Data governance is an established set of rules and best practices that ensure the highest levels of quality and security through the entire lifecycle of data in an organization. In other words, data governance is all about how organizations handle the data they collect.
Collecting data is useless without having a plan for how you will use that data and how you will keep that data safe and complete. Data needs to be treated consistently throughout all departments and areas of an organization. That’s where data governance comes into play. No matter what industry you’re in, you need a system of rules, processes, procedures, and accountability for every stakeholder that interacts with data.
Data governance defines:
Who can take certain actions involving data
What data individuals can work with
When and where data is collected and processed
How data is handled
In a world of countless analytics terms, it can also be helpful to understand what data governance is not.
Data governance isn’t data stewardship
Data governance hones in on an organization’s overall strategy, roles, and policies. With data governance principles in place, data stewardship focuses on the daily activities that make the data accurate and easy to process. In data stewardship, execution and operations are the name of the game.
Data governance isn’t master data management (MDM) either
Master data management (MDM) is all about focusing on the key entities within an organization and raising the quality of their data. It reconciles fragmented views of these key entities into a consolidated view. MDM is a larger process than data governance, but it can’t be successful with data governance.
Data governance isn’t data management
Data management is an umbrella term for managing data over its lifecycle. Data governance is an important part of data management.
Why is data governance important?
Data fuels successful organizations. It is essential for greater business intelligence and digital transformation. But data can only lead to success when it is governed effectively. Organizations need to find a proper balance between offering stakeholders access to data and still controlling data to keep it secure and compliant. This balance is unique for each organization. That’s why a detailed data governance plan is so important.
Benefits of data governance
Data governance brings many different benefits to individual organizations. With disciplined data governance, you can maximize the value of your data, better manage risk, and even reduce costs.
Speak the same data language
Data governance gives an organization a consistent view and terminology for all the aspects of its data strategy. Everyone in the business unit is speaking the same language, and nothing gets lost in translation. All data-related activities become transparent.
Know where to find data
Data governance creates a data map — the ability to understand where data is located, especially for key entities in the organization. Think of data governance as a GPS that makes data assets more usable and easy to find so teams can improve outcomes.
Manage data more effectively and efficiently
Data governance establishes the rules and best practices that make data management possible. It also makes data management more affordable by eliminating extra work and redundancies from mismanaged data.
Stay in compliance
Many industries and organizations must follow standards for security and compliance. Government regulations like the European Union General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), or the United States Health Insurance Portability and Accountability Act (HIPAA) are extremely specific on how data must be handled and offer hefty consequences for violations. Specific industries also must match requirements like the Payment Card Industry Data Security Standards (PCI DSS). Data governance is the solution that helps organizations remain compliant.
Get better data
When organizations create and follow a data governance plan, their data becomes more accurate, more complete, and more consistent. It simply becomes better data.
Support every level of business
Data governance supports every level of an organization. Management has better oversight of corporate data and can use its value to adapt business operations. Finance departments ensure accurate and secure reporting. Sales and marketing teams can trust accurate customer insights. Supply chains can reduce costs and make processes more efficient, and production teams can deploy successful automation. Legal departments enforce compliance and meet regulatory requirements.
How does data governance work?
Data governance is no small undertaking. An organization must establish a framework and create roles for oversight, insight, and accountability.
The data governance framework
The data governance framework is the policies, processes, structures, and technologies that make data governance possible. It can include aspects like an organization’s mission statement in regards to data, goals, key performance indicators (KPIs), and methods of accountability. It can also take into account what data software will be used.
Every data governance framework should cover 10 main areas of data:
Data modeling and design
Data storage and operations
Data integration and interoperability
Documents and content
Reference and master data
Data warehousing and business intelligence
In each of these areas, ask yourself the basic questions for understanding — who, what, when, where, and why:
Who are the people interacting with data? Define their roles and responsibilities.
What data is most important for your organization?
When do you need to deploy your data governance strategy? Take into account regulatory compliance and how it will affect current processes.
Where do you currently process and store data?
Why is data governance important for your organization? Why should your employees care? Why will data governance make an impact on your bottom line?
A finished data governance framework should be shared across an organization so everyone knows how to work with data in their individual role. And, don’t forget that data governance is an ongoing process. Frameworks will evolve and adapt as needs are identified.
Data governance roles
Data owners ensure that information in their domain is governed correctly. They might approve glossaries and data definitions, direct data quality activities, and work with other data owners to solve problems.
Data stewards are responsible for the day-to-day management of an organization’s data. They work together across departments to make data decisions. They are the go-to expert on data governance in their area of the organization.
Data governance or steering committee
This committee brings together senior management from the C-suite to set the overall strategy for data governance, work with data stewards to resolve concerns, and hold the entire organization accountable.
How do different industries use data governance?
Every organization will have its own unique data governance framework. That framework needs to fit with the organization’s key objectives and business model.
Across industries, data governance frameworks can be used in many ways including:
Making data-driven business decisions
Meeting regulatory data requirements and documenting data practices
Improving data security
Defining clear roles and responsibilities
Eliminating redundant data processing work
Securing stakeholder commitment
What should I look for in a data governance tool?
A data governance tool should make it easy for anyone in an organization to understand and control data. It should improve the quality of your data by offering validation and data cleansing. It should also be able to scale with your organization.
Since major trends like artificial intelligence (AI) and machine learning (ML) rely on having high-quality, reliable data, it’s safe to say that data governance won’t be going anywhere anytime soon. It will, however, continue to adapt for ease of use, cost effectiveness, and scalability. Expect to see more data governance working in the cloud and governing the data migration from physical storage to the cloud.
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