Data Governance Framework: Four Pillars For Success
Creating a data governance framework is a must-have for organizations that want to be truly data-driven. A data governance framework provides the essential structure thats needed for the core elements of data managementdata privacy and data security.
Despite the importance of prioritizing data governance, it is a difficult project for many organizations. A data governance framework eliminates the challenges by setting a clear path for building and maintaining a data governance program. It guides everything from identifying requirements and developing a plan to establishing a team and implementing the plan.
Done well, a data governance framework enables organizations to make the implementation of a data governance plan a smooth process that does not necessarily require a complete overhaul of existing data management systems. It helps organizations align and catalog existing data assets and establishes processes for ensuring that new data is organized and stored according to the data governance guidelines.
Lets jump in and learn:
How Do You Set Up A Data Governance Program
When I say start small, I mean really small. For example, choose a single yet critical reportthe one your institution requests over and over, or the one that has grown exponentially over the past five years as new questions keep getting asked. Take this report and follow these six steps to start a data governance program that will allow you to scale systematically and swiftly:
Benefit #3 Protects The Integrity And Relevance Of Data
For data to really be the bedrock of impactful decisions it must be relevant, of high quality and accurate, trustworthy, and easy to use. Data governance ensures the following by employing:
- Tracking and managing of all data in an organization with help of data catalogs
- Creating business glossaries for ease of understanding of this data
- Setting up data lineage to enable root-cause analysis and impact analysis
- Management of data access via established and communicated policies
Automate lineage via SQL parsing. Image by Atlan
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Data Governance Principles And Best Practices
When creating the framework needed for your data governance, you’ll need to create one that fits the objectives of your organization. Some things you’ll need to think about are how to use your data properly, improve data security, create and enforce data distribution policies, and keep in compliance with all regulatory requirements.
Think With The Big Picture In Mind But Start Small
Data governance is a combination of people, process, and technology. To begin building the big picture, start with the people, then build your processes, and finally incorporate your technology. Without the right people, its difficult to build the successful processes needed for the technical implementation of data governance. If you identify or hire the right people for your solution, then they will help build your processes and source the technology to get the job done well.
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What Do I Need To Know About Data Governance
Data governance requires an organization to understand and take stock of regulatory requirements, legal requirements, and business best practices which their data must meet, then establish rules and adopt automated and human processes to enforce the rules. The drivers of data governance are usually regulatory and legal requirements however a governance rule can be any practice to which the organization wishes to adhere. Governance often dictates where certain types of data may be stored and codifies data protection methods, such as encryption or password strength. Governance can dictate how to back up data, who has access to data, and when archived data should be destroyed. Organizations can also set governance objectives around improving data quality or breaking down silos that isolate certain data.
You often hear about data governance frameworks. A data governance framework consists of the rules, people roles, processes, and technologies that work together to align everyone in the organization on your data governance strategy. If data governance is what, then a data governance framework is how.
This article defines data governance, identifies four core components of successful data governance, and provides five actions to begin your data governance journey.
Benefit #2 Builds Effective Collaboration Between Teams
Having a clear understanding of who should have access to data and who shouldn’t, also reduces the friction between diverse data practitioners existing in same or different teams. For e.g. A person looking at a restricted data asset knows who owns it, and can quickly request access with the click of a button. Also, if a particular data is publicly discoverable, no matter if it’s in a different domain, a data user doesn’t need to wait for days for IT to give them access to that data or domain experts to relay what that data actually means.
Managing users is easy for Data Stewards, via, group, actions, or even personas. Image by Atlan
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What Is A Data Governance Strategy
Data governance focuses on the daily tasks that keep information usable, understandable, and protected. A data governance strategy consists of the background planning work that sets the holistic requirements for how an organization will manage data consistently. This includes:
- Assigning responsibility for implementing the policies and processes,
- Defining policies for sharing and processing data,
- Creating processes for naming and storing data,
- Establishing measurements for keeping data clean and usable.
A data governance strategy provides a framework that connects people to processes and technology. It assigns responsibilities, and makes specific folks accountable for specific data domains. . It creates the standards, processes, and documentation structures for how the organization will collect and manage data. This ensures integrity by keeping data clean, accurate, and usable. Through this foundation, you ensure secure data storage and access.
Governance Activities That Support Program Management
The governance of the program is primarily done through the program board and the program manager. However, there may be other activities or functions within the organization that also help the program manager in achieving these goals. In this section, we will look at five of these activities or functions. These are:
- Program management office
- Program management audit support
- Program management education and training.
