Data-Governance-Accelerator

Chapter 1

In this chapter, we will be:

• Unpacking the definition of data governance

• Discovering the elements of a data governance program

• Understanding the role of data culture

• Determining data governance readiness.

Understanding Data Governance

• The topic of data governance seems abstract to far too many people without a full appreciation of its definition, role, and value.

What is meant by governance?

• When first presented with the phrase data governance, most people immediately understand the data part, but can be quickly confused by the use and context of the word “governance.”

• Governance is the manner in which an entity chooses to oversee the control and direction of an area of interest.

• Governance is the system that formalizes control, processes, and accountabilities so that specific results such as meeting goals or sustaining standards can be attained.

What is data governance?

• Data governance refers to the set of roles, processes, policies and tools which ensure proper data quality throughout the data lifecycle and proper data usage across an organization.

• This means the data needs to be accurate and current.

• Leaders want data to provide the basis for rich insights that enable timely and informed data-driven decision-making.

• Data governance is all about managing data well, but data governance is not restricted to only data management.

Data Governance Versus Data Management

• Within the EIM space, there are many terms that sound like they might mean the same thing.

• There is often confusion about the difference between data governance and data management.

• Data governance is focused on roles and responsibilities, policies, definitions, metrics, and the lifecycle of data.

• Data management is the technical implementation of data governance.

• Information governance is entirely concerned with the meaning of the data and its relationship in terms of outcomes and value to the organization, customers, and other stakeholders.

The Value of Data Governance

• If an organization considers data to be a priority — and an increasing number of businesses believe just that (in fact, according to Anmut

• A data consultancy, 91 percent of business leaders say that data is a critical part of their organization’s success)

• Fundamentally, data governance is driven by a desire to increase the value of data and reduce the risks associated with it.

• It forces a leap from an ad hoc approach to data to one that is strategic in nature.

Some of the main advantages achieved by good data governance include:

• Improved data quality

• Expanded data value

• Increased data compliance

• Improved data-driven decision-making

• Enhanced business performance

• Improved data search

Creating a Data Governance Program

The basic steps for creating a data governance program consist of the following. These steps also form the basic outline of this course.

• Defining the vision, goals, and benefits.

• Analyzing the current state of data governance and management.

• Developing a proposal based on the first two steps, including a draft plan.

• Achieving leadership approval.

• Designing and developing the program.

• Implementing the program.

• Monitoring and measuring performance.

• Maintaining the program.

Figure 1: The most common elements of a data governance program. (Source: John Wiley & Sons)

Developing a Data Governance Framework

• You cannot buy a data governance program off the shelf.

• That’s actually good news.

• Organizations must implement a program relative to its level of interest, as well as its needs, budget, and capabilities.

• While there are many framework variations to choose from, including ISACA’s Control Objectives for Information and Related Technologies (COBIT) IT governance framework.

• They share some common components that address people, processes, and technology.

Leadership and Strategy

• Your data governance program must be aligned with the strategy of the organization.

• For example, how can data governance support the role that data plays in enabling growth in specific markets?

Roles and Responsibilities

• Your data governance program will only be possible with the right people doing the right things at the right time.

• Every data governance framework includes the identification and assignment of specific roles and responsibilities, which range from the information technology (IT) team to data stewards.

Policies, Processes, and Standards

• At the heart of every data governance program are the policies, processes, and standards that guide responsibilities and support uniformity across the organization.

• Each of these must be designed, developed, and deployed.

• Depending on the size and complexity of the organization, this can take significant effort.

Metrics

• The data governance program must have a mechanism to measure whether it is delivering the expected results.

• Capturing metrics and delivering them to a variety of stakeholders is important for maintaining support, which includes funding.

Tools

• Fortunately, a large marketplace now exists for tools in support of data governance and management.

• These include tools for master data management, data catalogs, search, security, integration, analytics, and compliance.

Communications and Collaboration

• With the introduction of data governance and the ongoing, sometimes evolving, requirements, high-quality communications are key.

• This takes many forms, including in-person meetings, emails, newsletters, and workshops.

• Change management, in particular, requires careful attention to ensure that impacted team members understand how the changes brought about by the data governance program affect them and their obligations.

