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What Is A Data Governance Model

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More Confidence To Make Data

What is a Data Governance Maturity Model? #datagovernance #maturitymodel

Establishing clear data governance protocols allows an organization to have more confidence in its datas accuracy. This confidence increases the likelihood that the organization will back its with data instead of subjective reasoning. By some estimates, data-driven organizations are three times more likely to report improvements in their decision-making than other, less data-driven ones. This can prove pivotal in increasing profits and overall business success.

What Is Data Governance And Who Does It

Now more than ever, data governance is crucial to effective management and decision-making within enterprise organizations.

Data governance is a process and set of principles whereby a company manages data availability, security, integrity, and usability within systems, software, and databases.

According to Experian, data governance is “a process to ensure data meets precise standards and business rules as it is entered into a system.”

Experian goes on to explain that: “Data governance enables businesses to exert control over the management of data assets. This process encompasses the people, process, and technology that is required to ensure that data is fit for its intended purpose.”

Companies generate and need to process, store, and share more data than ever before. Both from customer interactions within internal systems and relevant third-party data flowing into the business. In 2018, the amount of data captured, created, and replicated had reached 33 ZB , according to an IDC report. Since then, the world’s data volume has already exceeded 44 ZB zettabytes, and this figure keeps increasing exponentially, with it projected to reach 175 ZB in 2025.

In this article, we take a closer look at principles of data governance, data governance examples, and help companies answer the question many still struggle with: What is data governance, and why should we bother with it?

Who’s Responsible For Data Governance

In most organizations, various people are involved in the data governance process. That includes business executives, data management professionals and IT staffers, as well as end users who are familiar with relevant data domains in an organization’s systems. These are the key participants and their primary governance responsibilities.

Chief data officerThe chief data officer , if there is one, often is the senior executive who oversees a data governance program and has high-level responsibility for its success or failure. The CDO’s role includes securing approval, funding and staffing for the program, playing a lead role in setting it up, monitoring its progress and acting as an advocate for it internally. If an organization doesn’t have a CDO, another C-suite executive usually will serve as an executive sponsor and handle the same functions.

Data governance manager and teamIn some cases, the CDO or an equivalent executive — a director of enterprise data management, for example — may also be the hands-on data governance program manager. In others, organizations appoint a data governance manager or lead specifically to run the program. Either way, the program manager typically heads a data governance team that works on the program full time. Sometimes more formally known as the data governance office, it coordinates the process, leads meetings and training sessions, tracks metrics, manages internal communications and carries out other management tasks.

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Focus On The Operating Model

An operating model is an asset model that outlines how an organization defines roles, responsibilities, business terms, data domains, etc. This, in turn, affects how workflows and processes function. It impacts how an organization operates around its data.

The operating model is the basis for any data governance program. The idea here is to establish an enterprise governance structure. Depending on the organization, the structure could be centralized , decentralized, or federated .

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Data Governance Framework Examples The Traditional Approaches

How Important is Data Governance to Internet Marketing?[ebook]

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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What Exactly Is Data Governance Maturity

Data governance maturity refers to the stage an organization has reached in the implementation and adoption of data governance initiatives. An immature organization will have a great deal of unorganized data and will not be using this data to drive growth. Alternatively, a mature organization will be well-aware of the importance of data as a key business asset and governing and managing it accordingly.

S To Consider When Implementing A Data Governance Framework

Each step is critical to the success of the process. Ensuring all pieces are correctly placed starts with identifying the information you want to use to build your framework.

Define your analytics strategy

Start with your organization’s strategic initiatives and KPIs, metrics, or outcomes, because you cannot manage goals if you dont have the measurement framework in place. Knowing where you want to go and how you measure your success gives you the guide rails needed to meet your goals.

