Key Questions To Ask Before Developing A Model
Before even starting to develop a data governance framework, ask yourself some fundamental questions:
Why do we need a data governance framework?
Before even starting out, you need to ask: Why does my organization need a data governance framework? Whats motivating you and your organization to have one? Is it because its the shiny new tool someone heard about and now wants? Or are you responding to an adverse event that happened with your data and you want to ensure it doesnt happen again? Regardless, first articulate precisely why you need a framework around data governance.
What does my current data governance look like?
You need to understand what your organizations current data governance framework looks like. Do you even have one? And if you do, what sort of controls and policies are in place? Whats more, you need to understand if your current data governance is up-to-date and reflects current best-practicesor if it was written several years ago and hasnt been updated.
What are we trying to achieve with a data governance framework?
This goes beyond the general question of whether or not you need a data governance framework what do you specifically hope to realize by having one? In short, what is the end goal of having a data governance framework? What KPIs can I attach to its implementation? Its a fundamental question that will help determine what sort, if any, of data governance framework will best help you reach sound business objectives.
The Straightforward Guide To Data Governance
Data governance plays a major role in organizing and protecting your internal data. It acts as a form of insurance that every piece of information you collect is properly stored and distributed within your organization.
In this post, let’s discuss what data governance is and how you can implement a policy at your company.
Information Governance Initiatives Every Business Leader Should Follow
As one of the integral disciplines of digital transformation, information governance strategies have the potential to drive innovation and empower exponential growth. However, like every business transformation program, there are certain rules and standards to abide by and pitfalls to avoid. Here are our top recommendations to help you supercharge information governance in your organization:
1. Identify an executive-level sponsor
Every business transformation program requires support from leadership. If your information governance initiative doesnt have someone driving it, it will be far less likely to succeed. Larger companies should establish an IG board or committee that maintains strong links throughout the company and engages leaders across the full range of business roles. Recruiting an engaged sponsor who understands the language of business, and not just the language of compliance and IT, will greatly increase the chances of any changes being accepted.
2. Take an org-wide approach
3. Educate employees on an ongoing basis
4. Understand your information lifecycle
5. Choose the right tools and solutions
6. Incorporate data-driven decision-making
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Who Is Included In Data Governance
As you might imagine, building a strong data governance team is a key part of this process and of ensuring that its an effective process. While all teams look a little different, some key positions to include on a data governance team are:
- A data manager, who is responsible for leading the design and implementation of policies and systems and for staffing the data governance team. In some organizations, this is the chief data officer, yet many opt to have a dedicated role for the manager of data governance.
- A data governance architect, who is responsible for oversight of designs and implementation.
- A data strategist, who creates and implements strategic plans.
- A compliance specialist, who ensures compliance with all regulatory standards.
In addition to the core governance team, stakeholders for data governance include the board of directors, finance executives, operations, marketing, sales, the CIO, and IT management. Involving all stakeholders in the process is an important part of the process and can lead to better outcomes.
What Is Data Governance And Why Should My School District Care
School districts are required to collect, report, and store countless data on students and other aspects of the district. With great amounts of data, however, comes increased risk. Districts are responsible to manage this information responsibly and securely.
Data governance can employ sound policies, procedures, and technology plans to provide the school board, administrators, and staff with support and guidance on how to protect sensitive data, like student personal information.
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Create A Distribution Process
The last step in creating a data governance framework is determining how you’ll distribute each type of data. As we mentioned before, some data is sensitive and shouldn’t be shared throughout your organization. So, you need a reliable process in place to categorize it and highlight who it can be shared with. You should also define the channels that can be used for distribution as this will foster smoother and more consistent communication.
What Is A Data Governance Policy And Why Is It Important
Data governance policies are guidelines that you can use to ensure your data and assets are used properly and managed consistently. These guidelines typically include policies related to privacy, security, access, and quality. Guidelines also cover the roles and responsibilities of those implementing policies and compliance measures.
The purpose of these policies are to ensure that organizations are able to maintain and secure high-quality data. Governance policies form the base of your larger governance strategy and enable you to clearly define how governance is carried out.
