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How can you align data governance with business goals?

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Data governance is the set of policies, processes, and standards that ensure the quality, security, and usability of data across an organization. It is not only a technical discipline, but also a strategic one that aligns data management with business goals and values. In this article, you will learn how to achieve this alignment by following four steps: defining data governance objectives, identifying data stakeholders, establishing data roles and responsibilities, and implementing data governance tools and practices.

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1 Define data governance objectives

The first step to align data governance with business goals is to define what you want to achieve with data governance. These objectives should be specific, measurable, achievable, relevant, and time-bound (SMART), and reflect the business needs and priorities of your organization. For example, some common data governance objectives are: improving data quality and accuracy, reducing data duplication and redundancy, enhancing data security and compliance, enabling data-driven decision making, and increasing data value and innovation.

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Zawahir Kashif

Entrepreneur Extraordinaire: Propelling eCommerce Success Stories ???? | Igniting Growth and Innovation in Online Businesses
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Data governance objectives are like friendly guides to better data management: - Improve data quality and accuracy - Reduce data duplication and redundancy - Enhance data security and compliance - Enable data-driven decision making - Increase data value and innovation These SMART objectives lead the way, and you can build a tailored data governance framework to make them happen.

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2 Identify data stakeholders

The next step is to identify who are the key stakeholders involved in data governance, and what are their interests, expectations, and concerns. Data stakeholders are anyone who creates, consumes, owns, or affects data in your organization, such as data engineers, data analysts, data scientists, business users, executives, customers, regulators, and partners. You should map out the data stakeholders for each data domain, project, or initiative, and understand their data needs, challenges, and goals. This will help you to communicate and collaborate effectively with them, and to align data governance with their business outcomes.

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3 Establish data roles and responsibilities

The third step is to establish clear and consistent data roles and responsibilities for each data stakeholder, and to assign them to a data governance framework or structure. Data roles and responsibilities define who has the authority and accountability for data creation, collection, storage, processing, analysis, distribution, and usage, as well as for data quality, security, and compliance. Some examples of data roles are: data owner, data steward, data custodian, data consumer, and data governance committee. A data governance framework or structure is the way you organize and coordinate these roles across your organization, such as through a centralized, decentralized, or hybrid model.

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4 Implement data governance tools and practices

The final step is to implement the data governance tools and practices that support your data governance objectives, stakeholders, and roles. Data governance tools are the software applications and platforms that help you to manage, monitor, and measure your data assets, such as data catalogs, data quality tools, data lineage tools, data security tools, and data governance dashboards. Data governance practices are the methods and processes that help you to execute your data governance policies and standards, such as data quality audits, data classification, data lifecycle management, and data governance reviews.

By following these four steps, you can align data governance with business goals and create a data-driven culture in your organization. Data governance is not a one-time project, but an ongoing journey that requires constant evaluation and improvement. You should regularly review your data governance objectives, stakeholders, roles, tools, and practices, and adapt them to the changing business needs and data environment.

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Sachin Raikar, CDS, CIMP Specialization Data Governance

Certified Data Steward, Certified Information Management Professional | Collibra Certified Data Steward | Data Privacy | BigID Certified | Data Security Specialist @ Data Management COE, Fidelity Investments Ireland
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Establish a comprehensive data governance framework: A data governance framework is a set of data rules, organizational role delegations, and processes aimed at bringing everyone in the organization on the same page. Assess your organization's data governance maturity: Evaluate organization's current data governance maturity level to determine the level of automation you need from a data governance tool. Select the right data governance tool: Select the right data governance tool that meets your organization's needs and budget. Educate data stakeholders: Train and educate data stakeholders on data governance policies, procedures, and best practices.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you align data governance with business goals?

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3

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5

1 Define data governance objectives

2 Identify data stakeholders

3 Establish data roles and responsibilities

4 Implement data governance tools and practices

5 Here’s what else to consider

Data Engineering

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How can you align data governance with business goals?

1

2

3

4

5

1 Define data governance objectives

2 Identify data stakeholders

3 Establish data roles and responsibilities

4 Implement data governance tools and practices

5 Here’s what else to consider

Data Engineering

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