Data Governance: the challenges and necessities

Data governance is crucial for ensuring that a company fully leverages the value of its data. Organizations encounter significant obstacles in the form of stringent regulatory compliance demands, insufficient data literacy among staff, entrenched cultural resistance to change, and complexities in integrating new technologies with existing systems. 88% of data leaders stated that data security is expected to become an even higher priority by 2025, ahead of AI. These challenges further encourage the implementation of effective data governance practices.

Implementing strong data governance within an organization encompasses building a comprehensive framework of policies, procedures, and standards that guides internal stakeholders on how data is to be collected, stored, processed, and used throughout an enterprise. About 60% of corporate leaders have prioritized Data Governance and 62% of organizations plan to audit their existing Data Governance programs and explore new corporate Data Governance policies.

Data maturity assessments serve as a vital tool for organizations to capture their current state of maturity or seek to enhance their data governance practices. These assessments help identify areas requiring improvement and establish clear development priorities, enabling more efficient allocation of resources for data governance initiatives. 

As we can see on the Data Governance Maturity Map, we can analyze the Data Governance of a company as per multiple different aspects, within a scale of 1-5. The map allows us to dive deeper into specific components of the Data Governance Maturity of a company, focusing on specific aspects such as Data Sharing, Data Quality and other aspects. By visualizing a future maturity state of the company allows the organization to focus efforts to better attain this goal.

Furthermore, they ensure that the level of data governance aligns seamlessly with the organization's overall business strategy, fostering a more cohesive approach to data management. By providing a means to monitor and evaluate the effectiveness of data governance efforts, these assessments allow organizations to continuously refine their practices. 

One such example is the Data Governance Maturity Model of Gartner as can be seen below: 

Five levels of Data Governance Maturity:

1. Unaware (Level 0): No formal data governance or management processes exist.
2. Aware (Level 1): Organizations recognize the need for data governance but lack implementation.
3. Reactive (Level 2): Basic data management policies are created, but adherence is low.
4. Proactive (Level 3): Information management is more structured and proactive.
5. Managed (Level 4): Data governance is integrated into business processes.
6. Effective (Level 5): Data governance is fully embedded in organizational culture.

To link the Data Governance Maturity Map with the Gartner Data Governance Maturity Model, we map the score of 0 to "Unaware" (Level 0) till Effective (Level 5) and then use the average of all the scores in each category to find the final state.

Suppose a company assesses their Data Governance Maturity across the following categories. Each category is scored on a scale of 0-5, and the average score is calculated as 2.8 overall.

Based on this average score of 2.8, the company's overall Data Governance Maturity level aligns closely with the "Proactive" (Level 3) stage in the Gartner Data Governance Maturity Model. Our team also works to evaluate the entire data governance level holistically based on other evidence and company data given to analyze. 

This method allows for a comprehensive assessment and helps identify specific areas that require attention, facilitating targeted improvements in data governance practices. This approach provides a quantifiable method to assess and improve data governance practices systematically. 

Ultimately, data maturity assessments guide companies towards more robust, strategic, and effective data management, supporting better decision-making and operational efficiency.

Data governance faces numerous challenges, including resource constraints, data fragmentation, data silos, and insufficient leadership support. These issues are compounded by poor data quality, organizational resistance, and complex data ecosystems. 

Gartner's research identifies key governance concerns, with compliance audits (52%), non-compliance warnings (40%), and data breaches (37%) being the most common. Other notable issues include data loss (21%), non-compliance fines (20%), and data leaks (19%).

Additional obstacles include ongoing monitoring of regulatory compliance, insufficient data literacy, cultural resistance, and challenges with technology integration. To tackle these issues, organizations need to secure strong executive support, allocate adequate and suitable resources, improve data literacy, and cultivate a data-centric culture. Gradual implementation of governance, utilizing automation, and demonstrating business value can aid in overcoming this resistance.

The benefits of establishing a robust data governance framework are significant, with 66% of organizations reporting improved data security and 52% experiencing reduced compliance breaches. These positive outcomes underscore the importance of overcoming governance challenges to realize substantial improvements in data management and security.

Data governance champions and key roles are essential for implementing and maintaining effective data governance within an organization. The main roles typically include a Data Governance Sponsor or Champion, Data Governance Lead, Data Owners, Data Stewards and Data Users. 

At a strategic and tactical level, the Sponsor, usually a senior executive, provides high-level support and aligns data governance with business strategy. The Data Governance Lead or council oversees day-to-day implementation, coordinates the Data Governance Center of Excellence, and drives decisions on data standards. 

At an operational level, Data Owners are responsible for specific data domains, ensuring quality and compliance. Data Stewards act as a bridge between business and IT, helping standardize data definitions and optimize workflows. 

And finally for the data users. Data Users are employees who use data in their daily work and are responsible for adhering to governance policies. Together, these roles create a comprehensive framework that ensures data quality, security, and compliance while enabling effective use of data across the organization.

Snowflake and Databricks solutions both offer comprehensive data governance solutions to address security, compliance, and data quality challenges:

Snowflake provides robust governance features including column and row-level security, object tagging, data classification, access policies, and activity tracking. These capabilities help secure sensitive data, monitor usage, and simplify regulatory compliance.

Databricks leverages its Data Intelligence Platform, which combines generative AI with lake house architecture. Key governance features include Delta Lake for ACID transactions and time travel, data management tools, and strong security measures. The platform aims to understand an organization's unique data semantics.

Both solutions enable organizations to effectively govern their data assets, enhance visibility into data usage, and support improved decision-making and operational efficiency. Their comprehensive approaches help address the complex data governance challenges faced by modern enterprises.

BayBridgeDigital offers data maturity assessments to help better understand the data maturity of your company. BayBridgeDigital also provides skilled and certified Data Engineers and Data Scientists in Databricks and Snowflake to support your company's Data Governance Transformation Journey. 

As an authorized partner of Databricks, our devoted team is dedicated to crafting customized solutions that improve efficiency and drive your data governance transformation journey.  We also offer the service of implementing services such as snowflake and databricks specializing in Data governance and Data quality expertise.

1. What is the importance of data governance? 

Data governance is crucial in today's data-driven business environment, ensuring data quality, reliability, and security while enabling regulatory compliance.Its importance lies in maximizing the value of data assets while minimizing risks and ensuring compliance across the organization.

2. What are the main expertise that BayBridgeDigital offers?

BayBridgeDigital provides comprehensive data maturity evaluations to gauge and analyze your organization's current data capabilities. Additionally, we offer specialized implementation services, including Snowflake and Databricks deployment with a focus on data governance, to help establish and optimize your company's data quality.

3. What are the main benefits of Snowflake and Data bricks Data governance features?

Both solutions empower organizations to efficiently manage their data assets, increase transparency into data usage, and bolster decision-making and operational effectiveness. Their holistic approaches effectively tackle the intricate data governance challenges encountered by contemporary enterprises.