Data Governance Policies And Procedures

Learn More About Data Governance

DPIA: Meaning, Approaches, and Best Practices for Your Data Protection Impact Analysis

In today’s world of privacy regulation, its important to understand the mechanics of compliance rules. For privacy regulation in particular, these rules include the creation of key artifacts that show regulators and customers that the company understands and can address key components of the regulation. Learn what DPIAs involve, the key components to creating your own, and how NetApp Cloud Data Sense can help you approach this important first step.

Read more: DPIA: Meaning, Approaches, and Best Practices for Your Data Protection Impact Analysis

NIST Data Privacy Framework: A Quick and Easy Introduction to the NIST Framework At the beginning of 2020, the National Institute of Standards and Technology published a set of guidelines that will help enterprises adapt to today’s landscape of increasingly demanding data privacy requirements. Read about the background to the new framework and learn all the key concepts.

Read more:NIST Data Privacy Framework: A Quick and Easy Introduction to the NIST Framework

Governance, Risk and Compliance: Getting it Right

Governance, risk and compliance aims to address an organization’s strategy for integrating these three components in an effective way. Learn about the Governance, Risk and Compliance model, how it can benefit your organization, and how to accelerate GRC with automation using GRC software.

Data Governance Policy: 4 Foundational Policies

The Components Of A Data Governance Policy

While each policy is tailored to an organizations specific needs, these are some of the typical components included:

Policy purpose: The statement of purpose describes the reason the policy exists and how it supports the organizations mission or business objectives.

Policy scope: The scope explains who is affected by the data governance policy.

Policy rules: This is the main section that outlines the rules guiding data usage, and access.

Stakeholder roles and responsibilities: Stakeholders range from the data governance body and data owners, to data stewards and data users.

Definitions: A glossary includes common terminology referenced in the policy. Some examples might include:

• Data
• Custodian
• Metadata

Review process: Included in a policy by some organizations, this section describes how the data governance policy is established, reviewed, and updated.

Resources: Any related documents, policies, or regulations are referenced in this section.

Some policies also contain details such as:

• An explanation of the risks related to data.
• Applicable regulations.
• Guiding principles.

Data Governance Manager And Team

Data governance managers may be covered by the chief data officer role or may be separate staff. This role is responsible for managing your data governance team and having a more direct role in the distribution and management of tasks. This person helps coordinate governance processes, leads training sessions and meetings, evaluates performance metrics, and manages internal communications.

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Data Governance Best Practices

How should you implement data governance given those challenges? Here are some best practices that will help you with your data governance journey.

The Driving Forces Of Data Governance

Above is a useful example to help illustrate why data governance has entered center stage over the past couple of years. In my opinion, there are two important operational drivers forcing organizations to either create or enhance their data governance policy: risk and maintenance. Lets go into these with the marketing example in mind.

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Data Governance Framework Best Practices Definitions And Examples

Inside Out Security Blog » Compliance & Regulation » Data Governance Framework Best Practices, Definitions and Examples

Its 2020, do you know where your data is? If you answered yes to that rhetorical question, you have a decent grasp of data governance. If not, its time to start to figure that out. Either way, read on to learn more about data governance and how Varonis can help automate you out of a big hole.

If you need more convincing about why you need data governance, check out the Varonis 2019 Data Risk Report. Its an eye-opener.

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Importance Of A Data Governance Policy

The importance of a data governance policy is tied directly to the importance of a strong data governance program and the value of data itself.

Starting in the 20th century and accelerating in the 21st century, data became one of the most valuable assets held by most, if not all, organizations.

Data during this time started to fuel both tactical and strategic decisions.

It also powered automation, machine learning and artificial intelligence initiatives, with data being fed to these technologies to instruct them how to properly perform processes and operations.

Data also has enabled the creation of new products and services. For example, manufacturers found that they could use their data assets to analyze the performance of their products and predict when they’ll need scheduled maintenance based on customer-use patterns, thereby enabling them to sell predictive and prescriptive analytic services as well as preventative maintenance services based on data analysis.