Preparing for Data Governance

• It might seem a good idea just to form a team, create a plan, buy some tools, and then implement data governance.

• That would be a mistake. Data governance requires careful treatment, beginning with understanding whether an organization is ready to accept it.

• As the following sections make clear, there are some traps that you can avoid if you and your team are diligent.

What is data culture?

• Many well-designed projects and initiatives fall flat and fail even though their teams seem to have done everything right.

• Too often, the work gets deployed into an environment that is either not ready for change or doesn’t have the optimum conditions for success.

Assessing the Data Culture

• If you want to increase your chances of success - you need to understand the data culture of your organization and determine how to broaden and mature it if necessary.

• In a data culture, decisions don’t rely on gut feelings, guesses, or the opinion of the highest-paid/ranking person in the room.

• Rather, decisions are based on data and the insights they can produce.

Maturing the Data Culture

• If you decide that you need to better prepare the organization for data governance by maturing the data culture, consider these items to start.

• Help leaders communicate the value of data and model the type of behavior that demonstrates that data is a priority.

• This must include communicating the positive results of using data.

• Provide basic tools and education for data use that include manipulating data, analytics, data cleansing, basic query commands, and visualization.

Assessing Data Governance Readiness

• So, you’ve either determined your organization has a good data culture, or you’ve put into action some steps to help move it forward, and now you’ve decided it’s time to roll up your sleeves and begin designing a data governance program.

The following basic checklist of items will help you determine the data governance readiness of your organization:

• The basis of a data culture exists.

• The program is 100 percent aligned with business strategy.

• Senior leadership is 100 percent committed to the program and its goals.

• One or more sponsors have been identified at an executive level.

• The program has the commitment to fund its creation and to maintain it in the long term.

• The organization understands this is an ongoing program and not a one-off project.

• You have documented the return-on-investment (ROI).

Chapter 2

In this chapter, we will be:

• Defining data and its relationship to information

• Exploring the role of data in the 21st century

• Moving from data to insights

• Discovering the impact of big data

Defining Data

• We create and use data all the time and we usually take it for granted. It’s part of our daily personal and business vernacular.

• Like so many things, if you were asked to define data, you’d give a definition and it may not be the same as your colleagues.

Why all the focus on data?

• Data refers to collections of digitally stored units, in other words, stuff that is kept on a computing device.

Data is also defined based on its captured format.

Specifically, at a high level, it falls into one of the following categories:

• Structured: Data that has been formatted to a set structure; each data unit fits nicely into a table in a database. It’s ready for analysis. Examples include first name, last name, and phone number.

• Unstructured: Data that are stored in a native format must be processed to be used. Further work is required to enable analysis. Examples include email content and social media posts.

• Semi-structured: Data that contains additional information to enable the native format to be searched and analyzed.

Welcome to The Zettabyte Era

• Question, how big is a zettabyte.

• Just a few decades ago there was little to no use for the term by the general population.

• Today, we live in the zettabyte era. A zettabyte is a big number.

• A really big number. It’s 1021, or a 1 with 21 zeros after it. It looks like this: 1,000,000,000,000,000,000,000 bytes.

Figure 3: The qualitative and quantitative nature of data types. (Source: John Wiley & Sons)

Table 1b: Examples of Data Volumes

From Data to Insight

• Creating, collecting, and storing data is a waste of time and money if it’s being done without a clear purpose or intent to use it in the future.

• Data that is never used is about as useful as producing reports that nobody reads.

• The assumption is that you have data for a reason.

• You have your data and it’s incredibly important to your organization, but it must be converted to information to have meaning.

The Role of Data in the 21st Century

• Since the early days of data processing in the 19th and 20th centuries right through to digital transformation in the 21st century, data has played many important roles.

• It’s helped us understand the world in completely new ways, improved our ability to make better-informed decisions, and supported our efforts to solve all manner of problems.

Data-Driven Decision-Making

• Perhaps one of the greatest values of data is its ability to help us all make better decisions.

• Intuitively reading the customer reviews of a restaurant on a website such as Hello Peter or Google Reviews can help you decide whether you want to eat there.

