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Data Governance Framework Implementation Plan

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Creating A Data Governance Framework

Go From Records Management To Information Governance How To Implement An Effective Strategy

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.

Principle : Enable A Modern Workplace: Anywhere Anytime With Anyone

The GC strives to be an innovative organization that:

  • provides its employees with modern technology that supports information retrieval, use, sharing and collaboration by making information and data accessible when and where needed
  • provides customizable tools and resources with minimized learning requirements for users
  • provides automated, digital processes in support of better services
  • attracts, retains and encourages public servants to work smarter and be innovative, greener and healthier so that they better serve Canadians

Early thinking on new digital principles is in progress in support of the shift to digital .

Message From The Government Of Canada Chief Information Officer

Digital capabilities continue to transform the way individuals and businesses work. In parallel, a data revolution is taking place that requires transformative action to support different forms of collaboration. The GC must adapt to meet demands and expectations from clients, stakeholders, partners and employees. This update to the Government of Canada Information Technology Strategic Plan is an interim step to a larger digital policy and strategy that is currently under development for the 2018 to 2019 fiscal year.

The GC delivers a broad range of programs and services to individuals and businesses in Canada and abroad. IM and IT support the government in providing these programs and services.

Over the last few years, the GC has taken the first steps to shift toward an enterprise approach to managing information, data, technology and security. This direction supports priorities identified in recent budgets, ministers mandate letters, reports and audits while responding to key drivers.

This plan creates a framework and sets direction for the GC to become an open and service-oriented organization that provides programs and services to citizens and businesses in simple, modern and effective ways that are optimized for digital and available anytime, anywhere and from any device.

Consistent with the GCs first Strategic Plan, the following 4 strategic goals frame the direction for the GC:

  • service
  • security
  • agility
  • Community focuses on:

    Alex Benay

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    How To Build A Data Governance Framework

    There are numerous theoretical frameworks for data governance. The trouble is, most of them are too in depth for regular data users to understand, let alone implement. For example, they could include multiple frameworks for different areas of governance such as data security, metadata management, or data integration.

    The framework will provide you with a practical way to implement data governance measures in your organization quickly and comprehensively.

    The first thing you need to do before building a data governance framework is to establish what the state of data governance is in your organization. This step will inform how you proceed and the measures you need to take to build a framework.

    When you know what data you have in your organization and who is responsible for it, you must set up a data governance committee to implement your data governance program.

    Make Rules About Privacy

    Implementing Effective Data Governance

    A hallmark of data governance is privacy-related safety, both for your customers and your company. Its critical to establish how your company will handle Personal Identifiable Information and Personally Identifiable Health Information . Privacy concerns carry a level of risk in most organizations, but that risk will vary depending on the type of business. Further, these risks are always changing, so you might want to involve a team like Search Discovery to conduct a consent management and data privacy audit to truly identify your current state relative to your industry. Reach out to your chief privacy officer or chief security officer to get more information about your business privacy strategy, but heres a list of privacy elements to be aware of, since any of these elements could identify a person. You should also be aware of combinations that could identify someone .

    Read more about recent privacy changes and how your organization could be impacted.

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    What Does A Data Governance Framework Mean And Involve

    Ideally, a company’s data governance is derived from the overarching data strategy and is embedded in the corporate strategy. A framework can help to consider all aspects of the governance of data.

    A data governance framework defines more than just a data governance strategy. There are varying opinions on what other elements the framework should include. We recommend six building blocks for a data governance framework:

    Why Data Governance Matters

    Data is an essential asset for any company. Data governance acts to make this asset usable, accessible and ensure protection from any malicious threats. In addition, analyzing data helps you to measure success by highlighting areas of improvement, identifying the root causes of failures/discrepancies, and offering insights on process enhancement.

    Furthermore, data governance is a critical component in compliance management. An effective data governance framework provides a scalable and repeatable method of governing data to meet necessary compliance regulations according to the vertical your company may operate in.