There are a variety of PMO roles in organizations, consisting of different shades of roles and responsibilities. So what the PMO does in an organization may be very different from what PMI intends the PMOs role to be.
One of the best ways to express the role of a PMO is to call it the Center of Excellence for program management in an organization. This means that the best of the program management expertise, knowledge, and skills lie within the PMO.
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Q: Is Data Governance A Program Or A Project
A: Data governance should be viewed long-term strategic business program, not a single short-term project. Implementing data governance requires structural changes to a companys current data policies and practices, in addition to redefining the roles and responsibilities of data handling personnel.
Data Protection And Data Privacy
The increasing awareness around data protection and data privacy, for example, manifested by the European Union General Data Protection Regulation have a strong impact on data governance.
Terms such as data protection by default and data privacy by default must be baked into our data policies and data standards not least when dealing with data domains such as employee data, customer data, vendor data and other party master data.
As a data controller, you must have full oversight over where your data is stored, who is updating the data and who is accessing the data for what purposes. You must know when you handle personally identifiable information and do that for legitimate purposes in the given geography both in production environments and in test and development environments.
Having well-enforced rules for the deletion of data is a must too in the compliance era.
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Key Challenges To Effective Data Governance
The power of data in driving business growth is well known today. Effective data governance allows organizations to get maximum benefits from their most valuable asset. With high-quality data, businesses are able to gain insights for better business decisions and increase efficiency and productivity.
Moreover, data governance also protects the business from compliance and regulatory issues which may arise from poor and inconsistent data.Gartner predicts that through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business. Here are some common challenges organizations face while establishing data governance frameworks and policies:
Building A Data Governance Framework Even Before You Knew What To Call It
Many aspects of what we call a data governance framework have been part of my career for almost two decades. While Im not an IT person, I was a power user of business applications in my first few jobs and served as the line-of-business contact for a few IT projects.
As a counterpart to the IT group, I learned how things that looked simple on the business side can be ridiculously complex from IT’s point of view. For example, in my first job, we wanted to automate the ordering of spare parts for manufacturing companies through an e-commerce portal. The biggest stumbling block? Catalog data, which was often filled with incomplete descriptions or inconsistent part numbers. Worse, there was a lack of a common nomenclature across multiple vendors. The program ground to a halt due to bad data, and we lacked a framework to get different groups together to fix it.
In 2003, I went to work for a data management company, and I began to realize that many of these problems were symptomatic of data issues overall. Then, I talked to an executive at a global manufacturer about a systems migration she was sponsoring. The executive mentioned that her company was prepared to spend almost a year getting business and IT groups to agree on the rules in place before the migration to a new system even started.
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Grow Up Kid: The Maturity Model
Measuring your organization up against a data governance maturity model can be a very useful element in making the roadmap and communicating the as-is and to-be part of the data governance initiative and the context for deploying a data governance framework.
One example of such a maturity model is the Enterprise Information Management maturity model from Gartner, the analyst firm:
Most organizations will, before embarking on a data governance program, find themselves in the lower phases of such a model.
Phase 0 Unaware: This might be in the unaware phase, which often will mean that you may be more or less alone in your organization with your ideas about how data governance can enable better business outcomes. In that phase you might have a vision for what is required but need to focus on much humbler things as convincing the right people in the business and IT on smaller goals around awareness and small wins.
Phase 1 Aware: In the aware phase where lack of ownership and sponsorship is recognized and the need for policies and standards is acknowledged there is room for launching a tailored data governance framework addressing obvious pain points within your organization.
Phases 4 and 5 Managed & Effective: By reaching the managed and effective phases your data governance framework will be an integrated part of doing business.
The Data Governance Framework
A data governance framework is a set of data rules, organizational role delegations and processes aimed at bringing everyone in the organization on the same page.
There are many data governance frameworks out there. As an example, we will use the one from The Data Governance Institute. This framework has 10 components lets discuss in detail:
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Why Businesses Need Data Governance
Businesses use data governance to get the most from customer data.
Being able to quickly review information and make informed decisions based on real-time metrics not only minimizes risk it also helps your company capitalize on timely upselling and cross-selling opportunities.
Another crucial benefit of data governance is security. In a survey by McKinsey, 87% of respondents said they would not do business with a company if they had concerns about its security practices, and 71% said they would stop doing business with a company if it gave away sensitive data without permission. By implementing a data governance framework, you can ensure your customers’ data is safe from potential harm.
Considering all these benefits, it makes sense that the Data Governance Market is growing. According to data from Mordor Intelligence, it was valued at 1.81 billion US dollars in 2020 and is projected to be worth 5.28 billion by 2026.