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Benefits Of Data Governance
Most companies already have some form of governance for individual applications, business units, or functions, even if the processes and responsibilities are informal. As a practice, it is about establishing systematic, formal control over these processes and responsibilities. Doing so can help companies remain responsive, especially as they grow to a size in which it is no longer efficient for individuals to perform cross-functional tasks. Several of the overall benefits of data management can only be realized after the enterprise has established systematic data governance. Some of these benefits include:
- Better, more comprehensive decision support stemming from consistent, uniform data across the organization
- Clear rules for changing processes and data that help the business and IT become more agile and scalable
- Reduced costs in other areas of data management through the provision of central control mechanisms
- Increased efficiency through the ability to reuse processes and data
- Improved confidence in data quality and documentation of data processes
- Improved compliance with data regulations
Four Pillars Of The Data Governance Framework
For organizations to make their data a successful asset, there are four pillars to consider:
- Distinct use cases: it is essential to connect data governance to business results by considering revenue, cost and risk.
- Quantifiable value: the impact of the data governance implementation needs to be measurable.
- Product capabilities: data governance capabilities should provide for individual needs within data processing.
- Deliverable model: data governance should be a scalable service which means that the more cases addressed the more value the organization brings.
What Falls Under Information Governance
There are multiple elements to any successful corporate information governance program, and here are some of the mainstays:
The practice of managing and reducing the risks caused by unnecessary access to data. Employees may have access to data not required for their role or work, for example, or may try to access data via unsecured channels. Implementing it is often a matter of regulatory compliance, especially in sectors such as healthcare, financial services, or the legal industry.
Enterprise Content Management
ECM is the strategy and practice of capturing, managing, storing and delivering data and content by leveraging technology tools. ECM allows an enterprise to manage its unstructured information, wherever that data resides across the organization.
Audit trails are chronological records that provide documentary evidence of the sequence of activities involved in a specific program, operations, workflow, or event. Information governance software captures and preserves these, along with associated assets such as documents, messages, meeting records, et al, is critical in satisfying both internal reviews and regulatory oversight a lack of a detailed audit trail can prove disastrous, especially in heavily-regulated industries.
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Building A Data Governance Framework
A good data governance framework includes policies, procedures, processes, rules along with the right organizational structure and the technology to make it all happen. Believing a technology solution alone will magically result in data governance can be a fools errand. Any initiative is composed of people and processes, along with the supporting technology and data governance is no different.
The framework must also involve the executives in the organization. Data governance has to be understood and appreciated in the boardroom or it will never survive at the troop level. Resources, in the form of both money and people, need to be allocated. Empowerment must happen. Leadership needs to lead and achieve alignment that ensures adoption.
The first step of building the framework is to articulate the objectives in a Mission Statement and define the KPIs that will be used to determine progress. Expressing this early ensures that your effort is aligned with the vision.
Then the framework must include all aspects of how the organization will govern its data. What are the rules? The policies? The processes and procedures? The business glossary? The map of all data assets? This step will consume significant energy, data experience, and knowledge of the organization.
Next, determine accountability. Who will be responsible for each part of the program, and who sits with overall responsibility and decision making?
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What Is The Difference Between Information Governance And Data Governance
Information governance and data governance are complementary areas, but are not exactly the same thing. By understanding more about each one, businesses can enhance their approach to overall information management.
As the name suggests, information governance relates to an organizations information as a whole including documents, records, cybersecurity, and data. In other words, data governance is one avenue of information governance.
The differences between the two lie mainly in where they are carried out and by who. Data governance is an approach to managing at a data level and is focused on maintaining the integrity of any data assets within an enterprise. At its essence, information governance is much more multidisciplinary and relies more on top-down leadership to ensure effective management and collaboration across silos.
By ensuring effective data governance, however, an organization can enhance its overall information governance. Both approaches are ultimately vital for risk mitigation and maximizing the value of your information. The two also naturally intersect within information governance software that includes data management and analysis tools.
Who’s Responsible For Data Governance
In most organizations, various people are involved in the data governance process. That includes business executives, data management professionals and IT staffers, as well as end users who are familiar with relevant data domains in an organization’s systems. These are the key participants and their primary governance responsibilities.
Chief data officerThe chief data officer , if there is one, often is the senior executive who oversees a data governance program and has high-level responsibility for its success or failure. The CDO’s role includes securing approval, funding and staffing for the program, playing a lead role in setting it up, monitoring its progress and acting as an advocate for it internally. If an organization doesn’t have a CDO, another C-suite executive usually will serve as an executive sponsor and handle the same functions.
