Why Is Data Governance So Important
Data governance has risen to the forefront only in the last few years. But it didnt get there all by itself. It was pushed there by events. More specifically, a number of high-profile data breaches over the past several years have pushed data governance to the top of the agenda of many information technology professionals.From these beaches, and from the overall concern about data privacy, have arisen laws like GDPR and CCPA. These are placing tighter constraints around how organizations use the personal data they collect, how they manage that data, and even what data they can collect. As a result of the increase in data breaches and the advent of data privacy laws, many organizations are adopting data governance frameworks to formalize how their data is managed.
Build A Business Case
To get the board or management to approve a project, it is necessary to build a business case that demonstrates why the project is needed and what the benefits of the project will be when it is finalized.
Download the Business Case Builder here. This Business Case Builder provided by OvalEdge lists in detail the steps to build your own business case and also critical questions you will ask the stakeholders. Here is a walkthrough video of the builder.
Data Governance Vendors And Tools
Data governance tools are available from various vendors. That includes major IT vendors, such as IBM, Informatica, Information Builders, Oracle, SAP and SAS Institute, as well as data management specialists like Adaptive, ASG Technologies, Ataccama, Collibra, Erwin, Infogix and Talend. In most cases, the governance tools are offered as part of larger suites that also incorporate metadata management features and data lineage functionality.
Data catalog software is included in many of the data governance and metadata management platforms, too. It’s also available as a stand-alone product from vendors such as Alation, Alteryx, Boomi, Cambridge Semantics and Data.world. Learn more about the features that data catalog software offers, including its governance-related capabilities.
Continue Reading About What is data governance and why does it matter?
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Single Source Of Reference For All Your Data Needs
The productivity of data engineers and data scientists increases dramatically. The work of weeks for retrieving data can be done in minutes. Data is stored in disparate sources whether in different types of databases or siloed in specific departments. Under data governance, the organized data is searchable from a single source through a tool such as a data catalog.
Building A Data Governance Framework
There is no universally accepted process for creating a data governance framework. Different organizations have different frameworks depending on how they manage data governance. However, most frameworks include:
- Roles and responsibilities for data governance. Who is to govern data? What about access controls? Who is accountable?
- The data architecture required to govern data.
- Identifying critical data elements.
- Creating standardized data definitions for governance.
- How a data governance framework supports MDM.
Why Data Governance Matters
Without effective data governance, data inconsistencies in different systems across an organization might not get resolved. For example, customer names may be listed differently in sales, logistics and customer service systems. That could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence , enterprise reporting and analytics applications. In addition, data errors might not be identified and fixed, further affecting BI and analytics accuracy.
Poor data governance can also hamper regulatory compliance initiatives, which could cause problems for companies that need to comply with new data privacy and protection laws, such as the European Union’s GDPR and the California Consumer Privacy Act . An enterprise data governance program typically results in the development of common data definitions and standard data formats that are applied in all business systems, boosting data consistency for both business and compliance uses.
Focus On People And Process:
Technology alone cannot lead to a successful data governance program. People drive its success. Data governance activities are likely already happening within your organization in a less formal or undocumented fashion. Identify the people in the business who are doing this kind of work and designate them as data stewards. Without the right knowledge at the table, data governance efforts will go nowhere. Relieving the pressure on those individuals can only be accomplished by involving them heavily in this effort and then spreading out their knowledge from there.
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Creating A Data Governance Framework
Creating a data governance framework is crucial to becoming a data-driven enterprise because data governance brings meaning to an organizations data. It adds trust and understanding to data, accelerating digital transformation across the enterprise.
However, many organizations struggle to build a data governance program because the practice can seem amorphous. A data governance framework eliminates the complexity by providing a guide for identifying organizational priorities and needs, developing a plan to address these priorities and needs with data, and executing that plan. Consequently, a data governance framework makes it easier for organizations to use their data as an asset and scale data governance across the enterprise.
Create A Data Governance Framework
At the heart of good data governance is a data governance framework. This defines the who, what, and why behind the data.
You define who is responsible for data. A standard framework assigns data owners, data stewards, and a data governance committee.
Data owners are those who are ultimately responsible for data. Data stewards are employees that handle or have access to data. The committee sets the rules and policies of data governance.
What data do the policies and rules apply to? The committee decides what data needs to be elevated to this level. This usually includes data that is sensitive, such as financial information.
Data that has operational importance or requires compliance also falls under the data governance policies.
The committee also needs to get clear about why the data is so important to the organization. This helps you communicate to employees later on.
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The Role Of Data Governance In Data Privacy
Data governance is an overarching term that encompasses all of the data management practices implemented by a business. The official definition of data governance published by Gartner is “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics.”
Another helpful definition comes from Utopia and it is that data governance is the “management of data to validate for accuracy as per the requirements, standards, or rules that an individual organization needs for their individual business.”
In regards to data privacy, data governance is essential. Data governance is essentially a process through which businesses ensure and maintain data privacy. These practices are also designed to make sure that a business has data that adds value to itself, that data is usable, accurate, and secure.
Data governance is also the basic outline that a business can use to make sure that they are in line with all of the regulations that exist and this outline can be adapted as new legislation is created. Having a specific plan for data governance is how a business can maintain and protect data privacy.
What Are The Benefits of Data Governance?
The way that a specific business addresses this process depends on the data governance framework that it creates and adheres to.
Execute These Data Governance Best Practices
Data governance is a necessity because your organization relies on data to make decisions and improve productivity. Data governance sets the policies and procedures to organize data and ensure accuracy.
These data governance best practices introduce you to a data governance framework. Youll set your goals and get employee buy-in so everyone in your organization becomes a good data steward.
Do you want more technology tips? Head over to the Technology section.
