Data Governance Step by Step: Month 1

Steps to take during the first month to establish the data governance program...

The 9-month step-by-step guide for establishing data governance:

Month 1:

Identify Business Structure: The business areas and its structure guides the next set of activities. Gather Organization's vision, mission, values. Gather business goals, objectives and current imperatives.
Identify Data Influencers: These are members of the business and business operational teams who are the data owners. Conduct a survey or assessment to identify types of data assets managed, key challenges, and opportunities.
Assess your organization's readiness: This involves a broader reach to help quantify and qualify the information shared by the data owners. Conduct a survey or assessment to understand your organization's current data management practices, culture, and maturity level. Identify any gaps, challenges, or areas for improvement.
Identify current Data Policies: Gather data policies, standards, compliance requirements, regulatory requirements.
Assess current data architecture

Week 1

Identify Business Structure

Look for data related call outs in the business goals and objectives
Establish how to tie data governance to the business goals and objectives
If your organization is OKR based (refer to my blog post on OKR, Objective Key Results), assess how to establish OKRs for the data domains

Examples of tying the data governance initiative to the business goals and objectives Business goal: Improve customer satisfaction and reduce churn.

Data governance initiative: Improve the quality and consistency of customer data.
Expected outcome: By improving the quality and consistency of customer data, the organization can better understand its customers and their needs. This can lead to more targeted and effective marketing campaigns, improved customer service, and a more personalized customer experience.

Business goal: Increase revenue by 10%.

Data governance initiative: Optimize data usage and insights.
Expected outcome: By optimizing data usage and insights, the organization can identify new opportunities for growth, develop more effective products and services, and improve its marketing and sales strategies. Use data governance to improve customer segmentation, market analysis, and product development, leading to targeted marketing strategies and revenue growth.

Business goal: Reduce risk and ensure compliance.

Data governance initiative: Implement data security and compliance controls.
Expected outcome: By implementing data security and compliance controls, the organization can protect its data from unauthorized access and use, and ensure compliance with all applicable regulations.

Business goal: Launch a new data-driven product.

Data governance initiative: Establish a data governance framework for the new product.
Expected outcome: By establishing a data governance framework for the new product, the organization can ensure that the data is managed and used in a responsible and ethical manner. This can help to build trust with customers and partners, and reduce the risk of data breaches and other security incidents.

Business goal: Improve Decision-Making:

Objective: Make data-driven decisions to enhance business performance.
Data Governance Alignment: Establish data governance practices to ensure data accuracy, consistency, and reliability, enabling better decision-making based on trusted data.

Business goal: Increase Operational Efficiency:

Objective: Streamline operations and reduce costs.
Data Governance Alignment: Optimize data processes to reduce data redundancy and improve data integration, leading to more efficient operations.

Business goal: Reduce Risk and Data Loss:

Objective: Minimize data breaches and data loss incidents.
Data Governance Alignment: Establish data security and access control measures to protect sensitive data and mitigate the risk of data breaches.

Business goal: Innovation and Product Development:

Objective: Foster innovation and develop new products or services.
Data Governance Alignment: Leverage data governance to facilitate data access and sharing across teams, promoting innovation and enabling data-driven product development.

Business goal: Cost Control:

Objective: Control and manage operational costs.
Data Governance Alignment: Use data governance to identify and reduce data-related inefficiencies, such as data redundancy, which can lead to cost savings.

Business goal: Strategic Planning:

Objective: Develop long-term strategic plans and initiatives.
Data Governance Alignment: Implement data governance to ensure that strategic plans are based on accurate, timely, and relevant data.

Business goal: Data Monetization:

Objective: Generate revenue from data assets.
Data Governance Alignment: Establish data governance practices that enable data monetization through data sharing, partnerships, or selling data-related products and services.

Business goal: Brand Reputation:

Objective: Maintain a positive brand image and reputation.
Data Governance Alignment: Ensure data governance practices to protect customer data, maintain data privacy, and prevent data breaches, which can affect brand reputation.

By aligning data governance with these and other specific business objectives, you can demonstrate the value of data governance in achieving tangible benefits for the organization. This alignment helps secure support and resources for your data governance initiatives and ensures that they contribute to the overall success of the business.