Identify members of the cross-functional team

Document who is responsible for understanding current/future state capabilities, challenges, and goals set a vision with a project scope, prioritization, and success measures hold regular meetings gather/respond to feedback and document value. Team roles might include:

  • Executive sponsor, who sets the vision for modern analytics, aligns projects to transformational initiatives, nominates staff for advocacy roles, and ensures accountability.
  • IT sponsor, who is responsible for data governance installation, configuration, and maintenance partners with business leaders and SMEs enables secure governed data access and transitions content authoring to the business.
  • Line-of-business sponsors, who advocate for data-driven decision-making within their respective teams, promote content authoring and governed access, support content, encourage collaboration and sharing, and document business value.
  • Document your current enterprise architecture

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    Data Governance: A Primer For Managers

    • 16 Feb 2021

    Data is one of the most powerful tools modern businesses have at their disposal. Whether created by an organization, its customers, or a third party, data can lead to meaningful insights that positively inform and shift business decisions when leveraged correctly. The opposite also holds true: When data is leveraged incorrectly, it can expose an organization to significant liabilities.

    Thats why all organizations need to have clear data governance policies and protocols in place. While all employees should understand those policies, its especially important for managers to know the rules and monitor their teams activity for compliance.

    Heres an overview of what data governance is and why its crucial for modern organizations.

    Access your free e-book today.

    Build A Business Case

    Uncovering Data Governance Maturity Models (webinar) #datagovernance

    Full dedication and the investment of significant time and resources are essential for reaping the enterprise-wide benefits of a data governance program. It can be difficult to convince stakeholders to embrace the challenge unless you have a compelling business case. Start by identifying essential data elements and the critical business processes they support. Then detail the costs associated with managing, integrating, and validating those elements through current manual processes. Highlight the potential business impact of manual process failures to prove the value of adopting a data governance strategy.

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    What Is The Current State Of Data Governance

    For a multi-domain MDM program to succeed, data governance and data stewardship practices need to be closely orchestrated. This effort needs to start with an effective data governance process aligned with the MDM strategy and implementation plan. Chapter 6 provided strategies and examples for how to establish a consistent, transparent governance model across MDM domains and indicated that if a sufficient data governance process and structure does not already exist for the MDM program to use, the MDM program itself will need to become the driving force behind the implementation of the necessary data governance processes. Similarly, if an appropriate data steward model does not already exist, the MDM program and data governance will need to become the driving forces behind the implementation of the data steward model. Without well-aligned data governance and data steward models, the MDM program cannot succeed.

    Prior to a company having a multi-domain MDM strategy and plan, there are likely to be existing instances of data governance and data steward practices that have resulted from locally or functionally oriented data management initiatives. For example, in one functional area, a data administrator or a support engineer may be acting in a data steward role to control a specific set of master data, such as validating sources to target data loads according to certain acceptance criteria and monitoring error log activity associated with any data mapping or integration issues.

    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.

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    Where To Start With A Flexible Operating Model

    All organizations need data governance to become data-driven and, therefore, need an operating model. The benefits of a flexible operating model are outstanding, but the long list of components seems complex and daunting. So where does an organization start with an operating model? The answer is, wherever it is right for that organizations priorities at the time. Factors such as the organizations maturity, structure, and use cases will impact where to start. A flexible operating model allows an organization to get started according to its priorities rather than follow the rigid requirements of its technology solution.

    Consider this: a healthcare company and a retailer both need data governance, but they may have different priorities for data governance use cases.

    A healthcare organization may need to ramp up its data governance in order to meet standards for Health Insurance Portability and Accountability Act . Since its use case is about privacy and regulatory compliance, its first priorities in the operating model may be around defining asset types to classify data based on sensitivity and then building workflows to ensure processes are followed and completed.

    On the other hand, the retailers first use case may be supply chain analytics, so it can deliver products to its customers as swiftly as possible. Its priority in the operating model may be to set up workflows to speed up processes and eliminate operational inefficiencies.

    The Data Intelligence company

    What Is The Importance Of Data Governance

    What is Data Governance

    Data is the fuel that drives decision-making and change within an organization.

    Data governance should always be viewed as a business strategy, rather than an IT and technology function. IT teams can play a role in data governance, especially when it comes to managing, storing, and stewardship. However, the reason for implementing data governance should be a business and operational decision.

    According to a McKinsey 2019 Global Data Transformation Survey, 30 percent of time spent within an enterprise organization was wasted on non-value-adding tasks due to poor quality and limited access to data.

    Companies have eliminated millions in data-related costs after implementing data governance models. Billions of dollars of value is being generated by companies investing time and energy in data governance, compared to those that don’t take this seriously, according to McKinsey research.