Data governance manager and teamIn some cases, the CDO or an equivalent executive — a director of enterprise data management, for example — may also be the hands-on data governance program manager. In others, organizations appoint a data governance manager or lead specifically to run the program. Either way, the program manager typically heads a data governance team that works on the program full time. Sometimes more formally known as the data governance office, it coordinates the process, leads meetings and training sessions, tracks metrics, manages internal communications and carries out other management tasks.
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Framework For Responsibility And Accountability
One of the biggest historical problems with data governance is the absence of follow-through although some organizations may have well-defined governance policies, they may not have established the underlying organizational structure to make it actionable. This requires two things: the definition of the management structure to oversee the execution of the governance framework and a compensation model that rewards that execution.
A data governance framework must support the needs of all the participants across the enterprise, from the top down and from the bottom up. With executive sponsorship secured, a reasonable framework can benefit from enterprise-wide participation within a data governance oversight board, while all interested parties can participate in the role of data stewards. A technical coordination council can be convened to establish best practices and to coordinate technical approaches to ensure economies of scale. The specific roles include the following:
These roles are dovetailed with an organizational structure that oversees conformance to the business and information policies, as shown in Figure 4.2. Enterprise data management is integrated within the data coordination council, which reports directly to an enterprise data governance oversight board.
Figure 4.2. A framework for data governance management.
Mark Allen, Dalton Cervo, in, 2015
Keep In Mind That Data Governance Is Not A One
Creating a data governance program may appear like handling a new project. Sections of an organization may feel tempted to assemble a team to take on the project while the rest of the organization waits for it to be done. This is where many businesses witness their data governance strategies slow down.
A data governance strategy isnt a one-time project. There is no set end date for it. Instead, its an ongoing practice thats introduced as a regular policy. When implementing a data governance program, make sure that you present it as a long-term investment, not a one-off project. Data governance may eventually become a part of everyday life at your organization.
Where Is So Much Data Coming From
According to arecent report, the number of IoT devices reached 26.66 billion in 2019 compared to 4.1 billion people connected to the internet. McKinseysays every second, about 127 new IoT devices get connected to the internet, and at this rate, the total number of IoT devices is estimated to reach 30.6 billion.
It is highly improbable to store all this data in one place. The emerging edge computing technology allows for the storage of data at the point of collection. However, this requires protocols to ensure that data comes from trusted sources and is used for the consented purposes.
Hence, with growing digitalization during and after COVID, organizations could suffer heavily if proper data governance programs are not in place. Poor data governance can have a significant impact on:
a) Customer trust relationship with an organization storing customer datab) Regulatory or legislative compliance c) Impaired reporting and decision-makingd) Elevated Data Management costs.
Considering these factors, organizations need to define a data governance assessment and remediation approach to focus on what matters most and target their spending.
Components Of A Data Governance Framework
A data governance framework consists of the policies, rules, processes, organizational structures and technologies that are put in place as part of a governance program. It also spells out things such as a mission statement for the program, its goals and how its success will be measured, as well as decision-making responsibilities and accountability for the various functions that will be part of the program. An organization’s governance framework should be documented and shared internally to show how the program will work, so that’s clear to everyone involved upfront.
On the technology side, data governance software can be used to automate aspects of managing a governance program. While data governance tools aren’t a mandatory framework component, they support program and workflow management, collaboration, development of governance policies, process documentation, the creation of data catalogs and other functions. They can also be used in conjunction with data quality, metadata management and master data management tools.
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Starting A Data Governance Program
Working with relevant stakeholders to develop data governance standards and rules is the first step toward establishing a successful data governance practice. The next stage is to create plans for putting such procedures in place and enforcing them. These processes are just as vital as the underlying policies to ensure that projects are carried out properly and that guidelines are followed consistently.
Take these pointers into account.
- From the start, including all stakeholders and data asset owners in the process.
- Train and educate all necessary teams, people, and stakeholders on data governance on a routine basis.
- Assist in creating and executing the data governance systems and procedures by maintaining open lines of communication. This assistance can take the form of emails, newsletters, official reports, updates, or conferences, but regular contact with all stakeholders is critical.
- Make sure to work with goals that are clear, precise, and attainable.
- Begin small and gradually build up once ready. It is tempting to take on all the goals at once, but it is better to start with a few minor goals and gradually build up from there.
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.
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