Establishing & Maintaining Standards
The primary role of data governance is to establish and maintain standards around data. This can be achieved in different manners such as the following:
- Identifying what data sources are preferred for each type of data or metric used in an organization. Theres an concept called Master Data Management, or MDM, that identifies the most critical data within an organization and ensures there is a clear understanding of where that data should come from and where it should be stored.
- Ensuring that the reference data is complete and accurate. Reference data provides sets of allowable values for certain data attributes, or provides additional descriptive information about key ideas in the companys data environment. Reference data also helps data consumers by providing data descriptions and metadata.
- Establish common data definitions and rules / calculations. This will provide data quality and accuracy.
- Monitor / manage data access and compliance. A data governance process helps to manage and monitor who should have access to data under what circumstances. It is often applied in support of more general SOX controls and data privacy concerns.
Roles In The Framework
Chief Data Officer
Chief Data Officers are a fast-growing job over the last few years. 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 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 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
The Data Governance Committee sets policies and procedures for data governance. This committee works with the CDO to establish the who, what, when, where, and why of data governance.
How Does Data Governance Support A Data Strategy
A companys data strategy focuses on all people, processes and technology required to effectively make use of data assets to generate business value in line with the business strategy. This could include operational efficiency, minimising expenditure, risk mitigation, or revenue increase.
Data governance sets out the foundation for organizing data and its related assets, and establishes policies and practices for keeping data secure and usable.
A data governance strategy is a subset of the wider data strategy it creates the framework to govern data effectively, and allows the wider data strategy to be met and delivered upon in line with the business strategy.
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Analyzing What: Capabilities And Courses Of Action
Taking a capability-based approach, we link the capabilities to our list of objectives . Doing this will enable us to identify which capabilities will help us meet which objectives.
We can also apply a colour code to the capabilities to identify which are new and which require a degree of change . The capabilities which are blue do not require any change.
Once we have identified the capabilities that require change, we can then describe the changes in the form of Courses of Action.
Challenges Of Data Governance Initiatives
The biggest obstacles for data governance initiatives center on access and availability. Data availability involves more than who is authorized, but also where the data is kept and how it is retrieved, which can lead to new rules for data engagement and areas of misunderstanding. Other challenges come from overcomplicated mandates and policies that are hard to implement and can slow down the work of effective governance.
Conversely, the lack of proper oversight and adjustment can also prevent the benefits of governance from being realized throughout the organization. Many organizations advocate frameworks that value a focused, defined approach but allow for appropriate evaluations and modifications to address potential problems.
In her MIT 2007 Information Quality Industry Symposium presentation, Pierce explains that data is one of the least governed assets because of the following characteristics:
- Data is increasingly easy to collect and digitize.
- Data has increasing importance in products and services.
- Data is very hard to value or price.
- Data has a decreasing half-life.
- Data has increasing security and privacy risk exposure.
- Data is a significant expense in most enterprises.
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Why Businesses Need Data Governance
Businesses use data governance to get the most from customer data.
Being able to quickly review information and make informed decisions based on real-time metrics not only minimizes risk it also helps your company capitalize on timely upselling and cross-selling opportunities.
Another crucial benefit of data governance is security. In a survey by McKinsey, 87% of respondents said they would not do business with a company if they had concerns about its security practices, and 71% said they would stop doing business with a company if it gave away sensitive data without permission. By implementing a data governance framework, you can ensure your customers’ data is safe from potential harm.
Considering all these benefits, it makes sense that the Data Governance Market is growing. According to data from Mordor Intelligence, it was valued at 1.81 billion US dollars in 2020 and is projected to be worth 5.28 billion by 2026.
What Is A Data Governance Scorecard
A data governance scorecard is a collection of agreed-upon baseline and metrics reported on a regular basis, usually monthly, quarterly, and/ or yearly, to the data governance program sponsor and stakeholders.
- It is defined at enterprise or business level, usually depending on your operating model
- It is regularly measured and reported
- Can be split into individual business data steward scorecards or project level scorecards
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Create A Team Dedicated To Your Program
If you want your data governance to be effective, then you’ll need to create roles dedicated to your program. The leader of the program should have strong communication skills and be able to communicate its importance to the rest of your company. Each person on the team should have clear responsibilities and ensure each data governance initiative runs smoothly and quickly.
Your data governance office should consist of employees who know how to best manage your customer data. These people are capable of meeting on their own and determining the most ideal process for organizing information. As a business owner, it may be tempting to add to the decision-making, but it’s important to give your team space when designing the framework. This will allow them to optimize the process and personalize it for your business’s needs.
Pillars Of The Data Governance Framework
There are four pillars to the data governance framework that enable organizations to make their data a fruitful asset.
1. Distinct use cases
In order to get buy-in from stakeholders and drive adoption, it is essential to link the need for data governance to business results. Data governance champions should identify the business initiatives and the challenges that the organization faces in order to determine use cases. These use cases typically fall under three categories :
The number of use cases for data governance is practically limitless. But the data governance framework emphasizes that an organization must start small. They must prioritize addressing just a couple of use cases when first establishing data governance, rather than trying to boil the ocean.
2. Quantifiable value
Notice that all the use cases above are quantifiable. It is essential to have a value-quantified data governance program, meaning that the impact of data governance is measurable. This pillar of the data governance framework allows organizations to examine the success of the data governance practice and provide guidance on how to proceed in the future.
