Data Governance

Although it is a fact that data can be one of the true differentiators of a company if used correctly, many organizations still do not have a comprehensive data strategy, however, for some companies that have a formal strategy, such a strategy is more rather than a technical exercise, the main objective of which is to establish the basics of data management, compliance and similar basic requirements.

“Does your company have a formal data strategy? If so, does that strategy effectively chart a path to better business results by helping you optimize your use of data?”

Here he is, why? From my previous article, Does Information Have Value? Of course, it is the data that is collected in ERPs, CRMs, for example, as the amount of business data continues to increase, companies are increasingly recognizing the importance of data governance:

The framework for managing an organization's data assets to achieve accuracy, consistency, security and effective use must be considered very specifically, so we must consider our data transformed into information and its value to the business.

Today we will explore what data governance is, the key components of a data governance framework, and best practices for implementing a successful data governance strategy.

Whether you're just getting started with data governance or looking to refine your existing program, this article will provide valuable information and actionable advice to help you manage your data more effectively.

What is data governance?

Data governance is a broad term that refers to a critical set of organizational policies, processes, roles, standards, and metrics to ensure the accuracy, consistency, security, and availability of data, as well as its effective use.

The Data Governance Institute defines it as “a system of decision rights and responsibilities for information-related processes, executed according to agreed models that describe who can take what actions with what information, when, under what circumstances, and using what methods”

The Institute also highlights the need to be cautious when using the term governance, as it can have different meanings depending on the context.

Data governance is a crucial aspect of managing an organization's data assets, so the primary goal of any data governance program is to meet priority business objectives and unlock the value of your data throughout your organization. .

I recommend you keep in mind that a data governance program cannot exist on its own, it is not like this, it must solve business problems and generate results, let's start by identifying the business objectives, the desired results, the key stakeholders and the data necessary to achieve these goals, for example, pay close attention to data technology and architecture that play a crucial role in enabling data governance and achieving these goals.

• In terms of human resources, people refer to the organizational structure, roles, and responsibilities of those involved in data governance, including those who own, collect, store, manage, and use data.
• Policies provide guidelines for data use, protection, and management, ensuring consistency and compliance.
• Process refers to procedures for communication, collaboration, and data management, including data collection, storage, protection, and use.
• Technology refers to the tools and systems used to support data governance, such as data management platforms and security solutions.

Key areas where data governance adds value

Data governance scopes vary from organization to organization, and there is no fixed set of tasks that applies to all, however certain areas are commonly addressed in data governance, such as those listed in the following examples:

1. Data quality involves storing data in its correct and consistent form, here is a deep dive into data quality management and tools.
2. Data availability is responsible for making data accessible to the appropriate personnel within the system.
3. Data usability ensures that data is available in a structured format that is compatible with traditional business tools and software.
4. Data integrity is about maintaining the quality of data as it is stored, converted, transmitted, and displayed.
5. Data security is the set of processes that prioritize certain data based on its sensitivity and implement measures to protect it across all online and offline platforms.
6. Data modelling involves creating a conceptual representation of data objects and their relationships to one another, as well as the rules that govern those relationships.

To design an effective data governance program, it is essential to choose an operating model that fits the size and structure of your company, you have to take this into account.

Data Governance Models

There are three basic data governance models:

Centralized government model:

A central data governance team or leader manages the organization's data assets and establishes policies, processes and standards for data use and management, the beneficial part of centralized governance is that it allows consistent standards and definitions to be applied to the data across the organization, which reduces confusion and duplication of efforts, however, centralized models can fuel bureaucracy and overlook the unique needs of different areas of the business.

Decentralized government model:

The different business units within the organization have their own data governance teams and are responsible for managing their data assets, the benefits include improved representation, better data, greater efficiency and shared maintenance, the latter being a set of entities of consistent and reliable data that is shared across an organization and used to support business processes and decision-making, however decentralized models can lead to duplicate and inconsistent master data.

