What is Data Governance?

What is Data Governance?

Data governance is a set of principles, standards, and practices that ensure your data is secure and consistent. It also makes your data more reliable to run business processes, make the right decisions, and power digital transformations. A successful data governance program allows you to do this in a repeatable way. This type of program can scale and adapt as data volumes — and resources — increase and technologies evolve. In short, good data governance means you can use your data safely, both now and in the future.

Why is Data Governance Important?

Data governance ensures that your data is consistent and accurate and that you can rely on it for data-driven decision-making. Traditional data governance is focused on risk and compliance. However, in the last few years, the focus of data governance has changed. The acceleration of digital transformation and the huge increase in data volume, data distribution, data-driven regulations and the number of users who want to be empowered with reliable data have contributed to this change. Data governance has moved from a strict, one-size-fits-all approach to a more agile version that enables the data intelligence needed to make data-driven decisions.

See how smart data management allows you to bring people, processes, and systems together to deliver strategic business results.

Issues such as the abundance of data, users and legal regulations may seem difficult to tackle. However, the biggest problem is ensuring assurance in the quality and protection of data. Users must have access to appropriate and reliable use of the data, and all team members must be authorized to use the data safely in order to create value from that data. Effective data governance can help build trust in this data. Data governance can disseminate trusted data to allow data consumers at different technical levels in an organization to execute analytics, artificial intelligence (AI), and data-driven digital transformation.

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What is the Data Governance Framework?

Data governance requires your organization to understand and evaluate data. What regulatory and legal requirements apply to your data? Which business applications are best suited for your business?

Once you have this understanding, you need to establish the rules and determine the automatic processes and processes that will be carried out by human hand to implement these rules. The most important determinants of data governance are usually regulations and legal requirements, but the organization can also decide which rules to include.

Governance often dictates policies, such as the retention of certain types of data. It also codifies data protection methods such as encryption or strong password. Data governance can dictate how data is backed up, decide who has access to data, and set guidelines for when you should destroy archived data. You can also set governance goals around issues such as data quality or silos that isolate certain data.

You often hear data governance “frameworks”. A data governance framework consists of data strategy policies that affect everyone in the enterprise:

· People (roles)

· Processes and procedures

· Technologies

If data governance is the “what”, it is the “how” in a data governance framework, and aligning it with your strategy is the “why”.

What are the Fundamentals of Data Management?

A successful data governance program consists of four key elements:

· Vision and business rationale

· The right people

· Intelligent data governance technology

· Efficient processes

The vision details your overarching strategic objective to establish a governance programme. The business rationale also clearly tells about specific job opportunities. In other words, your vision is where you will arrive, and your business rationale is the tool you will use to get there.

A vision statement, although comprehensive, should be actionable, not abstract. It should cover the first 3 to 5 years. Below is an example of a concrete vision for data governance:

“Create a better customer experience by reducing time spent resolving issues, using more relevant marketing materials, and protecting sensitive customer data.”

You must also have a viable business rationale. Data governance should identify the real people (roles), technologies, and processes you will need to support your efforts. Your vision lays the foundation for the policies you intend to implement and align your business with your data-driven business goals.

Data Governance Roles

To get accurate outputs on your data, you must define the right roles. These roles will support, sponsor, provide services, and make your data functional.

First, you need a board of directors or a data governance board. This team communicates, prioritizes, funds, resolves conflicts and makes decisions about data governance for all of your business. The board of directors is made up of senior managers of your business. Sometimes these people belong to the C-level group (CIO, CDO, CTO, etc.). There may also be vice presidents or managers in charge of specific business areas.

Senior people on your board play an important role, but the board alone is not enough for this process. An effective data governance program should include:

· Executive Sponsor: He is a C-level manager whose responsibilities cover various silos. These silos can span business, application, and geographic silos. Identifying this person early is important for success. This person allocates resources, determines staffing and funding, identifies high-priority business issues, and promotes inter-task cooperation.

· Data controllers: Data controllers are business and IT subject matter specialists (SMEs). They are the people who interpret how your data governance framework will affect your business processes, decisions, and interactions. Business people should be knowledgeable about IT. Similarly, IT managers must understand the business. Experienced business analysts, who act as a communication bridge between business and IT teams, can be the best business managers. Data architects and senior business systems analysts can also be strong IT managers.

· Data governance leader: This person coordinates the tasks of data controllers and they help communicate the decisions made by those responsible. They also conduct continuous data audits and establish metrics that assess the success of the program and ROI (return on investment). Together with the executive sponsor, they can be the primary point of communication to the board of directors.

