with IT support, focus on critical data elements, emphasis on data artifacts, alignment around metrics and policy enforcement, and celebration of quick wins with alignment around the long-term roadmap.

The CDO Agenda

MANY COMPANIES HAVE ESTABLISHED CENTRALIZED DATA management departments. These departments are variously referred to as "enterprise data management," "enterprise information management," or the "chief data office." Whatever they are called, these departments share some common characteristics. They have accountability for data as an enterprise asset, and they increasingly report into the business. The leader of these departments often carries the title of vice president of enterprise data management, senior vice president of enterprise data management, vice president of enterprise information management, or senior vice president of enterprise information management. Increasingly, these leaders are being anointed as Chief Data Officers.

We will first define certain foundational terms for this book:

• Enterprise Data Management (EDM) refers to the ability of an organization to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication. EDM includes a number of disciplines, such as data governance, data ownership, data architecture, data modeling, data integration, database management and operations, data security and privacy, master data management, reference data management, data warehousing, critical data elements, metadata management, data quality management, information lifecycle management, and content management.

• Enterprise Information Management (EIM) is synonymous with EDM.

• The Chief Data Officer is a C-level executive with overall accountability for EDM.

• The chief data office refers to the EDM organization or department, which reports to the CDO.

• Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Data governance is a discipline under EDM.

The emergence of the CDO is driven by four macro-trends as shown in Figure 2.1:

1. Regulatory environment

Many companies, especially those in the financial services industry, must comply with stringent regulations that are data-centric in nature. Banks must comply with regulations such as the Basel Committee on Banking Supervision (BCBS) 239 for risk data aggregation and the Dodd-Frank Wall Street Reform and Consumer Protection Act. Insurers need to comply with regulations that include Solvency II in Europe as well as directives from regulators in each state in the United States. Broker-dealers must comply with regulations from the Financial Industry Regulatory Authority (FINRA). Many federal, state, and local governments also have CDOs to lead their open data initiatives so that citizens can freely access data...