Week 2

Identify Data Influencers To identify data influencers within your company, you can look for individuals who:

Have a deep understanding of the company's data assets and how they are used.
Are passionate about data quality and data governance.
Have the ability to influence others and build consensus.
Are respected by their peers and colleagues.

Data influencers can help the data governance program in a number of ways, including:

Providing expertise and guidance: Data influencers can help to develop and implement data governance policies and procedures. They can also provide guidance on data quality standards and data governance best practices.
Advocating for data governance: Data influencers can help to raise awareness of the importance of data governance and build support for the program across the organization.
Helping to socialize data governance: Data influencers can help to educate stakeholders on the data governance framework, policies, and procedures. They can also help to ensure that stakeholders understand their roles and responsibilities in the data governance program.
Championing data governance initiatives: Data influencers can help to champion data governance initiatives and ensure that they are successful. They can also help to identify and address any challenges or roadblocks that may arise.

Data influencers are individuals or groups who have a significant impact on data-related decisions, processes, or culture. Here are some steps to help you identify data influencers and understand how they can support your data governance program:

1. Review Organizational Structure:

Start by examining your company's organizational structure. Look for roles or departments that have a direct influence on data-related matters. Common areas to consider include IT, data analytics, data science, compliance, legal, and business units with a strong data focus.

2. Engage in Stakeholder Interviews:

Conduct interviews or surveys with key stakeholders to identify individuals or teams who are passionate or highly involved in data-related initiatives. Ask them about their roles, responsibilities, and their interaction with data.

3. Observe Data-Related Initiatives:

Pay attention to ongoing data projects or initiatives. Team members who take the lead, drive innovation, or demonstrate expertise in these projects are often data influencers.

4. Analyze Data Utilization:

Assess how different departments and teams use data. Those who rely heavily on data for decision-making or who are data-savvy can be considered influencers.

5. Review Data Governance History:

Look into past data governance efforts or initiatives. Identify individuals who were actively involved in promoting data governance principles and practices.

6. Examine Communication and Collaboration Patterns:

Analyze communication patterns within the organization. Those who frequently collaborate on data matters or bridge communication gaps between different departments are likely data influencers.

7. Analyze Informal Networks:

Sometimes, data influencers may not hold formal positions related to data, but they have established themselves as go-to resources for data-related questions or solutions. Identify these informal leaders.

Once you've identified data influencers, it's important to understand how they can support your data governance program:

1. Advocate for Data Governance:

Data influencers can help promote the importance of data governance within the organization. They can become advocates for data governance, helping to secure buy-in from colleagues and senior management.

2. Lead by Example:

Data influencers who demonstrate good data governance practices in their work set a positive example for others to follow.

3. Influence Decision-Making:

Data influencers often have the ability to influence data-related decisions and policies, which can align with the goals of your data governance program.

4. Bridge Communication Gaps:

They can serve as liaisons between different departments and teams, facilitating collaboration and communication regarding data governance issues.

5. Provide Subject Matter Expertise:

Data influencers can contribute their expertise to the development and implementation of data governance policies, procedures, and best practices.

6. Act as Change Agents:

They can champion cultural changes necessary to make data governance a part of the organizational DNA.

Here are some specific examples of how data influencers can help the data governance program:

A data influencer in the sales department can help to develop data quality standards for customer data.
A data influencer in the marketing department can help to educate other marketing team members on the data governance framework and policies.
A data influencer in the IT department can help to implement data security and compliance controls.
A data influencer in the executive team can help to promote data governance across the organization and secure the resources needed to implement the program successfully.

By identifying and engaging with data influencers within your company, you can build a strong foundation for your data governance program.

Here are some tips for identifying data influencers within a company and leveraging them to support a data governance program:

Look for power users of major data systems like ERPs, CRMs, analytics platforms. They have extensive knowledge of data and how it flows.
Identify business unit leaders and managers who rely heavily on data for decision making. Getting buy-in from them is key.
Find analysts and report creators who consume data from multiple sources. They understand dependencies and connections.
Consult with IT teams supporting critical systems. They maintain key data flows and infrastructure.
Talk to long-tenured employees. Their institutional knowledge can uncover hidden data challenges.
Seek out "data passionate" people. They advocate for the importance of data quality even if not in analytics roles.