In fact, the “2019 State of Data Management report” found that data governance was among the top five strategic initiatives for global organizations in 2019.

However, data is only a valuable asset if it’s relevant to the organization’s needs and objectives and if it’s accurate and available consistently over time and throughout the organization.

These committees should determine who has responsibility for the data, its security, its integrity and its use.

Assembling A Data Governance Team

Data governance policies apply to everyone within the enterprise: staff, leadership, and even board members. To establish a governance structure, a team or committee is formed to develop the goals, mission, and vision for data oversight.

The team can be composed of information analysts, IT personnel, subject matter experts, and project managers to provide expertise however, line-of-business professionals can offer the cross-functional balance needed for effective, proactive governance.

The team may be called the Data Governance Board, the Data Stewardship Council, or the Data Governance Committee, and role titles may vary from data manager to data scientist to data architect to data analyst. They may also be part of a larger data governance office .

The team is primarily responsible for stewardship of the data . The role can fall to one person or, in the case of large organization, a group. The position is responsible for accuracy and for appropriate access. The steward also serves as the arbiter for completeness and updating of data according to stated governance mandates.

The team also provides guidance and structure on strategic data planning, data literacy, and data use, and it provides resolution for master data management issues. MDM is often confused with data governance, but they are not one and the same: MDM is the method that enables organizations to link all data to a single master file to streamline data accessibility and sharing.

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Planned Updates For 2020

Once the Data Governance Committees update the procedures and guidelines listed below, the information will be made available on this website. Content is out of date and does not meet accessibility requirements in pdf format, thus they are not being republished until both issues are addressed. Contact the Data Governance Committees at for more information on the following.

Data Governance Policies And Procedures As A Process

Data governance is not a new concept by any stretch of the imagination, but it has come into sharp focus as the worlds data footprint continues to grow exponentially. Today, organizations not only must adhere to strict data policies and regulations , but theyre also looking to build a data governance strategy to better manage and properly safeguard their data as a valuable organizational asset.

Efficient access and understanding of your organizations data and its footprint is crucial. Lets take a quick look at what a data governance process strategy can look like through this lens.

Say your company is looking to market a new product by targeting a specific user group from your established customers. There are many aspects to a successful roll-out and launch, but Ill focus solely on the marketing campaign to target specific users interested in the product. Questions youll ask are: where can I get information about my customers, the previous conversations weve had, and any other relevant information to put a story together to sell them something new? Answers live in a number of places, but they probably include these sources: your CRM, customer support portal, and analytics dashboards.

If you have a data governance policy in place, youre likely to know exactly where all of your data resides and have rules for granting access to such data. What results is a streamlined process to efficiently utilize a protected asset.

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Apply Data Governance Best Practices

Once the planning process is over, its time to implement your new data governance processes. Its a big step, and you should follow certain tips and best practices to ensure things go smoothly.

Here are a handful of considerations to keep in mind:

• Establish formatting standards for your data.
• Use technology to enforce formatting standards and data integrations from multiple sources.
• Classify your data and tag everything with metadata.
• Track KPIs to determine how your data is being used.
• Automate data requests, approvals, permission requests, and everything else possible in your workflow to ensure data governance initiatives dont slow down your operation.
• Dont forget about unstructured and unmanaged data in your archives.

Data governance is dynamicits not a set-it-and-forget-it initiative. Be prepared to make changes and continuous improvements after the initial implementation.

As previously mentioned, you probably cant achieve all of your goals at once. So start with one or two and scale from there.

What Businesses Need Data Governance

Most businesses benefit from strong data governance, but industries with heavy regulatory burdens such as banking, finance, and health care have a greater need for formalized governance initiatives and are particularly focused on activities that put them at regulatory risk. Adherence to the regulatory challenges directly impact how they manage, report, and protect their sensitive information. Noncompliance can lead to fines, brand damage, or even jail time.

However, it should be noted that any organization that collects sensitive data, such as financial information, Social Security numbers, or medical records, is also subject to regulatory compliance mandates. Strong data governance first validates and promotes quality data, and then puts in place policies, controls, and management to meet internal and external expectations.

