What this book covers
Chapter 1, What Is Data Governance?, introduces you to data governance. At face value, data governance may seem like a cost center, if not approached with value generation in mind. Many companies start a data governance program without the right support, structure, or funding model. First, you will learn the basics of what data governance is and how it relates to adjacent capabilities. Then, you will learn the components of data governance programs, why each component matters, and finally, why to treat data governance as an enabler for business value.
Chapter 2, How to Build a Coalition of Advocates, explores gaining support for your program, which is arguably the most important part of launching a data governance program that drives impact. First, you will learn why and how to identify and secure the right executive sponsor for the data program, and then how to bring in additional leadership support. Lastly, you will learn how to engage and energize the entire company to collaborate toward value-based outcomes that matter to them.
Chapter 3, Building a High-Performing Team, focuses on establishing a high-performing data governance team, which is a critical and long-term investment in the success of a company’s use of data. First, you will be introduced to the key roles in a successful data governance function, how they should optimally structure for results, and finally, how to establish routines and rhythms to support the operations of the team.
Chapter 4, Baseline Your Organization, teaches you the importance of defining a baseline, not only for the organization as a whole but also for individual projects. A key component of measuring success is measuring where you start. You will learn how to capture a baseline and who to communicate it to. Finally, we will discuss how to ensure agreement on the baseline before beginning work.
Chapter 5, Define Success and Align on Outcomes, focuses on the area where many data transformations fall flat – aligning on outcomes that matter to a business. Most data transformations stop with data outcomes and fail to reach the final mile – where the business uses the delivered data capabilities to drive operational efficiency, increased revenues, and better insights. In this chapter, you will learn why defining success beyond data and with the business matters, how to successfully map all relevant stakeholders (including secondary and tertiary stakeholders), and how to translate results into business terms.
Chapter 6, Metadata Management, delves into establishing a high-value, high-return metadata management capability, which is required for any data governance program. The success or failure of a chief data and analytics officer hinges on being able to answer a few fundamental, core questions. Where is my data? Who owns it? How is it classified? Is it safe and secure? Can I leverage it for value? Do I know how to reduce risk? You will learn the answers to these questions and be guided through how to tactically set up a metadata management capability for success.
Chapter 7, Technical Metadata and Data Lineage, explores establishing a high-value, high-return data lineage capability, which is a core capability for any data governance program. Following on from Chapter 6, this chapter focuses on the data supply chain. You will learn the answers to the questions in Chapter 6, with a focus on data lineage, and will be guided through how to set up data lineage for success.
Chapter 8, Data Quality, examines understanding the quality of data and being able to have a defendable stance when it comes to “Can I trust it?”, which is key for any user of data or information that is used to make decisions. Establishing a data quality capability enables the CDO/CDAO and their teams to stand behind their data, being able to defend the quality of the information. This solution can also, when coupled with metadata management and data lineage, lead to a data certification process. You will learn the answers to the questions and be guided through how to tactically set up a data quality capability.
Chapter 9, Data Architecture, delves into data architecture. Designing the patterns and optimal flow of information throughout an organization is sometimes more art than science. With data architecture, you will learn just that. First, you will be grounded in what good data architecture is, when and how it should be applied to an organization, why perfection is not the goal, and when not to involve data architects in a program.
Chapter 10, Primary Data Management, focuses on primary data. One of the core capabilities of any organization is the ability to standardize and conform its most critical information – customer, product, and reference data. By nature, rationalized data provides a solution, whereas data used by multiple divisions for many uses is standardized and cleansed for the benefit of the organization as a whole. First, you will understand what primary data is and is not, clarifying misconceptions. Then, you will be guided through the various types of primary data, how to prioritize, and how to implement a strong and centralized primary data solution that will impact and elevate the power of data into a strategic asset. All the capabilities introduced so far will be woven into this powerful capability to tie them together.
Chapter 11, Data Operations, explores how to run the operations of a data organization, including support for the running of primary data management, data warehouses, data lakes, and other authorized provisioning points managed by the data organization. First, you will learn what data operations are, how to scale effectively, and when to pull in engineering. Lastly, you will learn how to optimize DataOps as a core capability and what opportunities there are to automate.
Chapter 12, Launch Powerfully, examines how to launch a good data governance program, which can quickly lose impact if not launched properly. The importance of the launch cannot be underscored enough. You will learn how to create simple and strong core messaging to engage and clearly articulate to the stakeholder community what and how delivery will be accomplished. Then, you will be led through the creation of a launch plan, a design of feedback loops to ensure continuous improvement, and finally, how to report on an ongoing basis for impact.
Chapter 13, Delivering Quick Wins with Impact, delves into the post-launch period, when the data governance team must quickly begin to deliver results. The time to first value metric should be as short as possible while producing impacts that matter to the business. You will learn how to create momentum through the delivery of quick wins, how to communicate the wins to the business, and how to ensure that the business not only understands the results but also becomes an advocate for the future success of the data governance program.
Chapter 14, Data Automation for Impact and More Powerful Results, focuses on automation, which is a lever that can be pulled to expedite data product deliveries. First, you will be introduced to what automation techniques can be applied to data governance. Second, you will learn how to select the right automation solutions for their transformation. Finally, you will learn how to power their transformation with automation across all solutions.
Chapter 15, Adoption That Drives Business Results, explores business adoption. Now that you have learned what data governance is, how to gather support, design a program, baseline the organization, launch, and deliver against the plan, you need to be able to ensure that your solutions are used by the business. Building an adoption roadmap, you will be able to articulate to your stakeholders how to use the solutions, ensuring a lasting impact of the solutions provided to the organization. Lastly, you will double the impact by ensuring ongoing supports are in place.
Chapter 16, Delivering Trusted Results with Outcomes That Matter, teaches you how to ensure consistency in what was promised to stakeholders versus what was actually delivered. As the implementation of data governance occurs, the chief data/analytics officer and their leadership team must keep all messaging focused on results. You will be guided through how to explain variances in expected delivery versus real results, and how that builds trust. Finally, you will learn how to message back to stakeholders powerfully, during delivery for impact.
Chapter 17, Case Study – Financial Institution, walks you through how to apply the topics covered in this book to an organization with a high degree of regulation (i.e., a financial institution). First, you will find use cases that are unique to this type of entity. Second, you will learn how to pull out the unique requirements and how to adjust messaging, sequencing, and results to accommodate the special needs of a highly regulated organization.