Constantly Adapt Your Data Governance Framework
Businesses change, and so too their data strategies. Companies need to continuously adapt and improve their data governance processes. This allows them to respond to issues like rising data privacy risks as they emerge.
Organizations need automation that helps track and measure the effectiveness of their strategies to:
- Determine policy conformance
- Analyze curation
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Be Transparent With External Stakeholders
You should always be transparent with your external stakeholders customers, partners, investors, suppliers, etc. about business functions and changes. In this case, they should all be made aware of your data governance program before you set it into place. You want your stakeholders to know that the security and validity of your company’s data is a main priority.
Start With A Small Sample Size
It’s best not to kick off your data governance program with a complex or long-term project. You might make errors or lose motivation from the team. Rather, begin with a smaller, more manageable project, like analyzing data for one team. Assess the state of the data, specifically its collection, storage, and usage, then decide how much of your budget will be invested in the initiative.
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What Is Cloud Data Governance
In multi-cloud or hybrid cloud computing systems, when data gets stored in various locations, and cloud data governance protocols such as permissions, guidelines, and metadata are inconsistent across databases, data governance takes on a new level of sophistication. Moreover, modern tools from a single software platform that enable data engineers, data governance and compliance teams to automate data governance, data access rules, and privacy protection can help data teams negotiate the complexities of data governance.
The activity of managing data availability, authenticity, consumption, and security in cloud computing systems to fulfill critical business objectives is known as data governance. These objectives most likely include the following listed below.
- Increasing the privacy and security of data.
- Access to sensitive data is regulated and monitored.
- Using timely data analytics to improve operations and corporate decision-making.
- Obtaining and ensuring compliance with data privacy and security standards on an ongoing basis.
- Data breaches and other cyber security threats get avoided.
Determine The Right Level Of Data Governance:
There isnt a one-size-fits-all approach to data governance and there are several factors that will determine the right level for your organization. The program you implement will vary depending on the:
- Size of your organization and complexity of your data.
- Resourcestime, staff, and fundsyou can make available.
- Level of regulatory requirements within your industry.
Start small and iterate. Dont attempt to govern every aspect of your data. Start with two to four business areas that are most interested in helping with data governance and begin by looking at the data subject areas that theyre most interested in. Form working groups right from the start so that the main work of data governance can be accomplished in small groups and the larger committee meetings can be focused on holistic progress and setting direction for the working groups. Keep in mind that data governance needs to be practical, maintainable, and proportional:
- The level and intensity of data governance should scale with your organizations size, needs, risks, maturity, and capabilities.
- It should align with your data strategy roadmap.
- It should set realistic expectations of what business stakeholders will be able to contribute to the program.
Your data governance program should blend into everyday operationsincreasing adoption and reducing maintenance.
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Importance Of Data Governance
Data governance is a fundamental part of any organization that works with big data and results in consistent, common business processes and responsibilities across the map. It highlights the type of data that needs to be carefully controlled through the organizations data governance strategy. It sets clear rules relating to the roles of individuals with access or who are responsible for data, and the rules must be agreed upon across different departments of the organization.
For example, maintaining the privacy of patient information and health records is especially important in the healthcare industry. For organizations, such as hospitals or individual doctors offices, it is certainly necessary to manage patient data securely as it flows throughout the business.
Better Decision Making And Business Planning
We live in an age where data has become the critical driver of business decisions. A strong data governance allows authorized users to access the same data, erasing the danger of data silos within a company. IT, sales, and marketing teams work together, share data and sights, cross-pollinate knowledge, and save time and resources. Increased data centralization
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Changing The Workplace Culture
Data Governance is really more about changing the way people handle and think about data than the technology used for automated processes. These automated features, however, help to reinforce the Data Governance framework, and the staffs behavior.
Creating a board of Data Governance Board or Steering Committee is a good first step when integrating a Data Governance program and framework. There are many examples of data frameworks.
The Data Governance framework is a set of rules, processes, and policies, typically created by the board of Data Governance. The framework is also used to describe the programsnot the business, but the Data Governance programsgoals, mission statement, and KPIs. The framework should also support the businesss mission statement and goals. An organizations governance framework should be printed out, and circulated to all staff and management, so everyone understands changes taking place.
The Data Governance framework should also include management policies around the databases operations. Typically, these frameworks should establish policies on data protection, database environments, service delivery, performance levels, and licensing.
Saul Judah, a Gartner analyst, has listed seven basic concepts needed to successfully govern data and analytics applications. They are:
The board should also create a job description and approve the hiring of a data steward.