Week 3

Assess your organization's readiness

This involves a broader reach to help quantify and qualify the information shared by the data owners. Conduct a survey or assessment to understand your organization's current data management practices, culture, and maturity level. Identify any gaps, challenges, or areas for improvement.

An example of the list of questions to include in your assessment Data management practices

Do you have a data governance framework in place?
Do you have policies and procedures in place for data collection, storage, access, and use?
How do you ensure the quality and accuracy of your data?
How do you monitor and measure data governance performance?
How is data currently collected, stored, and managed in your organization?
Do you have documented data management policies and procedures in place?
Who is responsible for data management within your organization?
What data governance tools, if any, are currently in use?
How often is data quality assessed and maintained?
What data security measures are in place to protect sensitive information?
How is data access control and authorization managed?
Are there data backup and disaster recovery procedures in place?

Data culture

Is there a culture of data-driven decision-making within your organization?
Do employees have the skills and knowledge they need to use data effectively?
Are employees encouraged to share data and collaborate with others?
Is there a culture of transparency and accountability around data use?
How do employees view the importance of data within your organization?
Is there a culture of data-driven decision-making, and if so, to what extent?
Are there data champions or advocates within your organization?
Do employees receive training on data-related best practices?
Are data-related goals integrated into performance evaluations?

Data maturity level

On a scale of 1 to 5 (1 being low, 5 being high), how would you rate your organization's overall data maturity?
How well-defined are your data roles and responsibilities?
How mature is your data governance framework and policies?
To what extent is data integrated into your strategic planning and decision-making processes?
How mature are your data quality and data integration processes?
Is there a clear understanding of data lineage and data ownership within your organization?
What are the biggest challenges facing your organization in terms of data management?
What are your top priorities for improving data management in the next year?

Data gaps and challenges

Are there any areas where you have gaps in your data?
Are there any data-related challenges that are preventing you from achieving your business goals?
What are the most common data quality issues experienced within your organization?
Do you face challenges related to data accuracy, consistency, or completeness?
Are there data silos or challenges related to data integration and sharing?
What data privacy and compliance challenges do you encounter?
Do you have a clear understanding of your organization's data security challenges and vulnerabilities?
What are the key obstacles to effective data governance within your organization?
Are there specific data-related projects or initiatives that have faced significant challenges or delays?

Data Tools and Resources:

What data management and data governance tools are currently used in your organization?
Are there any specific data-related training or resources provided to employees?

Data Future:

What are your future plans or goals related to data management and governance?
What resources or support would you need to improve data management practices and culture?

In addition to these general questions, you may also want to include more specific questions that are tailored to your organization's industry and business model. For example, if you are in the healthcare industry, you may want to ask questions about HIPAA, GxP, GDPR compliance. These questions will provide insights into your organization's data landscape, highlighting strengths and weaknesses in data management practices and culture. They can help identify areas that require improvement, allowing you to tailor your data governance initiatives to address specific challenges and gaps.

During week 3, identify current Data Policies Here are some examples of the data policies you might find. Depending on the type of organization, you'll identify gaps in the data policies and data standards. These documents/artifacts also helps in assessing and identifying what the organization values as data asset, data quality expectations, data security needs etc. You'll also be able to gather the business requirements that drove the creation of the policies and standards.

