Data Stewardship

An Actionable Guide to Effective Data Management and Data Governance

David Plotkin

• Verlag: Morgan Kaufmann
• Jahr: 2013
• ISBN:
  9780124104457
• Thema: Computer Science
• Thema: Databases

Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company's data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward's time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort.

• Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership
• Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management
• Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards

Chapter 1

Data Stewardship and Data Governance

How They Fit Together

This chapter discusses the definition and deliverables (including policies, procedures, and processes) for a Data Governance program. The structure of the organization is discussed, as well as the roles and responsibilities of participants in the program, including a brief list of stewardship tasks. The key role of Data Stewardship in a Data Governance program is discussed, as well as how Data Stewardship fits into the overall program.

Keywords

Executive Steering Committee; Data Governance Board; Data Governance Program Office; asset; business rule; policy; procedure; process

Introduction

Data has become so vital to the success of almost all organizations (both large and small) that many are attempting to implement a Data Governance program. When done properly, it provides the means to manage the overall collection of data (i.e., the data asset), including the structure, processes, and organization needed to manage key data elements. A vital component of a Data Governance program is Data Stewardship. However, to understand the role that Data Stewardship plays in Data Governance, you need to understand the overall Data Governance program itself, including the purpose, deliverables, roles and responsibilities, and value that Data Governance adds to the company. You also need to understand how Data Stewardship interacts with other aspects of the Data Governance program, and what part Data Stewardship plays in the success of the program.

This chapter defines Data Governance and Data Stewardship, and explains the structure and inner workings of the organization needed to support the effort. It details the responsibilities of each type of participant, including executives, Data Governors, Data Stewards, members of the Data Governance Program Office, and IT support staff. It also provides a “target”—the end result of what a good Data Stewardship effort can and should achieve.

What is Data Governance?

Ask a room full of Data Governance practitioners what Data Governance means, and you’ll probably get as many definitions as there are people. One of the best definitions I have ever run across comes from my friend Gwen Thomas of the Data Governance Institute (DGI):

Data Governance is the exercise of decision making and authority for data-related matters.

It’s a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.

The key thing to take away from this definition is that the practice of Data Governance has more to do with establishing the roles and responsibilities about how people manage and make decisions about data than about the data itself. That is, Data Governance—and Data Stewardship—is all about making sure that people are properly organized and do the right things to make their data understood, trusted, of high quality, and, ultimately, suitable and usable for the enterprise’s purposes.

Note

It is not the purpose of this book to fully define and discuss Data Governance. Instead, this book focuses on the practical details of Data Stewardship, which is a necessary component of Data Governance. For an excellent reference on Data Governance, please see Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program by John Ladley, published by Morgan Kaufman.

What is Data Stewardship?

Data Stewardship is the operational aspect of Data Governance, where most of the day-to-day work of Data Governance gets done. According to Danette McGilvray:

Data Stewardship is an approach to Data Governance that formalizes accountability for managing information resources on behalf of others and for the best interests of the organization.

As Danette also notes in her book Executing Data Quality Projects (Morgan Kaufmann Publishers, 2008), a steward is someone who manages something on behalf of someone else. In the case of Data Stewardship, the “someone else” is the business function that owns the data, represented by the business function’s representative on the Data Governance Board (Data Governor).

Put another way, Data Stewardship consists of the people, organization, and processes to ensure that the appropriately designated stewards are responsible for the governed data.

Data Stewardship is crucial to the success of Data Governance (which, in turn, is crucial to the success of data management). This is because it is through Data Stewardship (and the Data Stewards) that all of the metadata (definitions, business rules, and more) is collected and documented. In addition, having stewards who are responsible for the data, as well as having a set of procedures that require the stewards to be consulted on decisions about the data they steward, helps to ensure that the decisions are based on knowledge and are made in the best interests of all who use the data. The combination of designated stewards, processes, and a mission to manage data in the best interests of all leads to the data asset being improved in quality, and to it being used to drive competitive advantage and regulatory compliance for the business.

