👩‍💻 Actionable Ways to Implement Data Governance

Following up on our conversation with Susan Walsh last week, where we explored the challenges, businesses face when implementing data governance - we’re looking at the steps organizations can take to implement data governance processes below. 

🌐 From the Web

Why a DataOps Team Needs a Database Reliability Engineer 

The emergence of a Database Reliability Engineer (DRE) akin to an SRE is vital for improved data stack clarity. GitHub's outages highlight the need for better database design and optimized queries. A DRE can foster a data-driven mindset, educate developers, simplify database complexities, and enhance data accessibility, foreseeing a pivotal role in modern data management. 

5 ways to use AI and machine learning in dataops 

Managing the operations to integrate and cleanse data is labor intensive. AI and ML transform data operations in 5 key ways. Adoption empowers efficient, high-quality data management, advancing businesses toward competitive advantages. 

The Data Governance Hub and Spoke Model: Why it Works 

The hub and spoke model strikes a balance between centralization and decentralization, offering control while allowing flexibility in data manipulation. It empowers both technically adept hub teams and non-technical spoke teams by enabling virtual locations for data management, fostering data democratization while maintaining quality and security. 

Before getting started, let’s look at the Core Data Governance capabilities:

Authentication & Authorization: Verifying appropriate user access to specific data resources. 

Information Architecture: Concentrating on efficient and enduring data organization, structure, and labeling.  

Provisioning and Rights Management (Data Stewards & Data Asset Definitions): Distributing data to users for ensuring top-notch data quality and clear comprehension of data assets.  

Data Catalog and Classification: Supplying data overviews and metadata for simplified data access and comprehension.  

Data Lineage: Comprehending the consequences of alterations in primary systems and subsequent effects.  

Data Mastering: Collection of specified tasks to establish a sole reliable data source encompassing all business-critical data across the organization. 

The basic model above can help businesses understand the gaps they need to fill in their existing data governance strategies. 

🎯 Define Goals, Objectives, and Stakeholders for Data Governance 

Initially, take time to detach from immediate problems and think about your long-term goals for your businesses data, and the stakeholders who will work with that data. 

Define Goals and Objectives: 

• Clarify the intended outcomes: Improve data quality, enhance transparency, accountability, and decision-making. 

• Establish these goals as the cornerstone of the project to guide subsequent steps. 

Identify Stakeholders: 

• Gather a cross-functional team for implementation. 

• Ensure involvement and buy-in from various stakeholders: 

  • Data teams 

  • IT department 

  • Security department or relevant individuals

(Note: These steps ensure a clear understanding of objectives and engage the necessary stakeholders for successful data governance implementation.) 

📐 Structuring your data governance framework 

Create a tailored data governance strategy aligned with your business objectives to expedite the delivery of top-tier data. To ensure that data governance enhances rather than impedes your data delivery pace, it's crucial to devise a data governance strategy that considers your organization's distinctive objectives and resource limitations. There are three potential ways to structure your data governance framework: top-down, bottom-up, or a hybrid model: 

• The top-down approach: This method involves establishing regulations and principles aligned with business objectives at the executive level. While this centralized approach supports a long-term vision for data governance, it carries a heightened risk of failure due to the potential need for more specific governance policies to cater to detailed operations. 

• The bottom-up approach: Here, day-to-day data activities and procedures dictate the formation of governance policies and principles. This targeted method facilitates the identification of challenges, yet it may lack cohesion in data governance efforts as different departments are inclined to pursue their individual goals rather than a unified vision. 

• The hybrid approach: Incorporating a hybrid approach entails setting overarching data governance standards and a long-term vision at the executive level. However, it allows teams the flexibility to craft their own governance within the framework of higher-level standards and in harmony with the future vision, fostering a balance between unified direction and departmental autonomy. 

🛠 Implementing processes to support the framework 

As you progress with your data governance framework, the critical phase of implementation demands the activation of established principles through effective measures and protocols: 

Execution of Framework: 

• Translate the devised framework into actionable strategies. 