Hybrid (federated) governance model:

There is a centralized structure that provides a framework that is then used by autonomous departments that own their data and metadata, benefits include more autonomy, faster issue resolution, and improved agility, however, a hybrid model requires a centralized body that has a large Experience in each business area and coordination to ensure data consistency.

I think that ideally, regardless of the chosen model, data governance covers all the strategic, tactical and operational aspects of data management, which leads us to the need to distinguish between these and other terms.

Let's listen to this position:

Data governance vs. data management vs. data stewardship vs. master data management, it sounds complex, like we want to find the circumference of the triangle, well, that's right, if it's easy to get confused, look:

The term data governance is often confused with concepts such as data management, data stewardship, and master data management, so it is crucial to differentiate between these terms, as each one plays a distinct role in ensuring data management, use, and adequate data protection.

Data management is the overall process of collecting, storing, organizing, maintaining, and using data, essentially, the fundamental principle underlying this process is to recognize data as a valuable resource, given its important role in driving business success. So, data management is a technical implementation of data governance and involves the practical aspects of working with data, such as data storage, retrieval, and analysis.

Data governance, on the other hand, focuses on the strategic and organizational aspects of data management, such as establishing policies and procedures for data use and ensuring compliance with regulatory requirements, a component of data management, Data governance is a higher-level strategic concept that also encompasses elements such as data quality, data privacy, and data security.

DAMA International has published that the DMBOK is a model for data management where data governance resides at the centre.

Data governance is a subset of data governance that involves the management and oversight of an organization's data assets, so data stewards are responsible for ensuring that data is accurate, complete, and consistent with policies and organization standards.

Master data management (MDM) and data governance are related, but distinct practices in data management, in this case MDM focuses on improving the quality of specific business-critical data types, such as customer data or providers, and ensuring they are consistent across the organization, on the other hand, data governance provides a broader framework for managing all data, including defining data models, retention policies, and roles and responsibilities, although MDM is a subset of data governance, proper governance is crucial to a successful MDM implementation.

At their core, data management and data governance are different, but complementary concepts that work together to ensure that data is managed effectively and efficiently within an organization.

“Data governance provides the general framework for data management with data stewardship as part of this, while data governance focuses on the practical implementation of data related tasks”

Creating a data governance program is an iterative and incremental process.

Define your data strategy and data governance goals and objectives

What are the business goals and desired outcomes for your organization?

You need to consider both long-term strategic goals and short-term tactical goals, and goals can be influenced by external factors, such as regulations and compliance.

A data strategy: Identifies, prioritizes, and aligns the business objectives of your organization and its various lines of business, across multiple business objectives, a data strategy will identify data needs, measures and KPIs, stakeholders, and required data management processes, technology priorities, and capabilities.

It's important to regularly review and update your data strategy as your business and priorities change. If you don't have a data strategy, you should create one; It doesn't take a lot of time, but you need the right stakeholders to contribute.

Once you have a clear understanding of business goals and data needs, set data governance goals and priorities, let's say, an effective data governance program can be:

• Improve data quality, which can lead to more accurate and reliable decision-making.
• Increase data security to protect sensitive information.
• Enable compliance and reporting against industry regulations.
• Improves the overall trust and reliability of your data assets.
• Make data more accessible and usable, which can improve efficiency and productivity.

“Clearly defining your goals and objectives will guide the prioritization and development of your data governance program, ultimately driving revenue, cost savings, and customer satisfaction”

Secure executive support and essential stakeholders

Identify the key stakeholders and roles for the data governance program and who will need to be involved in executing it, this should include employees, managers, IT staff, data architects, and line-of-business owners and data custodians inside and outside your organization.

An executive sponsor is crucial:

Someone who understands the importance and goals of data governance, who recognizes the business value that data governance enables, and who supports the investment required to achieve these results.

With the key sponsorship established, assemble the team to understand the compelling narrative, define what needs to be achieved, how to raise awareness, and how to build the funding model that will be used to support the implementation of the data governance program.

