How Do Data Governance And Data Management Work Together
All information is data not all data is information. Information is data that is readily applied to business processes and which generates value. To arrive at information, data must undergo a rigorous governance process and a number of key measures are implemented to make useful data trusted and used as information.
As mentioned earlier, management without governance is like constructing something without a blueprint. Meanwhile, governance without management is just documentation.
Data Governance Is Not Master Data Management
Master data management focuses on identifying an organizations 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.
Data Governance Challenges Are Not The Same For Everyone
Diverse governance’s use-cases based on industry needs and organizations size
There are two main drivers for data governance programs:
- Level of regulation needed in the industry
Data regulation push the minimum bar of data governance processes higher. It requires business to add controls, security, reporting and documentation. Organizations set up a governance program to ensure transparency over sometimes unclear processes.
- Level of complexity of the data assets
Having a strong governance become increasingly important with the exponential growth of data resources, tools and people in a company.
The level of complexity increases with the scope of business operations , the velocity of data creation or the level of automation based on data.
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Data Governance And Information Governance Provide Specific Benefits For Organizations Looking To Improve How They Handle Data
Data governance and information governance can help enterprises frame the technical and business discussions required to meet governance, risk management and compliance needs while increasing the value of operationalizing data.
Both terms include the word governance. Other aspects are implied in both disciplines, including risk management, compliance, efficiency, alignment and simplicity. As businesses become more digital, a thoughtful approach to addressing the lifecycle and workflows of data governance and information governance grows in importance.
The two disciplines approach related problems at different conceptual levels. Data governance focuses on various technical considerations of the data itself. Information governance focuses on the implications relating to the meaning of the data in relation to enterprise goals, business users, regulators, legal teams and customers.
Data Privacy Depends On Data Health
Data privacy goes hand in hand with data health. Data is healthy if it is available to everyone across the organization who needs it when they need it, and they can trust it to provide value in their analyses or decision-making processes.
If your customer data is a mess, or your data is siloed and inaccessible across the organization, you’re probably in noncompliance with data regulations. Unhealthy data can’t be managed with enterprise-wide data governance, so you won’t meet the deadlines for GDPR or CCPA discovery requests
The good news is that data health is achievable through a combination of preventative care, supportive treatments, and a supportive culture. While data health metrics will look different for any organization, you can measure data health with data quality metrics and by assessing the business value of your data.Talend offers a free data health checkup: Talend Trust Assessor. When you export a subset of your data and run it through the tool, you’ll get a rapid evaluation of the validity, completeness, and uniqueness of the data. We also provide sample datasets so you can also see how it works without uploading any data of your own.
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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.
Working Together To Strengthen An Organization
Data governance enhances and makes data management stronger by imposing a set of rules and policies for how an organizations data is governed and protected. Without that framework for data governance woven into their data management, organizations open themselves up to greater risk and liability. But by having data governance and data management work together, the organization is better protected against risk and liability in the event something does happen to their data.
More Data Governance Resources
Infographic
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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 Governance Supports Data Protection In Particular The General Data Protection Regulation
Just a few years ago, the discipline of data protection was mainly about securing who had access to your data and ensuring the data did not fall into the wrong hands. Data governance, on the other hand, was mainly about managing your data and improving your data quality. Despite what many people think, data governance and data protection have never been the same thing, and the line between the two disciplines used to be very clear. But now we have a relationship between data governance and data protection where they work together and complement each other. What happened?
The General Data Protection Regulation happened. The European Union personal data regulation has raised the data protection bar and require businesses to better manage, store and document any personal data they may hold on European citizens. The GDPR requirements are creating the overlap between the two disciplines.
In order to understand how, we need to start with defining what data governance is. Expert in data governance, Nicola Askham, defines it as:
Proactively managing your data to support your business achieving its strategy and vision.
Data governance is achieved by implementing a data governance framework that consists of policies and processes as well as roles and responsibilities. How does that work together with data protection? Well, lets apply it to some of the data protection requirements listed in the GDPR.
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Plan For Data Storage And Organization
Your data strategy should also include policies related to data storage and organization. These aspects of data management are crucial, as they help determine how actionable and shareable your data is.
Data storage is a relatively simple technology capability, but methods for storing data can vary significantly from company to company. When creating your storage plans, you need to consider how much storage capacity you need, but you should also consider how your approach to storage will impact data sharing and usage. The way you organize your data impacts how easy it is to access, understand and use. Your storage solution also influences how easy it is for different departments to share data.
Ultimately, your goal in creating a data storage and organization plan is to make your data as accessible, shareable and actionable as possible for the parties that may need it. Different approaches may work best for different companies, but, generally, you should store your data in an easily accessible system in a consistent format.
Types Of Data Governance Tools
Implementing your data governance program can be made much easier with the help of various technology and software tools. Here are some of the core types of data governance tools that you should consider using.
- Data cataloging: Software that uses automated data discovery to create catalogs for better organization and standardization.
- Data management: Collects data from multiple sources and provides a master view for data governance purposes.
- Visualization: Consider implementing a tool that helps visualize your entire data ecosystem in a single, easy-to-use interface and report for data owners.
- Data lineage: Trace data lineage by parsing code from data sources, applications, tools, and source code automatically.
- Policy management: Some software automates policy enforcement and assignment of business rules to ensure full compliance with your governance program.
- Threat detection: On the security front, youll want software that alerts you to potential threats, before your data gets compromised or stolen.
These are just a few key tools that can aid your Data Governance efforts. By working with an experienced compliance partner like Varonis, you can gain better clarity on the tools and technology stack that best suits your needs.
