Data Management Vs Data Governance: What Is The Difference
To fully unlock the power of data, enterprises must understand the people, processes, and technologies behind it. Managing critical enterprise data correctly and efficiently is the key to gaining insights, meeting regulatory requirements, and exceeding business objectives. These goals can only be achieved with a strategic and well-planned data management strategy, which includes the need for effective data governance. While both data management and data governance work in unison to build, maintain, and manage enterprise data, they are in fact different.
To differentiate between data management and data governance, it is important first to have a clear definition of each.
Determine A Data Governance Model
The next step is to create a data governance model for your team to work off of. This model should describe the hierarchy for who can view and distribute different types of data. This ensures that sensitive data is placed in the hands of your most trusted employees and isn’t shared without authorization. You can view one example of a data governance model below.
You should also describe your rules and regulations for data collection. Outline your standards for securing data as well as which channels you’ll use to obtain it. This will create consistency in your data collection which will lead to more reliable and accurate takeaways.
Data Governance Framework Components
The policies, regulations, procedures, organizational structures, and technology implemented as part of a governance program make up a data governance framework. Additionally, it outlines the programs mission, goals, and metrics for success, as well as decision-making roles and accountability for the several components that will make up the program.
The Data Governance Institute states that the following are requirements for every organization for a data governance framework:
- A set of guidelines outlining how various parties collaborate to create and implement these guidelines
- Making and enforcing the regulations are individuals and institutional entities.
- Processes that will control data while generating value, controlling cost and complexity, and assuring compliance
The data governance framework of an organization should be established and distributed internally so that everyone engaged is aware of how the program will operate from the outset.
Data governance framework:
On the technical side, managing a governance program can be automated using data governance software. Data governance tools dont have to be a part of the framework to enable program and workflow management, collaboration, the establishment of governance policies, process documentation, and other tasks. Additionally, they can be used in conjunction with tools for master data management , metadata management, and data quality.
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But First Your It Team Needs To Make Sure You Can Provide Reliable Data The Benefits Of Having Accessible Accurate Data Are:
Having a single source of truth. All decision-makers work from the same data sets, terminology, and view, giving more opportunities for internal flexibility.
Improved data quality. Your team can rest assured that all the available data is safe to use, complete, and consistent.
Improved data management. Helping establish a code of conduct and best practices to ensure your team addresses organizational needs and concerns immediately and consistently.
Faster, consistent compliance. Having clean data management throughout your governance process means procedures correctly generate, handle, and protect your data to keep it in compliance.
Reduced costs and a better profit margin. Eliminating decisions based on outdated information results in efficient day-to-day operations, easier audits, and reduced waste.
A stellar organizational reputation. When your business is steadfast and reliable, you position your business as a leader in your marketplace.
While adding a data governance strategy to your organization has many benefits, a few challenges might arise if your team isn’t prepared for its organizational implementation.
Atlan: Effortless Data Governance For The Modern Data Stack
The entire data management space is going through a paradigm shift.
The data world is slowly converging around the best of the tools for processing large amounts of data, a.k.a the Modern data stack
Data governance for the Modern data stack needs a rethinking.
This is where Atlan comes to your support A data catalog and data governance solution built for agility, trust, and collaboration.
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Data Management & Governance
Playbooks, guidance, templates, and other resources to support the implementation of policy, the creation of data governance structures, and the day-to-day work of data management in the federal government
Outlines 8 principles for best practices for government open data, formed by open government advocates in 2007 and cross-referenced with principles from OMB Memo M-13-13.
Data Management Vs Data Governance: The Difference Explained
In the simplest terms, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making. To unpack this idea further, it helps to understand what each of these concepts is to better understand how they operate together in practice.
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Why Is Data Management Needed
Quite simply, data management is needed because organizations need a framework or structure to manage all the data they have. Data management provides that framework, guiding organizations and individuals on how to properly use the data for the organization.