    A strong data governance framework is also crucial because it helps you assign ownership and accountability for data. Any new initiatives or projects involving data must start with a needs analysis to identify requirements and priorities. This approach will also ensure that your business has an accurate understanding of the current state of its IT landscape, which can help detect any gaps in your overall IT strategy.

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    Determining How Many People Are Needed To Run Data Governance

    To obtain management approval and budget, it is necessary to estimate how many employees are needed to execute effective data governance. The best way to determine that figure is to estimate the overall workload. In a typical data governance setup, there are some full-time positions, typically in a central data management team. In addition, there will also be some employees in the business units and support functions that cooperate with the central team. However, these are typically not full-time data management positions.

    Best Practices For Data Governance

    How to Create a Data Governance Plan (the 30/60/90 days plan)

    When looking for data governance best practices, you can learn a lot from others who have worked through the various processes and templates. While each organization is different and you will need to adapt your data governance practices to your process, there is no need to completely reinvent the wheel. When applying an agile development mindset to data governance, start small with a minimum viable deployment, and then iterate and grow from there. This can yield greater long-term benefits and bring the rest of the organization on the journey with you. First, it is important to understand what data governance is and what it can bring to your organization.

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    What Is Cloud Data Governance

    In multi-cloud or hybrid cloud computing systems, when data gets stored in various locations, and cloud data governance protocols such as permissions, guidelines, and metadata are inconsistent across databases, data governance takes on a new level of sophistication. Moreover, modern tools from a single software platform that enable data engineers and compliance teams to automate data governance, data access rules, and privacy protection can help data teams negotiate the complexities of data governance.

    The activity of managing data availability, authenticity, consumption, and security in cloud computing systems to fulfill critical business objectives is known as data governance. These objectives most likely include the following listed below.

    Implementing A Data Governance Framework Is An Iterative Process

    Data governance is not a project that can be done in a weeks timeand not just because theres too much work to do! Sure, the work needs to be broken up into executable chunks, but data governance is also a practice that needs to be cultivated and kept alive and vital. Its a perpetual cycle.Whether youre starting a data governance program from scratch or wanting to improve your existing program, start here. And review these items yearly to keep your program fresh:

  • Conduct data security and risk assessments: What is important for my business via our risk profile?
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    Data Governance Framework Best Practices Definitions And Examples

    Inside Out Security Blog » Compliance & Regulation » Data Governance Framework Best Practices, Definitions and Examples

    Its 2020, do you know where your data is? If you answered yes to that rhetorical question, you have a decent grasp of data governance. If not, its time to start to figure that out. Either way, read on to learn more about data governance and how Varonis can help automate you out of a big hole.

    If you need more convincing about why you need data governance, check out the Varonis 2019 Data Risk Report. Its an eye-opener.

    Roles In The Framework

    Data Governance Program

    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

    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

    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.

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    An Example Of Data Governance

    If the CEO decides to embark on a growth course and acquire further companies, this means a massive growth of the data basis in a possibly short period of time. The data strategy must address this strategic realignment, for example by defining goals for handling new data. Standards for integrability in turn have implications for data modeling. These changes must also be communicated to internal stakeholders so that the operational managers can develop plans for integrating the new data and adapt their workflows.

    Why Do Businesses Need Data Governance

    Even though most organizations do have volumes of data stored digitally or physically, most of the data is in a non-standardized format. Further, organizations cannot always be sure of the reliability of data due to age, the source, etc. Employees or business leaders often hesitate to rely on this data for decision-making due to worries about data quality. Data governance is a process that makes an organizations data reliable. It also makes sure that high-quality information is available across the organization. It empowers every department to make decisions based on this data. Data governance also drives the digital transformation of a business.

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    Selecting The Right People

    The best data governance concept is likely to fail if the wrong employees for the data management roles are selected. Business understanding is key and often more important than technical/IT knowledge. At the end of the day, data managers often bring people from diverse backgrounds together, so strong communication skills are a must.