And although banking, finance, and healthcare are some of the most highly regulated industries, their governance structures can provide advantages that go beyond information security. In health care, for example, knowledge acquisition can open up opportunities for better patient outcomes.

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Data Governance Policies And Procedures: What You Need To Know

Show me the money!, the famous quote from the hit movie Jerry Maguire, describes how a negotiation works between two people. But it also works as a great data governance analogy.

Instead of a negotiation between people, think of data governance policies and procedures as a negotiation between your organization andyour data. Whats more, its a negotiation that can be completely automated. So what does this all mean in todays data-driven world and, more importantly, why should you care?

In this blog, I want to discuss data governance as a process, beyond the realm of data policies and government regulation, as well as explore the critical factors that drive organizations to design and implement strong, maintainable data governance policies. Ill also suggest incremental steps to build a policy thats right for you. Its not one size fits all. What is the same, however, is the fact that every organization should have one.

Goals And Benefits Of Data Governance

The primary goal of governance is to assure the integrity of data assets through accountability, consistent data distribution policies, processes, and procedures, standardized systems, and education. The benefits include the following:

• Improve data quality .
• Deliver trustworthy information.
• Create confidence with high-quality, consistent data.
• Make intelligent business decisions.
• Drive optimization and effectiveness across departments.
• Enable better strategic planning, risk management, and compliance.
• Establish better collaborative opportunities across organizations and departments.
• Eliminate redundant work.
• Improve data security by identifying vulnerabilities and remediating.
• Increase data value.
• Support data-driven customer service initiatives.
• Improve business operations quickly during times of growth or merger.
• Comply with industry regulations.
• Improve data transparency across the organization.
• Improve productivity and reduce error with high quality data.
• Enable continuous data improvement initiatives.

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S For Developing A Strong Data Governance Policy

So your organization has identified that you either need to improve your policy or create a new one. Lets get into how you can get the best bang for your buck and develop a customized policy thats reliable and maintainable.

My first recommendation is to start with your regulatory and operational data risks. Once the risks have been mapped out, you can then start thinking about how to make your data work for you. By this, I mean, think about the data you collect from your customers and explore how you can deliver a better overall experience. Here are some incremental steps to get your process on the right track:

What Is A Data Governance Policy

A data governance strategy is a set of guidelines, which ensure that data assets are consistently managed and correctly used. These guidelines typically include individual policies regarding data quality, access, security, privacy, as well as roles and responsibilities for implementing these policies and monitoring compliance.

A data governance strategy should clarify the principles, practices, and standards that the organization deems necessary to have high quality data and protect data assets. This process is the responsibility of a group called the Data Governance Committee, which consists primarily of executives and data owners.

Policy documents developed by this team should clearly define the data governance structure that executives, managers and production line workers must follow in their day-to-day routines.

Here are some important basic data policies that any organization should establish.

Learn more in our detailed guide to data governance policies

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What Is A Data Governance Policy And Why Do You Need One

A data governance policy is a set of rules that help safeguard data, and establishes standards for its access, use, and integrity. The policies are typically accompanied by standards, which provide more detailed rules for implementation of the policy.

A data governance policy is a critical component of a data governance framework. It guides your companys decisions about data assets. The framework creates a structure for carrying out data-related activities, and the data governance policy provides guidelines for activities that involve data.

The core purpose of a data governance policy is to recognize that data is a critical asset and must be treated as such. At a high level, the policy promotes a security-focused culture where all stakeholders play an active role in protecting data assets. Although the policy is critical for organizations in highly regulated industries, any businesses that handles sensitive data or relies on data strategically will benefit from a data governance policy.

Institutional Data Governance Council Data Trustees

The standing University Committee which prepares, compiles, creates, and recommends policies and procedures to the President for his or her approval on institutional data standards, guidelines, protocols on the collection, management, revision, and access to such data. The Council is appointed and charged by the President. The Data Trustees are comprised of the President and the Institutional Data Governance Council.

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