• Implement robust processes ensuring compliance with governance guidelines. 

• Establish data quality assurance protocols to maintain high standards. 

• Enforce access control measures for data security and confidentiality. 

Leveraging Data Tools: 

• Ensure your data infrastructure tools offer granular control over data management. 

• Capability to manage controls at an intricate level, encompassing each data entry and user interaction. 

• Extend the application of governance tools across various organizational sections for comprehensive data management. 

Continuous Monitoring and Evaluation 

Understanding that data governance is a dynamic process requiring constant attention and adaptation, the ongoing assessment of its effectiveness is crucial: 

Continuous Assessment: 

• Regularly monitor the functioning of data governance measures. 

• Evaluate effectiveness in alignment with predefined goals and objectives. 

• Identify areas necessitating improvements or modifications. 

Adaptive Evolution: 

• Evolve data governance strategies parallelly with changing data utilization in the business. 

• Ensure the initiative remains aligned with evolving business needs and technological advancements. 

This ongoing scrutiny and adaptability ensure that the data governance initiative remains effective, adaptive, and aligned with the evolving landscape of data utilization within the organization. 

💻 Platform Highlight

Data Governance Courses on LinkedIn: expert-led courses Data Governance courses on LinkedIn. 

Data Governance Concepts: Introductory guide to data governance, and how to implement a data governance framework. 

DGI Data Governance Framework: A list of comprehensive data governance and data management courses offered by The Data Governance Institute. 

💼 AI in Business

How Businesses can start with Generative AI 

Companies navigating Generative AI (GenAI) face challenges in determining where to begin implementation. An article from Harvard Business Review suggests examining GenAI through two lenses: a broader, future-oriented perspective and a more immediate, practical view. They introduce the concept of WINS Work—tasks heavily reliant on Words, Images, Numbers, and Sounds—as a focal point for leveraging GenAI. The article categorizes companies into four groups based on their reliance on WINS work and digitization levels: 

🔍 Assessing Company Urgency: 

Companies are advised to evaluate the extent of WINS work in their cost structure and the current digitization level of WINS inputs. 

Plotting on a 2×2 matrix helps categorize companies into segments: In the Crucible, Holding a Lever, Next in Line, and In the Balcony. 

📍 Categories of Company Positioning: 

In the Crucible: Industries heavily reliant on WINS work and high digitization levels. Immediate adoption of GenAI is recommended for competitiveness. 

Holding a Lever: Companies with the potential to gain advantages through GenAI, even if not primarily focused on WINS work. 

Next in Line: Companies with opportunities to digitize non-digitized tasks using GenAI for future advantages. 

In the Balcony: Businesses with low digitization and limited WINS work. They’re advised to continue learning about GenAI's potential impact. 

🛠 Implementation Steps: 

Recommendations for companies include forming cross-functional teams, experimenting with GenAI at different levels, and strategizing based on their position in the categorization. 

⚠️ Risk Considerations: 

Caution is advised when using GenAI, particularly for high-risk tasks. Human oversight is essential due to potential inaccuracies in current GenAI models. 

💡 Actionable Strategies: 

Depending on their categorization, companies are advised to engage in immediate experimentation, digitization efforts, or continued learning about GenAI's potential benefits. 

🔭 Long-Term Vision: 

Businesses should adopt a proactive stance towards GenAI, as failure to act could lead to increased competition and disruption within a relatively short period. 

♟️ Strategic Approach: 

The article emphasizes a proactive approach to GenAI adoption, highlighting its potential to transform industries within a few years. 

💬 Social Highlight

Data scientists on Reddit highlight their year-on-year salary progression: Link. 

Flashback: “a watershed moment for data governance”: Link

🤖 Prompt of the week

Generate business tags for a table named: [table name]. With the following columns: [columns name]. The query used to create the table: [insert query]. And for non-sensitive tables, you can add a data sample: [data sample].

See you next week,

Mukundan