3. Targeted product capabilities
4. Scalable delivery model
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Whether a personal or professional project, our free cost benefit analysis template is a powerful and flexible tool. It can be used over and over again, collecting the project information, quantitative costs and then the quantitative benefits . Adding up the three cost sections and the four benefits sections of the template provide a total cost to compare against total benefits. Its an essential analysis to undertake before committing to any project.
The Straightforward Guide To Data Governance
Data governance plays a major role in organizing and protecting your internal data. It acts as a form of insurance that every piece of information you collect is properly stored and distributed within your organization.
In this post, let’s discuss what data governance is and how you can implement a policy at your company.
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Aspects Of The Framework
We have already discussed the data governance framework as the who, what, when, where, and why of data in your organization. Now lets expand on what that could mean for you.
Who: These are the people the CDO to Data Stewards and Owners, the Data Governance Committee, and the employees that touch and create data during their jobs. Each person in their organization needs to be aware of their responsibilities to data and their role in maintaining the quality and care of data. Data governance is not just a job of the CDO and Data Owners the whole organization has to be on board.
You can implement technology to ease the burden of data governance on your end-users. Automation can help maintain privacy and protect your data from breaches, keep data in the proper storage areas, and enforce data retention policies.
What: The data, obviously. But what data? You dont need to worry about that marketing guys finely curated GIF collection the same way you would govern the companys financial documents. Define what data is of import to your business, both from a compliance and privacy and operational perspective. This is the data that you are going to focus your data governance policies upon.
Why could mean so we are compliant and dont get fined, or so we understand our customers better or optimize our production capability. Whatever your why, keep the message to your team clear and consistent.
Scaling The Data Governance Framework For The Future
Data governance is not a one-time project it is a practice that an organization needs to embed in its business strategy and use on an ongoing basis. One of the principal pillars to the data governance framework is the scalable delivery model, because organizations should apply the data governance framework to additional teams and use cases over time, so they can make the most of their data assets and transform into data-driven enterprises.
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Managing & Communicating Data Development
The third key aspect of data governance framework is to help manage the overall process of data development and to communicate changes to the data environment. The following are two key activities:
- Prioritize data projects: Data is required by many teams. And, the challenge is that the teams require data in different forms based on some modifications relating to the business demands. However, this is very cumbersome to meet everyones demand and there needs to be some way of prioritizing the work that needs to get done. This is where data governance framework lays down the process for assessing, and prioritizing which data projects are undertaken, usually by rationalizing those projects against the overall business priorities of the enterprise.
- Communication around data projects & environments: In addition, data governance framework also ensures the communication regarding the evolution of data environments and letting the users of the data know when new data is added. Having a well structured data governance approach can facilitate communication about data and make sure everyone is informed and aware of the changes.
Data Governance Framework Examples The Traditional Approaches
There are two traditional approaches to establishing a data governance framework: top-down and bottom-up. These two methods stem from opposing philosophies. One prioritizes control of data to optimize data quality. The other prioritizes ready access to data to optimize data access by end users across business units.
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It Project Plan Template
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Project Tracker & Task List Template
Tracking the project is crucial to ensure that it remains on schedule. That means also keeping tabs on the work at a task-level.
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Take Advantage Of Quick Wins
Its easy to jump off the deep end and start analyzing each tag sometimes there are hundreds if your page has been unchecked for a long time. Instead, start first by remediating tags that are considered to be low hanging fruit, such as removing expired and unknown tags, and updating protocols and tag implementations.
Why Data Governance Is Important For Businesses
Imagine that you are working on a multi-national project worth approximately $125 million. Now imagine that just as your project is going to touch down on Mars the project explodes. Obviously not a great outcome, but thats exactly what happened to the first Mars rover in 1999. The root cause of this catastrophic failure? One team used the imperial system instead of the metric system. The lack of a data governance plan lead to the failure of that project. NASA implemented a data governance standard, and now
That is a simple example of how data governance can help your organization be successful. Here are a few other advantages:
- Centralized policies and systems reduce IT costs related to data governance
- Data standards allow for better cross-functional decision making and communication
- Compliance audits are easier to manage, and compliance standards are easier to maintain
A data governance plan can also be a competitive advantage as you grow your business. Modern business runs on data, so without proper planning and business intelligence, you will fall behind your competitors.
- Data fuels business intelligence for short and long term planning, including mergers and acquisitions
- Data governance keeps data growth under control and organized
- Stable data makes adapting to new data and privacy legislation easier
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Set Clear Goals For Data Governance
Once you’ve implemented the new governance system, setting goals for your program will ensure its long-term success. These goals can include protecting top-level data, reducing friction between teams, decreasing the costs of data management, and creating a faster data entry process. Whatever your goal is, it should be actionable and include a roadmap to success.
Start With A Small Sample Size
It’s best not to kick off your data governance program with a complex or long-term project. You might make errors or lose motivation from the team. Rather, begin with a smaller, more manageable project, like analyzing data for one team. Assess the state of the data, specifically its collection, storage, and usage, then decide how much of your budget will be invested in the initiative.
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The Difference Between Data Governance And Data Management
The Dictionary of Data Management defines data governance as the exercise of authority, control, and shared decision making over the management of data assets. Data governance initiatives provide the foundation to develop appropriate data management protocols and procedures.
Data management, on the other hand, is the process that puts governance policies into action. Governance provides a framework thereafter, you can define areas for management and infrastructure or architecture management. The governance establishes the why and who for data accessibility and control, while management sets the where and how.
In a similar vein, it should be noted that data governance and data quality are not synonymous, but are closely related. Data quality is the measurement of data accuracy, completeness, availability, and effectiveness. Data governance policies apply guidelines to this vetted data.
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