Such a framework is needed because of the important data plays throughout society. Its the lifeblood of many organizations. And theres lots of it. Over 2.5 quintillion bytes of data is created every day, and that data is the lifeblood of many organizations. And if an organization doesnt manage its data well, its just information floating around a computer system.
Data Governance Vs Data Management #: The People
While data governance is considered to be a business function, data management is seen as an undertaking for the IT or technical teams. Heres why.
Most data governance frameworks expect organizations to set up a data management council or steering committee that oversees the governance program. Such councils are made up of the leadership or senior management involved in business roles. Thats because the data governance program objectives must impact the overall business goals of the organization.
So, a business manager could be in charge of overseeing a governance program for a specific data domain.
Meanwhile, data management is all about execution implementing the governance framework and influencing the organizations business goals. This involves everything from defining rules for data storage to setting up access rights and controls.
So, it involves more technical roles, such as a data engineer, architect, or database administrator .
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Data Governance Vs Data Management: Key Differences
Data can be one of the most valuable assets for any organization. With the rise of big data, companies can take advantage of vast stores of enterprise data to gain insights and make better decisions. However, as we create and store more data, consumer privacy concerns are growing. Companies need to comply with increasing numbers of regulations, such as GDPR. Data breaches occur with greater frequency, and they can be extremely damaging to an organizations reputation as well as expensive to clean up. The risks of poor data policies are severe, even fatal for organizations. Its essential to create data governance and data management practices to make sure that data is handled properly. Lets look at data governance vs data management.
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:
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Develop A Business Case
Ensuring buy-in and sponsorship from leaders is key when building a data governance practice. But buy-in alone wont fully support the effort and guarantee success. Instead, building a strong business case by identifying opportunities that data quality will bring may be helpful.
Improvements can include an increase in revenue, better customer experience, or efficiency. Leaders can be convinced that poor data quality and poor data management is a problem. But, data governance plans can fall flat if leadership isnt committed to driving change.
Peernovas Cuneiform Platform: Active Data Governance
Enterprises struggle with building effective data management and data governance strategies due to siloed systems and data quality challenges. Enterprises experience data quality issues due to their existing data governance approach and static metadata tools.
PeerNovas Cuneiform Platform is an active data governance and data quality tool that provides a strong backbone to enterprise data management strategy. The platform automatically builds, updates, monitors, and optimizes data dictionaries, glossaries, catalogs, and rule repositories. Using a dynamic approach to data quality and management, the platform creates end-to-end , integrated, and active lineages across disparate tools and systems. The Rules Engine in the platform executes all business rules in near real-time. Data Quality rules are also run as part of the Rules Engine dynamically. This means that high-quality data and metrics around data quality are always current. When there are data quality issues, the platform provides integration into third-party workflow/exception management tools to ensure that the issues are resolved quickly. Root cause analysis of the data quality issues can be performed faster using active lineages. Through a self-serve model, enterprises can create accurate regulatory and governance reports with strict audit control.
In summary, PeerNovas solution ensures enterprises can more easily implement an effective data governance framework and data management strategy.
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How Combining The Two Roles Helps Companies Become Data
Despite their differences in scope and means, the concepts of data governance and data management should not be opposed. In order for a company to adopt a data-driven strategy, it is imperative to reconcile these two axes within a common action. To achieve this, an organizations director/CEO must be the first sponsor of data governance and the first actor in data management.
It is by communicating internally with all the teams and by continuously developing the data culture among all employees that data governance serves the business challenges while preserving a relationship of trust that unites the company with its customers.
How A Math Problem Cost Nasa $125 Million
Way back in 1999, NASA lost its because of a translation problem. Unfortunately, spacecraft engineers didnt make the switch from the Imperial to the metric system for their measurements.
It is very difficult for me to imagine how such a fundamental, basic discrepancy could have remained in the system for so long. I cant think of another example of this kind of large loss due to English-versus-metric confusion. It is going to be the cautionary tale until the end of time.
John Pike, space policy director at the Federation of American Scientist
What if they had a central data repository complete with a glossary that provided adequate context and standardized processes that governed the recording and storage of all data? The Orbiter might still have disappeared, but discrepancies in data wouldnt be the cause.