Data Governance Tools and Technology

Four Features of an Intelligent Data Governance Solution

The features of intelligent data management are automation, scale, flexibility and agility.

To initiate organizational transformation and achieve better business results, you must bring data together through people, processes, and technology.

In the context of data governance, “technology” means automation. Many technology solutions and platforms can help you automate data management. To choose the right one, you must consider the entire life cycle of critical data, from its creation to archiving.

You also need to focus on intelligent automation. Smart automation has four key features:

1. Automation

2. Scale

3. Flexibility

4. Agility

Data Governance Policies and Procedures

Business policies and standards are crucial to any data governance program. It is important to decide on policies that can be applied across the business. Typical policies include:

· Data responsibility and ownership

· Business roles and responsibilities

· Data capture and validation standards

· Information security and data privacy guidelines

· Data access and usage data storage

· Data masking

· Data archiving policies

The culture of every business is different. There is no set of right or wrong policies to be aware of. Today's successful data governance programs work together and focus on enhanced collaboration. Also understanding that the application does not have to be restrictive, decide together what is best for the business. By rotating on this axis, you move your data governance agenda from policy-centric to value-centric.

Best Practices in Data Governance: 5 Steps to Take as You Begin Your Data Governance Journey

If you're just starting to explore data management, below is a simple five-step roadmap to help you get ahead.

Step 1: Choose a project

Your first data governance initiative and choosing the right project are important. If this is your first attempt at data governance, you need to be able to demonstrate business value. And that means you need to deliver a solid return on investment (ROI) — or at least return on effort — within a reasonable amount of time. If possible, find a project that will excite senior management. Be able to provide metrics that show tactical success as well as progress on long-term goals.

Step 2: Set your goals

What do you want to achieve? This is not a question asked just to be asked. Many governance programs fail because goals are too vague or expectations differ. Below are some examples of the most common data governance goals:

· Improve the efficiency of critical processes that have been damaged by poor quality data in the past

To better and more effectively comply with legal regulations (may include risk reduction and avoidance of punishment)

· Consistent use of reliable data across the enterprise to execute each tactical and strategic decision

Step 3: Choose the right people and organize them appropriately

Data governance programs cover a large number of people. Even if your actual data governance team is small, your project will impact a large number of employees, customers, partners — in short, everyone who relies on your data. Many of these people will have opinions, and some will voice it out loud. Do not be afraid of this. Embrace their passions, but organize.

Use a responsibility assignment matrix such as RACI (responsible, accountable, consulted, informed). This ensures that the right people provide input at the right time and everyone knows their individual responsibilities.

The person in charge of doing the job (responsible) can be an experienced project manager. This person manages the planning, allocates resources and forms the rationale for the work.

· Undertakes major decisions and outcomes regarding the accountable program. It can be a senior person who has the resources and has veto power.

Consulted persons are business and IT professionals. They are the ones who will help you provide the necessary context to achieve your goals.

Informed people are the people who will be affected by your data governance initiative. They do not have the right to have a direct say in the direction of your initiative — and this is something you will need to make clear at the beginning of the process.

Step 4: Build your processes

The data governance team needs clearly defined, repeatable processes that are designed to bring their tasks to life. There are four key processes that support each data governance program:

· Discover: Identify and understand managed data.

· Define: Document data definitions, policies, standards, and processes. Assign ownership (an important but often overlooked step) and identify your key metrics and key performance indicators (KPIs).

· Implement: Functionalize data governance policies, business rules, and accountability.

· Measure and track: Measure the value of your data governance initiatives and monitor compliance with your policies.

Step 5: Choose your technology

Data governance initiatives are constantly evolving. New data projects and regulations (and new risks) are constantly emerging. Today, you need a technology platform that offers value but can adapt and evolve as your requirements change. Below are some key considerations to consider when choosing your data governance technology:

· Focus on flexibility, agility, and interoperability, so you can grow and climb upward as business needs change.

· Have the capacity to address all critical needs such as data cataloging, data responsibility and governance, data quality, and data sharing and democratization.

· Automate processes, workflows, data discovery, and reporting.

· Consider cloud technology for scalability gains.

· Set up a top data pool.

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As Komtaş, we are ready to support you in all processes with the data governance solution we designed end-to-end, with the appropriateness, integrity and use of the data that companies need within the scope of data governance.

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