Data Policies:

Data Governance Policy: This overarching policy defines the organization's commitment to effective data governance, outlines the structure of data governance roles and responsibilities, and establishes the importance of data quality, security, and compliance.
Data Privacy Policy: This policy outlines how the organization collects, stores, and protects sensitive customer or employee data to ensure compliance with data privacy regulations (e.g., GDPR, CCPA).
Data Security Policy: Data security policies set guidelines for protecting data from unauthorized access, breaches, and cyber threats. They cover aspects like encryption, access controls, and incident response.
Data Retention Policy: This policy defines how long different types of data should be retained, when data should be archived or deleted, and under what circumstances data can be disposed of.
Data Classification Policy: Data classification policies categorize data based on its sensitivity and importance, determining how it should be handled, stored, and secured.
Data Access and Authorization Policy: This policy governs who can access specific data, what permissions they have, and how they should request and manage access rights.
Data Quality Policy: A data quality policy establishes the standards and procedures for ensuring the accuracy, completeness, consistency, and reliability of data.
Data Integration and Interoperability Policy: This policy outlines how data should be integrated across systems and departments to ensure consistency and interoperability.
Master Data Management (MDM) Policy: An MDM policy provides guidelines for creating, maintaining, and governing master data, such as customer or product data, to ensure consistency and reliability.
Data sharing policy: This policy defines the rules for how the organization shares data with third parties.

Data Standards:

Data Naming Conventions: Data naming standards define how data elements, tables, and fields should be named to ensure consistency and understanding across the organization.
Data Formatting Standards: Formatting standards specify how data should be presented and formatted, including date formats, numerical representations, and data encoding.
Data Coding Standards: Coding standards provide guidelines for coding data, such as using specific codes for categories or values.
Data Classification Standards: These standards define the criteria and process for classifying data into categories (e.g., public, internal, confidential) based on sensitivity.
Data Dictionary Standards: Data dictionaries provide a common repository for defining data elements, their meanings, and relationships, making data standards easily accessible and understandable.
Data Quality Standards: Data quality standards establish criteria and metrics for measuring and maintaining data quality, including data accuracy, completeness, consistency, and timeliness.
Data Metadata Standards: Metadata standards define how metadata is captured, stored, and managed to ensure consistency in describing and documenting data.
Data Interchange Standards: These standards determine the format, structure, and protocols for data interchange between systems or with external partners, ensuring compatibility. This may be called data exchange standard.
Data Encryption and Hashing Standards: Standards for encrypting and hashing data to protect it during transmission and storage.
Data API Standards: If your organization uses APIs to access or share data, standards can ensure consistency in API design, documentation, and usage.
Data modeling standard: This standard defines the rules for creating and maintaining data models.
Data encoding standard: This standard defines how data is encoded for storage and transmission.
Glossary of terms: This document defines the meaning of key terms used in the organization's data environment.

These policies and standards provide a framework for organizations to govern and manage their data effectively, ensuring data quality, security, compliance, and consistency across the organization.

Week 4

Assessing Current Data Architecture:

Data Inventory: Start by cataloging and documenting all data assets in your organization. Identify data sources, formats, and locations. Document how data flows through your systems. Which data assets are most critical to your business?
Data Flow Analysis: Analyze the flow of data within your organization. Understand how data moves between different systems, departments, and teams. What systems and applications are being used to store and manage your data? How does your data flow between different systems and applications?
Data Mapping: Create data flow diagrams and data lineage maps to visualize how data is processed, transformed, and integrated across the organization.
Data Quality Assessment: Evaluate the quality of your data. Look for issues like inaccuracies, duplications, inconsistencies, and missing data. Are there any areas where your data is incomplete or inconsistent?
Data Security Assessment: Assess the security measures in place to protect data from unauthorized access, breaches, and cyber threats.
Data Integration Analysis: Examine how data is integrated across systems and departments. Identify data silos and integration challenges.
Data Privacy and Compliance Review: Ensure that your data architecture aligns with data privacy regulations and industry-specific standards. Assess how data is handled to maintain compliance.
Scalability and Performance Assessment: Evaluate whether your data architecture can scale with the growth of your organization and whether it meets performance requirements.
Data Access and Authorization: Review data access controls and authorization mechanisms to ensure they are consistent and aligned with data governance principles.
Data Tools and Technology Stack: Document the data-related tools and technologies you use, including databases, analytics platforms, data integration tools, and data governance software.
Assess the performance of your data architecture. Is your data architecture able to meet the needs of your business in terms of performance, scalability, and security?

In the next post, I'll cover the activities you should plan for the second month of your data governance journey.

Sash Barige

Apr/24/2022

SASH BARIGE

Data Governance Step by Step: Month 1

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