Data-Governance

Defining Data Governance

• What is data governance
• Data governance elements
• The role of data culture
• Data governance readiness

Data governance basically a framework about proactively managingthe quality of your data to help your organisation achieve its strategy and goals. It is all about managing data correctly . If a company's data is managaed well it can spark immense growth and can become the most abundant and important lever for success.

Data Governance Versus 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.

Data Governance versus Information Governance Data governance generally focuses on data, independent of its meaning. For example, you may want to govern the security of patient data and staff data from a policy and process perspective, despite their differences.

The interest here is in the data, not as much in the business context.

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 advantages of implementing data governance in a organization:

• Improved data quality
• Expanded data value
• Increased data compliance
• Improved data-driven decision making
• Enhanced business performance
• Greater sharing and use of data across the enterprise and externally
• Increased data availability and accessibility
• Improved data search
• Reduced risks from data-related issues
• Reduced data management costs
• Established rules for handling data

Elements of the data governance program.

Developing a data governance This below figure distills down the most important qualities of a data governance framework:

Leadership and Strategy:

The data governance program implemented in your program must align with the organizations strategy. Data plays an important role in an organizations strategy, including risk management, innovation, and operational efficiency, so you must ensure there’s a clear alignment between these aspects and the goals of data governance.

Roles and Responsibilies

To allow for a data governance program to success almost all members of the organization has to be responsible for something. Every data governance framework includes the identification and assignment of specific roles and responsibilities.

Policies, Processes, and Standards

Policies, processes, and standards must include accountability and enforcement components; otherwise it’s possible they will be dead on arrival.

Metrics

The data governance program must have a way of measuring the performance to identify when it is delivering the intended 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.

Preparing for Data Governance

In order to implement data governance you first need to prepare the organization to be ready to accept it.

Being ready as an organization involves determining the extent to which a data culture exists. Intuitively you can conclude that an immature, reactive data culture, where data is simply handled in an ad hoc manner, is an entirely different experience than a sophisticated data-driven culture.

Maturing Data Culture

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.
• Senior leadership understands this is a strategic, enterprise program and not the sole responsibility of the IT department.
• 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).
• Legal and compliance teams (internally or externally) understand and support the goals of the program.
• Fundamental data skills exist for the data governance journey.
• The IT organization is capable and resourced to support the program.

Chapter 2:Exploring a world with overflowing data

Defining Data

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.

The zettabyte era

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.

Examples of Data Volume:

Quantification of Data Storage:

The qualitative and quantitative nature of data types:

From Data to Insight

Differences between Information and Data:

Data leads to insight:

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. In this way, it’s fair to say that data has always been important.

Something is quite clear though. The value data that has been added over the course of many decades has not remained flat. On the contrary, since the mid-20th century, as more computer systems came online, the role of data has grown. It’s not just that the quantity of data began to increase rapidly, but it was also the quality of the data and the availability of it to so many people. In particular, the arrival of the Internet in the mid-1990s resulted in the reality of the Information Industry Association’s 1970’s motto, “Putting information at your fingertips.” In the late 1980s, Bill Gates, co-founder and former CEO of Microsoft, would later build on this and champion the idea that eventually, “any piece of information you want should be available to you.”

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. It’s valuable to you, but it’s also valuable to the restaurant owner. Those reviews can make a big difference, including being a motivation for action. Perhaps the restrooms should be cleaner.

Deciding on a restaurant based on reviews is an example of data-driven decision-making, but it’s also on the less complex end of the decision-making spectrum.

Deciding to enter a new market with an existing product or service requires a deep understanding that can come from rich sets of data. If the data exists and you have the tools to process and interpret it, you may be well-positioned to make the right decision. It may also be easier to decide because you’re able to get the answers to your concerns. Conversely, without good data and the skills and tools to analyze it, a bad and costly decision may result. This happens far too often.

Data Ownership

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

Data Architecture

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

Data architecture is the manner in which data design and management decisions are being made to align with EA and in turn, with the business. Simply stated, data architecture is the agreed blueprint for how data supports an organization’s functions and technologies.

The Lifecycle of Data

• Creation is the step where data comes into BEING!

• Storage is used to store the created data for later use.

• Usage is the step where you actually do something with your precious data

• Archival is the stage in which data, which is not being used, is moved to a long term storage system for the future!

• Destruction of data makes sense if it comes to a point where the data is not needed anymore, or is required by a regulation or policy. Data destruction involves making data inaccessible and unreadable. It can include the physical destruction of a device such as a hard drive. DECODE DESTRUCTION!

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.

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. For example, marketing teams can target potential customers with precision. Analytics applications can use high-quality real-time data generated in a manufacturing setting.

Smart data uses new processes and tools to make the data most useful. For example, the increasing use of artificial intelligence (AI) is now being applied to find patterns in unstructured big data and extract the data that is most relevant for a given application. Using new methods such as AI reduces time, lowers errors, and enables the creation of data subsets that may not have been previously possible. In addition, smart data solutions are often applied at the point of collection rather than a post-processing solution.

Chapter 3:

Identify the roles of data

Operations: Strategy: Decision-Making: Measuring: Monitoring: Insight Management: Reporting: Other Roles for Data:

Improving Outcomes with Data

After identifying and discussing the various roles that data may play, it's helpful to know how to use data to get the most value possible. Realizing that data is an organizational asset is the first step. This merely indicates that the item adds financial value to the company. When brought up, this is easy to observe, yet many team members still do not view data in this manner. When data is viewed as an asset, particularly a high-value asset, it is frequently handled differently...

Approaching Data as an Asset

You’ve seen that data has value and you’re ready to take steps to manage it that way. Just like assets on the balance sheet, you need to know what you have and where it is, as well as have an expression of its value. It may not be a dollar amount, but you can probably measure it in terms of how essential it is to the success of the business. By the way, if data has no value, seriously consider whether you should be managing it.

Data Analytics

Raw data is useless by itself. In order to make sense of data one needs to put procedures and processes in place. The process of examining data in order to make sense of it and to convert it into information is called data analytics.

Data analytics has four primary types.

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.

The relative complexity and business value of four types of analytics

Data Managment

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. The control would be the documentation of the process and enforcement of that policy. If, in the review of policy adherence, data wasn’t getting backed up, then you’d quickly know that governance, for whatever reason, was not working.

Chapter 4: Transforming through data

Examining the Broader Value of Data

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.
• More efficient operations.
• Reducing data inconsistencies across the enterprise.

Data Catalogs

A data catalog lists the availability of data sets and includes a wide range of valuable details about that data.

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.