Thats why data governance, along with a solid framework, is so important. However, before we move on to the nitty-gritty of a data governance framework, lets quickly recap the concept of data governance.
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Data Management And Data Governance: The Intersection
While the two roles are distinct, it is important to notethat data management and governance sometimes intersect. For instance, abusiness unit might have a request for more information or data from its seniormanagement. In such a case, it is the data management teams responsibility tofacilitate that access. They must ensure that users accessing the data aregiven only those privileges they require and are role-based. This way, theywill not be able to access or modify more than what they need for theirassigned job purposes. Likewise, there will be controls put in place torestrict access even further if needed.
Lastly, data management and data governance can work intandem. In this case, a business unit might want to gather more informationfrom its systems that is not readily available for analysis or reporting. Forthis, it will get involved with the data security team to ensure that onlyauthorized users have the necessary access to gather this data.
How To Create An Enterprise Metadata Management Strategy Within A Data Governance Framework
Metadata management cant work as a standalone strategy, because it has to be developed within the context of a data governance framework. When created and rolled out correctly, a data governance framework will integrate users with core data governance processes, make data-driven innovation part of company culture, and enable collaboration.
Fail to define and execute a data governance framework before you set out to develop a plan for your metadata, and youll struggle to get your strategy off the ground.
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Data Governance Framework: What Is It And Do I Already Have One
Since the first person flipped the power switch on the first computer, IT and business groups have made decisions on what to do with the data used by, and created by, technology. While youre no longer filing punch cards or archiving magnetic tapes , you still need to govern data on a daily basis. That has led to the need for a data governance framework an overarching approach to how you collect, manage and archive data in your enterprise.
A white paper unveils a data governance framework that borrows from hundreds of data governance implementations to create the foundation for a modern program. The data governance framework encompasses everything from the people and process behind data governance to the technologies used to manage data.
The best part? It encompasses many of the things you already have in place. It’s normally a matter of finding a way to bring a number of different efforts together.
Virtually every organization put elements of a data governance framework in place to support different initiatives along the way… The greater need, however, is to tie all of this together.
Start Small But Consider The Larger Picture
Data governance is built on three pillars: people, process, and technology. A business builds the larger picture when it starts with the people, builds the processes, and finally incorporates technology into the processes.
Without the right people, its difficult to build successful processes needed for the technical implementation of data governance. Hence, identifying or hiring the right people for your solution can be the starting point for an organization. The right people can then help build your processes and source the technology to accomplish the job.
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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.
What Is Data Governance And How Is It Related To Data Management
Data governance on the other hand is a core subset of data management it outlines a framework or a set of policies, rules, standards, people involved, and processes that help in the management of data. Since data governance issues guidelines and a strategy for all the other areas of data management it plays a crucial role in the process.
Data governance is what helps turn data into truth which can then be analyzed and used for reporting. Data governance has four integral pillars including the processes, the people, the standards, and the rules. If an organization doesnt implement all four pillars, their data governance processes will not be rock solid.
Without data governance processes in place, the company will be at the risk of poor data, compliance issues, operational issues, and several other risks. Data governance outlines rules and regulations for every scenario for the lifecycle of the data set and this includes the business areas and data owners involved at every stage. Data governance also provides accountability and ownership of the people involved. Additionally, you need definitions to maintain consistency and accuracy of the data across the organization.
So, while data governance outlines a strategy, it needs to be supported by software. There are several tools that you could use to implement data governance such as software for to name a few.
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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.
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Data Governance Is Not Master Data Management
Master data management focuses on identifying an organization’s key entities and then improving the quality of this data. It ensures you have the most complete and accurate information available about key entities like customers, suppliers, medical providers, etc. Because those entities are shared across the organization, master data management is about reconciling fragmented views of those entities into a single viewa discipline that gets beyond data governance.
However, there is no successful MDM without proper governance. For example, a data governance program will define the master data models , detail the retention policies for data, and define roles and responsibilities for data authoring, data curation, and access.
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