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What Are The Best Practices Around Data Governance
Establishing a strong data governance framework is a journey and its worth re-evaluating whether you have clear alignment with your overall goals from time to time. Here are some general best practices weve heard from customers and the industry:
- Understand how to measure success and involve the business in defining goals: A good data governance strategy should have clear metrics and KPIs to measure progress over time. Business leaders should also be involved in defining goals, both to ensure organizational alignment and policy enforcement.
- Define clear roles and accountability teams across the data lifecycle: Data isnt static its transformed, cleansed, deleted, etc. by different users for different purposes. Because of this, you should have a way to build audit trails and data lineage throughout the entire lifecycle, with all users who interact with data so that the right people are accountable.
- Dont overcorrect on data restrictions: Restricting data access to a high level can be tempting, however creating bottlenecks to data access can drastically slow down the business, creating a new type of operational risk that of project failure and falling behind the competition. Before creating new policy restrictions, try to gather information from the business on how the data is used before making decisions so that you know at which level to restrict access.
Who Is Responsible For Data Governance
A comprehensive data governance program requires a few specific roles, groups, and functions. Heres who is responsible for what, and the capabilities they should have:
Chief Data Officer
The Chief Data Officer is a rapidly emerging role. Companies are beginning to understand the importance of managing data and implementing a data governance framework, and that means hiring a CDO. The CDO is the company leader of the data governance strategy, and hiring a CDO shows the commitment to data and buy-in from the top to take a data governance program seriously.
Data owners
Data owners are the people that have direct responsibility for data. They are involved in the protection and quality of data as a business asset. A data owner will be on the team that uses the data. For example, a member of the finance team should be a Data owner for the finance teams data.
Varonis automates the process for data owners to manage access to their data. Data owners know who in their organization should have access to their data, and providing them the tools they need to manage and audit access to data is good data governance.
Data stewards
Data stewards are the champions of your data governance strategy. They meet with Data owners and enforce data governance policies and procedures, as well as train new data owners and employees in data governance.
Data governance committees
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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.
How Data Governance Influences Data Privacy And Security
As data governance frameworks support the integrity and overall quality of data, data governance initiatives have a major influence on data privacy and security. Although data governance, data security and data privacy are distinct concepts, they share the common goal of getting the most value from data and making it accessible to the organization.
Even though data governance is primarily a strategic concept, it still defines actionable processes and procedures that should be followed to get the maximum value from data while continuing to protect it. This helps the organization achieve data privacy objectives such as reducing the risk of data loss, theft or misuse.
Detecting, identifying and responding to data security issues require collaboration, communication and organizational alignment. Data governance can help align resources that are used for data security so organizations can respond to threats without delay.
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Minutes In The Cloud: Data Privacy Vs Governance Vs Security
Data privacy, data governance and data security are all terms that are mistakenly used interchangeably. They are indeed related, particularly when it comes to keeping data in the cloud protected, private and secure, but the definitions and mechanics of executing on each are all quite different.
Join us for another 15 Minutes in the Cloud session for an overview of what each of these terms means, how and where they intersect, and why its a balancing act to pay adequate attention to each one or risk threatening the overall security of your data.
Presenting will be security experts Thomas Rivera, CISSP, CIPP/US, CDPSE and Strategic Success Manager at VMware Carbon Black together with Eric Hibbard, CISSP-ISSAP, ISSMP, ISSEP, CIPP/US, CIPT, CISA, CDPSE, CCSK and Director, Product Planning Storage Networking & Security, Samsung Semiconductor.
Drivers Challenges And Goals
Drivers
The greatest driver of the SCDS is the changing requirements for data and information of Canadians, Canadian businesses and Canadian institutions. Canadians want an authoritative source of information about what is important to them â relevant information and insights which respond to the increasingly complex economy and society. The legalization of cannabis, the opioid crisis, and the effect of foreign ownership on property values are some recent examples. Statistics Canada like many other National Statistical Offices around the world is responding to this need by reshaping its business model, building new networks and expertise, and devising new ways of unlocking the value of data for public good.
Challenges
The Data Strategy Roadmap for the Federal Public Service included details on challenges faced by Government of Canada organizations:
- Absence of horizontal governance for strategic direction on data issues
- Lack of data literacy and cultural reticence to break silos
- Lack of adequate digital infrastructure and a complex rules framework
- Challenge of acquiring, governing, and managing large volumes of disparate data
Statistics Canada faces similar challenges. The agency modernization agenda is addressing these challenges through digital transformation in the form of IT modernization, the move to a broader use of administrative and alternative data sources, and fostering collaboration and partnerships with external partners including other NSOs.
Goals
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Better Understand The Information We Hold
Recommendation 17: Establish a centralized view of government-held data, develop a government data quality framework, and develop guidance for the long-term management of digital government assets
Goal: know what data we hold and ensure their quality and maintenance
Know what data the government holds
To increase the use of and access to data and reduce duplication, we must first have a complete view of the data we hold, along with an understanding of their quality, location, and format. This exercise will create an interface or tool to view all government data assets and will support interoperability so that organizations can share, combine and make optimal use of data. The work involves attaining a common vision for governance and stewardship and the development of a data reference model, privacy protection, security protocols and a maintenance plan. This will leverage existing work, including StatCans Inventory of Administrative Data Providers from the Public Sector. Experience gained will inform metadata standards and inform a data reference model for a whole-of-government approach.
Next steps
Ensure the quality and maintenance of data
Next steps
- Analyze current data quality policies/practices by January 2019
- Draft proposed quality approach/framework by May 2019
Maintain digital information
Data visualization as a tool for new insights
Supporting government decisions and accelerating change
Harnessing data visualization for internal decision-making