    How To Implement A Data Governance Initiative

    Tackling Implementation Challenges of a Data Governance Program

    A goal of a data governance initiative is to identify the principles for the team and to establish targets and direction. This template will aid you in capturing the meaning of data governance for your organization before you begin your initiative. It will help you gain sponsorship and educate the organization about your mission, vision, and goals.

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    Strategic Data Management Board

    This board is the central decision-making board in data governance that aligns and approves the data management strategy, annual objectives and budget. It also tracks the progress of the data management activities and resolves issues that had to be escalated. Oftentimes, the board consists of data definition owners from the business units and managers of the support functions as well as the Head of Data Management / Chief Data Officer. It is possible that these roles already meet in other boards or committees. If so, it should be considered whether an additional strategic data management board is really needed or whether the topics to be discussed can be integrated into the other boards or committees.

    Principles Underlying Data Governance

    Two important design principles are fundamental for any data governance design:

    • Enterprise-wide data governance requires collaboration between business, data and IT – Consequently, a federated approach is needed for enterprise-wide data governance. This implies that the roles and responsibilities can be assigned to employees who work in different parts of the enterprise.
    • Data management and analytics roles facilitate the information supply chain – While the data management roles emphasize the provision of data for different business purposes, the analytics roles endeavor to deliver analytics products throughout the enterprise and integrate data across the business units. Both roles clearly depend on each other and facilitate information supply chains.

    *Data definition includes business and quality rules, data access policies, data lifecycle and the conceptual data model. From the data point of view, it thus provides the input for the authorization concept, the risk management and compliance.

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    Typical Challenges Of Data Governance

    Within the pharmaceutical industry Data Governance hasnt yet received the required attention. This is mainly due to historic and organic growth of functions and departments.

    • Lack of alignment between silos which resulted in duplicate data storage, different definitions and use, duplication of systems and disjointed procedures leading to increased inefficiencies and risk of incompliance.
    • Lack of buy-in from all stakeholders either before initiating or during execution of a data governance project.
    • Due to different needs of different stakeholders and no common understanding of Data Governance concept, a lack of cooperation and harmonization can occur.

    Analyzing What: Capabilities And Courses Of Action

    Data Governance

    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.

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    Strategic Goal : Agility

    An agile, connected and high-performing workforce with modern tools:

    • attracts and retains highly skilled and diverse IM-IT talent
    • provides a technologically advanced workplace that supports mobility
    • promotes digital literacy and collaboration
    • pilots new practices, processes and solutions that exploit information as a strategic asset
    • rethinks how data and information professionals can help meet current and future business needs

    Build A Business Case

    Getting buy-in and sponsorship from leaders who will be part of the process is key when building a data governance practice, but buy-in alone wont fully support the effort and ensure success. Build a strong business case by identifying the benefits and opportunities that data quality will bring to the organization and show the improvements that can be gained, like an increase in revenue, better customer experience, and efficiency. Help everyone involved see and understand both the energy required and the eventual benefits to be successful. Most leaders can be convinced that poor data quality and poor data management is a problem, but data governance plans can fall short if leadership isnt committed to driving change.

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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.

    Get Full Control Of Your Data Landscape With Automated End

    Good Governance Framework (GGF) Training – PMRU

    Until youve mapped the full flow of your data, you cant properly evaluate the state of your solution. An ideal starting point is to visualize the end-to-end data lineage of your Power BI, Tableau or Qlik tool. With NodeGraphs Dependency Explorer, you can automatically and instantaneously map your entire data solution from data source to end-user application, on a field-level.

    NodeGraph Dependency Explorer showing data lineage from Snowflake database to Tableau workbook

    This can serve as your go-to data lineage solution or as a powerful extension to the lineage tools allowing you to visualize how your data assets are connected to the overwhelmingly overlooked BI environment. Of course, you can always attempt to produce a manual or semi-manual solution in-house. However, we believe that if youre relying on people to perform manual processes to achieve this data overview, you will never have 100% coverage.

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