Read an Excerpt
The Chief Data Officer Handbook for Data Governance
By Sunil Soares MC Press Online, LLC
Copyright © 2014 Sunil Soares
All rights reserved.
ISBN: 978-1-58347-418-1
CHAPTER 1
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.
CHAPTER 2
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 without any encumbrances. Because data is critical to regulatory compliance, these companies have identified their CDOs as the single focal point to handle data-related issues. In other words, the CDO is answerable to the regulators if the data is not trustworthy.
2. Data monetization
Data monetization is the process of deriving quantifiable business value from information. The C-suites in large companies increasingly recognize the tremendous value of data. This has spawned an entire generation of analytics departments that are geared toward data monetization initiatives. However, these initiatives have to be tempered by the acceptable use of data in the light of regulatory compliance, privacy concerns, brand reputation, and contractual restrictions. The CDO often has overall responsibility for the tradeoffs between data monetization and acceptable use concerns.
3. Big data
Big data has highlighted a number of new data sources, including sensors, social media, web cookies, and data brokers. It has also driven new technologies such as Hadoop and NoSQL. The traditional IT departments may not be familiar with these technologies. In addition, the corporate security and privacy departments may be playing catch-up with the latest developments around big data. Once again, the CDO may be asked to lead the overall big data initiative.
4. Data politics
Companies have always grappled with the differing business priorities of data producers and data consumers. We refer to this dynamic as "data politics." As discussed in Case Study 2.1 (on page 8), data governance councils have sought to align the competing business objectives of multiple business areas. However, these data governance councils have had mixed success due to their lack of political clout. The rise of the CDO is an effort to add some level of centralized control over data as an enterprise asset.
Case Study 2.1: Data politics around phone number at a retailer2
In some states in the United States, retailers incent store associates to capture phone numbers at the point of sale. Phone numbers create an interesting political dynamic between store operations and marketing. Marketing depends on high-quality phone numbers to improve customer insight. Even if the customer pays in cash, marketing can do a "reverse append" to obtain the customer ID based on the phone number. Marketing can then use the customer ID to add the customer's purchases to their overall transaction history in the data warehouse. Store operations needs to train and incent the store associates to capture accurate phone numbers at the point of sale. However, some customers will decline to provide a phone number. In addition, some store associates try to meet their targets by entering the phone number of the store or a local hotel. As part of the chief data office, the data governance team must work with marketing and store operations to establish real-time validation rules to ensure that store associates enter appropriate phone numbers at the point of sale. The CDO must have enough political clout to align the competing objectives of store operations and marketing.
Data governance is critical to the success of the CDO. The following data governance topics are key to the CDO agenda:
• How do we organize for data governance?
• How do we drive business ownership of data?
• How do we set data policies, standards, and processes?
• How do we monitor the adherence to these data policies, standards, and processes?
• How do we enforce the compliance with these data policies, standards, and processes?
• How do we leverage technology, especially in the context of big data?
The remainder of this book will review each of these topics in detail.
Summary
In this chapter, we reviewed the key trends that are driving the emergence of the CDO. These trends include the regulatory environment, data monetization, big data, and data politics. Companies are awakening to the realization that data is the lifeblood of the organization and it needs to be dealt with appropriately. The chief data office is often tasked with establishing data as a key business differentiator by discovering valuable data, defining data policies, and managing the trustworthiness of the data as an asset.
CHAPTER 3
Organizing for Data Governance
ALTHOUGH THE CDO ROLE IS STILL EVOLVING, A NUMBER of organizational patterns are already beginning to emerge. Companies must recognize these organizational patterns to maximize the likelihood of CDO success. These topics are discussed in this chapter.
Agree on the Reporting Relationship for the Chief Data Officer
There is no one single answer to where the CDO should report. This section is based on the reporting patterns that exist today. However, we anticipate that these reporting patterns will become standardized as more companies appoint CDOs. Today's CDO may report to a number of executives as discussed below.
• Chief Information Officer
The CDO most commonly reports to the Chief Information Officer (CIO). Because there is no logical "Switzerland" or supposedly unbiased party that has a strong interest in data, the default reporting structure for the CDO is the CIO. This structure also eliminates any potential conflicts between the CDO and the rest of the IT department. However, this structure may reinforce the perception that data is owned by IT and is not a business problem.