By effectively engaging key stakeholders, identifying and delivering clear business value, implementing a data governance program can become a strategic advantage for your organization.

Evaluate, create and refine your data governance program

With your business objectives understood and your data governance sponsors and stakeholders in place, it is important to map these objectives against your existing people, process and technology capabilities to achieve these objectives.

Analysis of the current state of data governance

Data management frameworks, such as the EDM Council's DCAM and CDMC, offer a structured way to assess the maturity of your data against industry benchmarks with a common language and set of data best practices.

Have a look at how data is currently governed and managed within your organization.

• ?????What are the strengths and weaknesses of your current approach?
• ?????What is needed to meet key business objectives?

Remember, you don't have to, nor should you do everything at once, identify areas for improvement, in the context of business objectives, to prioritize your efforts and focus on the areas most important to deliver business results in a meaningful way, a program of effective and efficient data governance will support your organization's growth and competitive advantage.

Document your organization's data policies

Data policies are a documented set of guidelines for how an organization's data assets are consistently governed, managed, protected and used, such data policies are driven by your organization's data strategy, align with business objectives and desired results, and can be influenced by internal and external regulatory factors.

Data policies may include topics such as data collection, storage and use, data quality and data security:

Data policies ensure that your data is used in a way that supports the overall objectives of your organization and complies with relevant laws and regulations, this can lead to better data quality, better decision-making, and greater confidence in the organization's data assets, ultimately leading to a more successful and sustainable organization.

Establish roles and responsibilities

Defining clear roles and responsibilities of those involved in data governance, including those responsible for collecting, storing, and using data, will help ensure that everyone understands their role and can effectively contribute to the data governance effort.

The structure of data governance can vary by organization, for example, in a large company, data governance may have a dedicated team overseeing it, while in a small company, data governance may be part of the existing roles and responsibilities.

A hybrid approach may also be suitable for some organizations, it is crucial to consider the culture of the company and develop a data governance framework that promotes data-driven practices, the key to success is to start small, learn and adapt, while continuing focuses on delivering and measuring business results.

Having a clear understanding of the roles and responsibilities of data governance stakeholders can ensure that they have the skills and knowledge to perform their roles.

Develop and refine data processes

Data governance processes ensure effective decision-making and enable consistent data management practices by coordinating teams within and outside your organization, and data governance processes can also ensure compliance with regulatory standards and protect confidential data.

Data processes provide formal channels for direction, escalation, and resolution, so data governance processes must be lightweight to achieve your business goals without adding unnecessary burden or hindering innovation.

Processes can be automated through tools, workflow, and technology.

It is important to establish these processes early to avoid problems or confusion that may arise later in the implementation of data management.

Implement, evaluate and adapt your strategy

Once you've defined the components of your data governance program, it's time to put them into action, this could include implementing new technologies or processes or making changes to existing ones.

Data Governance Program:

Driving data transformation and fuelling a data-driven culture, it's important to remember that data governance programs can only succeed if they demonstrate business value, so you need to measure and report on the delivery of prioritized business results, Regularly monitoring and reviewing your strategy will ensure that it meets your business goals and objectives.

Continually assess your goals and objectives and adjust as necessary, this will allow your data governance program to evolve and adapt to the changing needs of the organization and industry, maintaining a continuous improvement approach will allow your data governance program to Data remains relevant and delivers maximum value to the organization.

Data governance is a whole issue, especially for large companies that handle a huge volume of data, the current reality is that we are all data consumers and generators, in legal, commercial issues, regulations between countries, we must observe a good data governance, I do not want to leave out the financial, credit, banks, treasury entities of each country, etc.

I await your comments, doubts, any sign, I ask you to tell me, if I am doing well or if I return, to my friends outside of Mexico, I also invite you to participate in this great world of information technology, I know that many we are on the side of cybersecurity, this article talks about what to take care of, one of the most valuable assets today, anywhere, receive many greetings from Toluca, State of Mexico.

I hope you find it useful, regards.

His friend,