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Calculating The Roi Of A Data Governance Program
The ROI of a data governance program is value-driven, always use-case specific, and not intrinsically tied up with tangible profitsat least not in every circumstance. So, to calculate the ROI, you have to look at the governance program as a whole.
We will explore three areas that benefited from data governance to calculate ROI: efficiency, compliance, and data-driven decisions.
Developing A Functional Data Governance Framework
Data Governance practices need to mature. Data Governance Trends in 2019 reports that dissatisfaction with the quality of business data continues in 2019, despite a growing understanding of Data Governances value.
Harvard Business Review reports 92 percent of executives say their Big Data and AI investments are accelerating, and 88 percent talk about a greater urgency to invest in Big Data and AI. In order for AI and machine learning to be successful, Data Governance must also be a success. Data Governance remains elusive to the 87 percent of businesses which, according to Gartner, have lower levels of Business Intelligence.
Recent news has also suggested a need to improve Data Governance processes. Data breaches continue to affect customers and the impacts are quite broad, as an organizations customers must continually take stock and change their user names and passwords. Effective Data Governance is a fundamental component of data security processes. Data Governance has to drive improvements in business outcomes. Implementing Data Governance poorly, with little connection or impact on business operations will just waste resources, says Anthony Algmin, Principal at Algmin Data Leadership.
Before Testing, DefineData Governance Requirements
Image Credit: Global Data Strategy
Combining the Data Governance definition with use case priorities, leads to a set of deliverables. For example, in the use cases above:
Data Governance TestingCoverage
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Five Standardise And Define Where Your Framework Should Be Applied
The final step is to standardize this framework in the way that it applies to your business. It must be repeatable and easy to implement for every employee who uses data within an organization.
Your framework should be easy to enable but also functional enough to support every employees data needs.
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.
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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
How Does It Benefit Businesses
In the above section, we saw the pain points which trigger a data governance initiative. But governance also creates immense value rather than just risk reduction. The holistic value comes from data-driven decision-making. Be it finance, sales, marketing, production, procurement, all teams get hugely benefited from data insights.
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Two Choose The Areas To Focus On
This should include the long and short terms goals of the data governance strategy. You should also define the key metrics that you will use to measure the effectiveness of your data governance program.
As well as looking at the measurement of this, you should think about the funding and resources that are required to implement data governance.
Our long term goal is to ensure that data collected through our media inventory is compliant with data protection regulations. As well as this, our shorter-term goals are to build a filter for inaccurate data and ensure that we make consumer opt-out functionality.
We will measure this by looking at the amount of data that is collectible under data privacy regulations and by looking at the quality of data that is deemed suitable for use .
Some Of The Direct Business Benefits Include:
- Improved data quality As a business implements data governance, data quality will improve. This leads to more accurate business processes and higher customer trust.
- Better analysis Because data governance enhances data quality, analysts have an easier time finding and analyzing the data they need.
- Easily defined goals Data governance sets out a clear method to achieve business goals.
- Consistent compliance Data governance helps data users stay compliant with regulations, reducing the risk of fees and reputational damage.
- Improved data management Improved data management minimizes duplicative efforts, which boosts operational efficiency.
- Standardized systems and data policies Standardizing systems and policies across the organization instills users with ethics and awareness.
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Why Do I Need A Data Governance Framework
A data governance framework enables the business to define and document standards and norms, accountability, and ownership. In addition to setting out roles and responsibilities, this involves establishing key quality indicators , key data elements , key performance indicators , data risk and privacy metrics, policies and processes, a shared business vocabulary and semantics, and data quality rules.
A data governance framework includes discovery of data to create a unified view across the enterprise. This includes not only the data itself, but data relationships and lineage, technical and enterprise metadata, data profiling, data certification, data classification, data engineering, and collaboration.
A data governance framework supports the execution of data governance by defining the essential process components of a data governance program, including implementing process changes to improve and manage data quality, managing data issues, identifying data owners, building a data catalog, creating reference data and master data, protecting data privacy, enforcing and monitoring data policies, driving data literacy, and provisioning and delivering data.
Datameer And Data Governance
Datameer provides a deep suite of security and governance features and is intentionally designed NOT to replicate already-in-place governance mechanisms but instead work with these controls. These capabilities are well matured from Datameers ten years of experience working with large enterprises in highly regulated industries requiring deep security and governance.
Cloud and hybrid environments can create security and governance gaps due to capability mismatches between on-premises enterprise and cloud platforms. Datameers deep security and governance features ensure you get the same robust governance in the cloud and you would expect on-premises.
Datameer also integrates with enterprise and cloud security, offers asset-level controls and encryption on the wire and at-rest. It fully integrates with Snowflake security to protect all the data.
From a governance standpoint, Datameer provides many key capabilities:
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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.
Governance Structure For Portfolio Companies
A strong governance framework is built to guide an organization in how it can achieve accountability, authority and sound decision-making. By contrast, a weak governance framework will cause a breakdown in the stages of the investment process and affect overall economic growth.
For portfolio companies, a governance structure should ensure that all shareholders are provided with the opportunity to vote on matters of governance. All shareholders should have a voice and foundational rights.
Portfolio companies should conduct business with respect for both the shareholders interests and the capital that has been entrusted to them.
A portfolio companies governance structure should take the following into account and demonstrate:
- The companys ability to create value and yield long-term profits in response to demand
- The ability to facilitate investors predicted earnings as a result of accurate and timely disclosure
- A commitment to ethical conduct as a member of society
- A commitment to fulfilling corporate social responsibilities including, but not limited to, the organizations environmental impact.
A portfolio companys governance structure should seek to ensure adherence to the above commitments, as the organization carries out its responsibilities as a market participant.
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