    McKinsey says that: “Data governance is one of the top three differences between firms that capture this value and firms that don’t. In addition, firms that have underinvested in governance have exposed their organizations to real regulatory risk, which can be costly.”

    Some of the largest creators and consumers of data are healthcare organizations, financial services companies , and technology firms. When a company commits to enacting data governance principles –mdash which is a gradual process that takes time and investment –mdash millions or billions of dollars in value can be unlocked.

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    Better Decision Making And Business Planning

    We live in an age where data has become the critical driver of business decisions. A strong data governance allows authorized users to access the same data, erasing the danger of data silos within a company. IT, sales, and marketing teams work together, share data and sights, cross-pollinate knowledge, and save time and resources. Increased data centralization

    What Are The Benefits Of A Flexible Operating Model

    The primary benefit of a flexible operating model is that it allows you to build a data governance framework specifically tailored to your organizations needs. It allows you to:

    • Extend and coordinate A federated flexible operating model allows teams to enhance the model to meet their unique needs.
    • Create a shared language Build a common understanding around data to facilitate collaboration.
    • Build for all users Balance the needs of technical and business users with a collaborative operating model.
    • Sustain high performance Scale and adapt models as the business environment changes and asset and relation types evolve.

    An operating model serves as the backbone to data governance, and consequently, digital transformation and Data Intelligence. Leverage a flexible operating model so you can get your data governance program up and running fast and adapt to any use case.

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    The Data Governance Framework

    A data governance framework is a set of data rules, organizational role delegations and processes aimed at bringing everyone on the organization on the same page.

    There are many data governance frameworks out there. As an example, we will use the one from The Data Governance Institute. This framework has 10 components lets discuss in detail:

    Figure 1.

    Why Do I Need A Data Governance Framework

    How To Select a Data Governance Maturity Model #datagovernance

    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.

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    Data Governance Is Not Data Management

    Data management refers to the management of the full data lifecycle needs of an organization. Data governance is the core component of data management, tying together nine other disciplines, such as data quality, reference and master data management, data security, database operations, metadata management, and data warehousing.

    Everyone Is Using Data

    With only a few specialized people using data, its easy to control access to data and enforce some kind of data traceability. The issue is, data is not reserved for a small group of specialists its now used by everyone.

    Today, companies increasingly engage in operational analytics an approach consisting in making data accessible to operational teams, for operational use cases . We distinguish it from the more classical approach of using data only for reporting and business intelligence. Instead of using data to influence long-term strategy, operational analytics informs strategy for the day-to-day operations of the business. Trends such as code-less BI make operational analytics possible by empowering operational teams to manipulate data.

    Organizations are thus increasingly aiming at democratizing data, ensuring everyone can access the data they need, whenever they need it. Although this brings about many great things, it also creates two major problems:

  • With everyone using data and building reports/dashboards/new datasets, organizations quickly end up with numbers that dont match between different departments.
  • It became much harder to control the level of access to data, as well as to ensure data was used the right way and by the right people. This makes compliance issues even more of a nightmare than they were before.
  • This data anarchy cause traditional data governance models to fail, prompting the need for new, more adapted models.

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    Which Data Governance Maturity Model Should You Use

    Although there are several data governance maturity models out there, the best known were developed by OvalEdge, IBM and Gartner. As mentioned earlier in this blog, a maturity model is a tool for measuring the level of your data governance capabilities. So, you must ensure that when you adopt a maturity model, you also have in place a data governance framework and roadmap that follows the same methodology.

    When you set out to decide on a data governance maturity model you need to consider many factors. These include key business drivers, the budget required to implement the model, the existing data management and governance framework, and the industry you operate in.

    Diagnose The Data Assets Within The Organization

    The State of Information Governance in Corporations

    For data to be fully profitable for an organization, it is necessary to know how to select, collect, store and use it effectively, especially as data is both abundant and easily lost.

    You can start to do this by taking inventory of all the data present in the company, identifying its various sources and then defining the points of friction where there is a loss of value due to poor data quality. Keep in mind the 5 Vs:

    Piano Tip: With our Data Manager tool, you validate each new property sent to your tag before making it available to your company’s employees.

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