Data as The New Oil

• A popular refrain coined by the mathematician Clive Humby in 2006 is that data is the new oil.

• Just as oil drove and grew economies in the past, data is doing that now.

• Some have subsequently added that just like oil, data has value but must first be processed to be useful.

Data Ownership

• Data ownership describes the rights a person, team, or organization has over one or more data sets.

• These rights may span from lightweight oversight and control to rigorous rules that are legally enforceable.

• For example, data associated with intellectual property — items such as copyrights and trade secrets — will likely have high degrees of protection, from accessibility rights to who can use the data and for what purpose.

Data Architecture

• Today, it’s not an exaggeration to state that almost every organization is a technology business.

• After all, what businesses can function without having systems to support their operations and deliver their products and services?

• When designing the technical needs of an organization to support its business strategy, this practice is known as enterprise architecture (EA).

At a minimum, data architecture considers and typically supports the following:

• Ensuring data is available to those who need it and are approved to use it.

• Reducing the complexity of accessing and utilizing data

• Creating and enforcing data protections to support organizational policies and obligations.

• Adopting and agreeing to data standards

• Optimizing the flow and efficient use of data to eliminate bottlenecks and duplication.

• Data architecture is a direct reflection of data governance.

• An established and functioning data architecture immediately signals that an organization values data, manages it as a critical business asset, and has controls in place to ensure that it aligns with business needs.

The Lifecycle of Data

All data goes through phases during its lifecycle. Figure 6 illustrates a typical lifecycle.

Figure 6: The lifecycle of data

• Creation: This is the stage at which data comes into being. It may be manual or automated and get created internally or externally.

• Storage: Once data is created and assuming you want it available for later use, it must be stored. It most likely will be contained and managed in a database.

• Usage: Hopefully you’re capturing and storing data because you want to use it. Maybe not immediately, but at some point, perhaps for analysis. Data may need to be processed to be useful.

• Archival: In this stage, you identify data that is not currently being used and move it to a long-term storage system out of your production environment.

• Destruction: Despite a desire by some to keep everything forever, there is a logical point where destruction makes sense or is required by regulation or policy.

Understanding the Impact of Big Data

• Data isn’t some kind of new phenomenon. In fact, we’ve been capturing and storing data in an analog fashion for thousands of years.

The Role of The U.S. Census in The Information Revolution

• Processing data on some form of computing device has been around since the late 1800s.

• In fact, the need for a mechanism to better tabulate the results of the U.S. constitutional requirement to conduct a population census every ten years is said to be the origin of data processing.

Defining Big Data

One way to define and characterize big data is through these five Vs:

• Volume: The sheer scale of data being produced is unprecedented and requires new tools, skills, and processes.

• Variety: There are already a lot of legacy file formats, such as CSV and MP3, and with new innovations, new formats are emerging all the time. This requires different methods of handling, from analysis to security.

• Velocity: With so many collection points, digital interfaces, and ubiquitous connectivity, data is being created and moved at increasing speed. Consider that in 2021, Instagram users created, uploaded, and share
  65,000 pictures a minute.

• Variability: The fact that the creation and flow of data are unpredictable.

• Veracity: The quality, including accuracy and truthfulness, of large volume of disparate sets of data, can differ considerably, causing challenges to data management.

Drivers of Big Data

• At a technology conference in 2003, the then-CEO of Google, Eric Schmidt. At the time said that every two days the world was creating more data than all the data created since the dawn of civilization.

Consequences of Big Data

• While these big data statistics are impressive, they don’t really paint the full picture.

• It might be easy, for example, to assume that all the data is good quality.

• You might believe it is easy to analyze. You may even think it is easily accessible.

What about small data?

• All this talk about big data might suggest that small amounts of data just aren’t as interesting or valuable. I don’t want to suggest that, as it would be wrong.

Enter the Realm of Smart Data

• Smart data has emerged as a new term that defines big data that’s been optimally prepared for use to deliver the highest business value.

• Instead of being overwhelmed by the distractions inherent to the volume, velocity, and variety of data in big data sets, processes are applied to big data to prepare it for specific uses.