Overall Goals of Data Stewardship

So, what does “good” Data Stewardship look like? That is, what goals should a Data Stewardship program be striving for—what are we trying to achieve? The goals should align with the highest level of maturity (optimized) discussed in Chapter 9 and should include the following:

■ A smoothly functioning Data Stewardship Council.

■ Policies and procedures are in place, and have become part of the corporate culture.

■ Business Data Stewards are designated and participating from every business function that owns data. There is no participation from business functions that do not own data. Data Governance and Data Stewardship includes outside business partners.

■ Technical Data Stewards are designated from all enterprise applications, data stores, data warehouses, data marts, and ETL processes.

■ Data Stewardship involvement is integrated into enterprise processes, such as project management and system development methodology. Data Stewards are viewed as an integral and necessary part of data management.

■ Clearly defined roles and responsibilities for all Data Stewards and ratings for how effectively those roles and responsibilities are performed are built into the steward’s compensation objectives.

■ The responsibility of all employees for the management of data is accepted as part of the corporate culture.

■ There is executive support and endorsement for Data Stewardship. Executives are publicly supporting the policies and actively promoting adherence to them, as well as the creation of procedures to implement the policies.

■ There is clearly defined and recognized value being added by Data Stewardship.

■ Key business data elements are identified and defined and business rules determined, and all are linked to physical instances of the data. Where appropriate, the data has been profiled to understand and correct data quality.

■ Data Stewardship decisions are clearly documented and published to interested parties using sanctioned communication methods.

■ Training for all involved parties (including stewards, project managers, and developers) has been written and is being given on a regular schedule.

■ Supporting tools (e.g., a metadata repository, business glossary, central issue log, and data profiling tool) are installed, supported, and in regular use.

■ Innovation in maintaining the vision of data quality and remediating data issues is encouraged, as well as creativity and competitive advantage in using high-quality data.

■ Management and the Data Governance staff keep abreast of important emerging trends in data management and adapt accordingly.

■ Processes and procedures are written, approved, and in use to:

Identify key business data elements

Collect, review, and approve business metadata for the key business data elements

Log, analyze, prioritize, and remediate data and data quality issues

Support projects

Manage domain data

Replace Data Stewards as necessary

Review and analyze data quality improvement opportunities

Document stewardship work done and methods for publication

Moving Data to a Governed State

At its simplest level, the purpose of executing on Data Governance is to move data from an ungoverned state to a governed state. Ungoverned data refers to most of the data that an enterprise has, at least at the beginning of a Data Stewardship effort. It is rarely defined, its quality is unknown, its business rules are nonexistent or conflict with one another, and no one is accountable for the data. Governed data is data that is trusted and understood and for which someone is accountable for both the data itself and for addressing issues about the data.

In addition, fully governed data means that you know all of the following at the data element level:

■ The standardized business name of the data element. This is the standard business name by which the data element is called (ideally) everywhere in the company. Where a business unit has a need to call it something else, that alias is documented.

■ The standardized business definition of the data element. Just as there should be one standard business name, there should also be only one standard business definition for the data element. Where there is disagreement on the definition, the data owner must either change the definition or the errant data element must be defined and given a new name.

■ For calculated or derived data elements, the calculation or derivation rule. The rule needs to be very specific so that there is no confusion about how a quantity or value is derived. As with the standard business definition, if there is disagreement, either the derivation rule must be changed, or a new data element (with a different derivation) must be defined.

■ The physical location of the business data element in a database/system. The physical data element is essentially a mapping of the business data element to, for example, a column in a table in a database in a system, or something equivalent.

■ The data quality rules, in context. This includes the rules that specify good quality (e.g., format, range, valid values, pattern, and so on), as well as the level of quality needed for each intended use of the data.

■ Rules for creating the data element. These are the rules that must be followed before an instance of the data element can be created. When ap...