• Chief Operating Officer
An increasing number of companies have the CDO reporting to the Chief Operating Officer (COO). This reporting structure has a few key advantages. First, the COO may be perceived to be the "Switzerland," or impartial arbiter, of data as an enterprise asset. As a result, marketing, sales, finance, and other functions will be more inclined to accept the role of the CDO. Second, the COO already has a key focus on operational efficiency. This reporting structure reinforces the COO's key mission by leveraging data to improve operating efficiency. In one instance, a large bank had both the CIO and the CDO as direct reports to the COO.
• Chief Executive Officer or President
The CDO may also report to the Chief Executive Officer (CEO) or President of the company. This reporting structure works well in information services companies, which aggregate information for sale to customers. It may also work well in situations where there is a heightened level of focus on information as an enterprise asset. The downside is that the CEO or President may not always have the time to devote to the CDO.
• Chief Risk Officer
The CDO may also report to the Chief Risk Officer (CRO). This reporting structure works very well in industries such as banking and insurance that have a strong risk management focus. However, other functions, such as marketing, sales, product management, and finance, may feel shortchanged.
• Chief Financial Officer
The CDO may also report to the Chief Financial Officer (CFO). This reporting structure works very well in industries such as manufacturing or in companies that are finance-driven. Other functions, however, such as marketing, sales, product management, and risk management, may feel less connected to the EDM program.
• Chief Marketing Officer
The CDO may also report to the Chief Marketing Officer (CMO). This reporting structure works very well in in companies with a strong focus on customer-oriented marketing and analytics. However, the non-marketing functions may feel less aligned with the CDO in this reporting structure.
Although there is no right or wrong answer, companies must adopt the following criteria when determining the appropriate reporting structure for the CDO:
• Size of company — The CDO may report to a more senior executive in smaller companies. However, the CDO may report a few levels down from the CEO in very large companies.
• Executive champion — The CDO should report to the senior executive who champions the use of data as an enterprise asset. This executive may be the CRO, CFO, CMO, or somebody else.
• Level of business sponsorship — If a company has limited business sponsorship for EDM, it will likely struggle with the CDO role. In this case, it is advisable to have the CDO report to the CIO on an interim basis. Once the business starts to see value from EDM, the CDO can move into the business.
Determine the Level of Centralization of the Chief Data Office
The organization must also determine the level of centralization of the chief data office. We discuss a number of patterns for the chief data office below:
• Single, Centralized Chief Data Office
The first pattern is a single, centralized chief data office. This model works well for smaller companies that operate in a single geography and a single product line.
• Federated Chief Data Office by Geography
Larger organizations may adopt a federated approach by geography. As shown in Figure 3.1, a multinational corporation may adopt a federated model with a global CDO as well as regional CDOs for the Americas, Europe, and Asia Pacific. The regional CDOs report into their respective regions but are dotted line to the global CDO. This approach addresses some of the political realities inherent in the fact that most organizations are not ready to completely centralize their data-related decision-making. The geographically federated model also allows data decisions to be made closer to the revenue-generating regions.
• Federated Chief Data Office by Business Unit
Some organizations may also adopt a federated approach by business unit, as shown in Figure 3.2. The business unit CDOs report into their individual business units but are dotted line to the global CDO. This approach allows organizations to have some level of enterprise coordination but also permits data decisions to be close to the revenue-generating business units.
• Federated Chief Data Office by Geography and Business Unit
Some companies may also adopt a hybrid approach, with a federated model by geography and business unit. As shown in Figure 3.3, a large multinational bank established a global CDO role. The Americas CDO had a dual role as the corporate banking CDO, while the Asia Pacific CDO was also the retail banking CDO. The geography CDOs reported into the regional organizations but were dotted line to the global CDO.
(Continues...)
Excerpted from The Chief Data Officer Handbook for Data Governance by Sunil Soares. Copyright © 2014 Sunil Soares. Excerpted by permission of MC Press Online, LLC.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