Chapter 3

Driving Value Through Data

• Identifying many roles data plays in organizations

• Delivering insight from data

• Recognizing that data is an asset

• Exploring four approaches to data analytics

Identifying the Roles of Data

• To fully appreciate the value that data brings to every organization, it’s worth exploring the many ways that data shows up on a daily basis.

• Recognizing the incredible diversity of data use and the exposure it has across all business functions reinforces its importance.

Operations

• Business operations concern themselves with a diverse set of activities to run the day-to-day needs and drive the mission of an organization.

• Each business has different needs, and operational functions reflect these specific requirements.

Strategy

• Every organization has a strategy, whether it’s articulated overtly or not.

• At the organizational level, this is about creating a plan that supports objectives and goals.

• Strategy leads to implementation and requires the support of operations to realize its goals.

Decision-Making

• It’s generally accepted in business that the highest form of value derived from data is the ability to make better-informed decisions.

• The volume and quality of data available today have no precedent in history.

Measuring

• Organizations are in a continuous state of measurement, whether it’s overt or tacit.

• Every observed unit of data contributes to building a picture of the business.

• The often-used adage, what gets measured gets managed, is generally applicable.

• Data measurements can be quantitative or qualitative.

• Quantitative data is most often described in numerical terms, whereas qualitative data is descriptive and expressed in terms of language.

Monitoring

• This is an ongoing process of collecting and evaluating the performance of, say, a project, process, system, or another item of interest.

• Monitoring is another process that converts data into insight and as such, exists as a mechanism to guide decisions.

Insight Management

• Data forms the building blocks of many business functions. In support of decision-making — arguably its most important value — data is the source for almost all insight.

• As a basic definition, business insight is sometimes referred to as information that can make a difference.

Reporting

• Perhaps the most obvious manifestation of data and information management in any organization is the use of reports.

• Creating, delivering, receiving, and acting on reports are fundamental functions of any organization.

Other Roles for Data

• While we’ve gone over a number of the most visible uses of data in organizations today, it was not the intent to list every conceivable way that data is used.

• Artificial intelligence (AI): Data is considered the fuel of AI. It requires a high volume of good data. With huge quantities of quality data, the outcomes of AI improve.

• Problem-solving: Acknowledging the close association with decision-making, it’s worth calling out problem-solving as a distinctive use of data.

• Data reuse: While we collect and use data for a specific primary purpose, data is often reused for entirely different reasons. Data that has been collected, used, and stored can be retrieved and used by a different team at another time

Improving Outcomes with Data

• Now that the diverse roles of data have been identified and discussed, it’s useful to understand how data can be leveraged to acquire its maximum value.

• It begins with recognizing that data is an organizational asset.

• Perhaps the function that is most associated with data is the process of exploring it and looking for insights.

Approaching Data as an Asset

• An asset is something that is owned by a person, an organization, or a government with the expectation that it can bring some economic benefit.

• This includes the generation of income, the reduction of expenses, or an increase in net worth.

• An asset can be tangible or intangible. Tangible assets are physical things such as inventory, machines, and property.

  After it is processed from its raw form, data has the potential to create enormous economic value for all manner of stakeholders. Here are some examples of the economic value of data:

• Improves operations

• Increases existing revenue

• Produces new forms of revenue

• Builds relationships with customers and other stakeholders

• Reduces risk

• Bottom line: Data is an asset and for its value to be leveraged, it must be governed. This may be one of the most important motivations for good data governance.

Data Analytics

• Raw data is largely useless. If you’ve ever briefly glanced at a large data set that has columns and rows of numbers, it quickly becomes clear that not much can be gathered from it.

• In order to make sense of data, you have to apply specific tools and techniques.

• The process of examining data in order to produce answers or find conclusions is called data analytics.

Figure 8: Basics steps in data analysis

Data analytics has four primary types. Figure 9 illustrates the relative complexity and value of each type.

• Descriptive: Existing data sets of historical data are accessed, and analysis is performed to determine what the data tells stakeholders about the performance of a key performance indicator (KPI) or other business

• Objectives. It is insight on past performance.

• Diagnostic: As the term suggests, this analysis tries to glean from the data the answer to why something happened. It takes descriptive analysis and looks at the cause.

• Predictive: In this approach, the analyst uses techniques to determine what may occur in the future. It applies tools and techniques to historical data and trends to predict the likelihood of certain outcomes.

• Prescriptive: This analysis focuses on what action should be taken. In combination with predictive analytics, prescriptive techniques provide estimates of the probabilities of a variety of future outcomes.

Data Management

• Data management is not the same as data governance! But they work closely together to deliver results in the use of enterprise data.

• Data management is the implementation of data governance. Without data management, data governance is just wishful thinking. To get value from data, there must be execution.

  Figure 9: The relative complexity and business value of four types of analytics

  Data analytics involves the use of a variety of software tools depending on the needs, complexities, and skills of the analyst. Beyond your favorite spreadsheet program, which can deliver a lot of capabilities, data analysts use products such as R, Python, Tableau, Power BI, QlikView, and others.

  Who would choose to make decisions based on bad data?

• On the other hand, good data management can result in more success in the marketplace.

Governing Data

• Governing data means that some level of control exists to support a related policy.

• For example, an organization may decide that to reduce risk, there needs to be a policy that requires data to be backed up every day.

• To fully realize the potential of data in your organization means that data must be governed.

• People: While recognizing that data is increasingly created and used exclusively by machines without human intervention, handling and benefiting from data is still a highly people-centric exercise.

• Policies: A data policy contains a set of adopted rules by an organization that apply to the handling of data in specific conditions and for particular desired outcomes.

• Metrics: It’s largely true, what gets measured gets managed. In developing policies in support of data governance, you have to consider how each is measured.

Chapter 4

Transforming Through Data

In this chapter, we will be:

• Understanding the purpose of a data catalog

• Exploring data monetization

• Implementing data-driven decision-making

• Creating a data strategy.

Examining the Broader Value of Data

• In small organizations or when a business is first created, only a few systems are used, and team members know the type and location of most of the data that is available.

• You can imagine, for example, basic repositories for customer data, invoices, and marketing materials.

• As organizations grow and more systems are employed, eventually no single person knows what data is available and where it is in the enterprise.

• Without this knowledge, the ability to properly govern your data and leverage its value is greatly hampered.

• Without deliberate actions, data democratization becomes elusive.

  Knowing what data is available is essential for the following reasons:

• Better informed decision-making.

• Ensuring compliance and regulatory requirements.

• Lower costs by avoiding duplicate system and data efforts.

• Improved data analytics and reporting.

• Higher performing systems.

Data Catalogs

• You can take a few approaches to assist your organization so that your team members can find data.

• One option involves the creation of an enterprise search engine.

The three essential benefits of data catalogs are:

• Finding data: Helps users identify and locate data that may be useful.

• Understanding data: Answers a wide variety of data questions such as its purpose and who uses it.

• Making data more useful: Creates visibility, describes value, and provides access to information.

Figure 10: A basic orientation of the components of a data catalog

• A data catalog delivers a comprehensive inventory that provides an enterprise view of all data.

• This view provides essential insight that helps with leveraging data value and provides a robust tool to assist with data governance.

  A data catalog can contain three types of metadata: technical, business, and operational.

• Technical metadata: Data about the design of a data set such as its tables, columns, file names, and other documentation related to the source system.

• Business metadata: Organizational data such as a business description, how it is used, its relevancy, an assessment of data quality, and users and their interactions.

• Operational metadata: Data such as when the data was last accessed, who accessed it, and when was it last backed up.

• Examples of metadata include the following:

  Associated systems

• File names

• File locations

• Data owners

• Data descriptions

  With a data catalog, an organization can:

• Know what data it has (and by extension, know what data is missing).

• Reduce data duplication.

• Increase operational efficiencies and innovation.

• Understand data quality.

Data Analytics

• Intuitively, data can contain enormous business value, but it must be unleashed.

• Simply staring at pages of columns and rows of data is unlikely to reveal any notable insights.

• It may give you a headache or you might get lucky and discover a pattern in the data.

• Data analytics involves both specialized skills and software to explore data sets and extract insights that may be useful to an organization.

The source of data for analytics is one or a combination of the following:

• First-party data: Data that an organization collects.

• Second-party data: Data that is obtained from another organization.

• Third-party data: Aggregated data obtained from a provider.

  Typical uses of contemporary data analytics tools and techniques include:

• Vastly improved decision-making

• Focused marketing campaigns

• Understanding the competitive landscape

Data Monetization

• In Chapter 3, we went over the basis for data being considered an intangible asset of an organization.

• An asset is something owned that has the expectation of delivering value.

• Cost value method: Value is calculated by determining how much it costs to produce, store, and replace lost data.

• Market value approach: Value is calculated by researching how comparable data is being priced in the open market.

• Economic value approach: Value is calculated by measuring the impact a data set has on the business’s bottom line.

• With-and-without method: Value is calculated by quantifying the impact on cash flow if a data set needs to be replaced.

Data-Driven Decision Making

• Simply stated, data-driven decision-making (DDDM) is the process of using data to drive business decisions.

• Perhaps the most important value for most enterprises to derive from data is the ability to make better, more-informed decisions.

• Organizations that excel at DDDM achieve it through deliberate actions and investments.

Consider this six-step process to data-driven decision-making:

• Define the objectives: This step involves understanding the objectives relative to the effort and their alignment with organizational goals.

• Identify the data: In addition to using a data catalogue, enterprise search, or similar, this step requires engaging with impacted stakeholders.

• Prepare the data: After Step 2, you'll understand the degree of preparation you need. If the problem you’re trying to solve is narrow and the data is easily accessible and high-quality, you’ll be in pretty good shape.

• Analyze the data: Once you reach this point, the most exciting part begins. The assumption is that you’re using a useful analytics tool.

• Determine the findings: Once you have data that you can display in a variety of ways, you can ask questions about it.

• Take action: That’s all there is to this step. Make your decisions. If you’ve completed Steps 1-5 well, but no action is taken (assuming that’s not the decision based on analyzing the data since concluding that no decision is necessary is.

Developing a Data Strategy for Improved Results

Why does every organization need a data strategy?

• The purpose of any type of strategy is to agree on a set of guiding principles that inform decision-making in support of a desired outcome.

• In simple terms, it’s the roadmap on how to reach your goals.

Creating a Data Strategy

• Data maturity: This can be defined simply as the degree to which the organization already uses and optimizes data and has experience and skills, as well as the quality of the existing data.

• Industry and size: You can think of data prioritization through two frameworks: defense and offense. Defense deals with fundamental areas such as data security and quality.

  A data strategy should typically account for these five areas of data requirements:

• Identify: To find and make data usable, it must be clearly defined and described. This includes a file name, a file format, and metadata.

• Store: Design and develop the capabilities for supporting the place and process for hosting data and how it will be shared, accessed, and processed.

• Provision: Determine the processes to share and reuse data and define the guidelines for access.

• Process: Raw data must be transformed to become valuable. This includes processes for data cleansing, standardization, and integration with other data sets.

• Govern: Institute processes to manage and communicate data policies for data use within the organization.

Data requirements should consider these four data strategy components:

Alignment with the business: A data strategy is a subset of the overall business strategy. This means the data strategy must support and advance the larger goals of the organization.

Identifying roles and responsibilities: A strategy requires people to take specific actions. Without action, a strategy is a worthless document.

• Data architecture: This area relates to the processes, systems, and applications that support working with data. Basic areas include defining data storage needs and analysis tools.

• Data management: This area is the broad umbrella of activities that manage the full lifecycle of data in an organization.

Figure 11: The four components of a data strategy (Source: John Wiley & Sons)

Managing and Monitoring Your Data Strategy

• It would be quite the feat if you successfully design, create, and implement a data strategy in your organization.

• The organization that embraced this would be ahead of a lot of businesses and their ability to leverage the value of data and increase organizational performance will be enhanced.

• All strategies must be open to periodic modification. It’s not realistic to expect a strategy to be fixed for its duration in a fast-moving business world.

• Your evolving customer expectations, organizational needs, the economy, and more all play a role in forcing a strategy to adapt.

• Monitoring your data strategy means having the right metrics, getting feedback regularly from participants, and auditing related outcomes.