This article recaps a previously live streamed panel from Data Leaders Forum: Governance Reimagined 

Relive the rest of the panels:

• ‍Governance Reimagined: Faster Access, Stronger Controls
• Governance Reimagined: Rethinking Governance for The Future

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Below are some key highlights from the discussion, featuring three industry experts shared insights on creating scalable, impactful governance frameworks within their organizations. Kapil Kanagal, Chief Data Scientist at TransformCo, an executive overseeing a major cloud migration, shared strategies for transforming data swamps into reliable, accessible data ecosystems that drive business value. Rudy Rudolphi, Senior Analytics Manager at Loop Returns, discussed the importance of reducing tech debt and how she streamlined team workflows by minimizing time spent on ad hoc data requests. Finally, Amine Ben Amor, Director of Engineering, from IQVIA highlighted the critical role of scalability in data governance, focusing on establishing frameworks that evolve with company growth and prevent operational risks. Together, our panelists offered practical approaches to embedding data governance practices that not only mitigate risk but also empower organizations to maximize their data’s potential.

Defining Data Governance: Balancing Flexibility and Control

Data governance is often defined based on an organization’s structure and goals. Kapil defined data governance as striking the right balance between flexibility and control, particularly within a multi-brand enterprise like TransformCo. With brands under one umbrella, Kapil’s team faces the challenge of coordinating different data practices, making it essential to create a unified approach for managing data lineage, access, and ownership.

He emphasized the critical role of data governance in both mitigating risks and creating value within the organization.

• Risk Mitigation: Kapil highlighted that effective data governance is essential for establishing a "single source of truth" across TransformCo's diverse brands. By standardizing data definitions and implementing robust data lineage tracking, the company ensures consistency and accuracy in its data assets. This approach minimizes the risk of discrepancies and errors that can arise from inconsistent data sources, thereby safeguarding the integrity of business operations.

• Value Creation: Beyond risk mitigation, Kapil discussed how data governance facilitates value creation by enabling more informed decision-making. With clear data lineage and standardized definitions, teams can efficiently access and analyze data, leading to actionable insights. For instance, this governance framework has empowered TransformCo to reprice its insurance portfolio and optimize pricing models across various business areas, directly contributing to enhanced financial performance.

Rudy’s team sees governance as an enabler of value - a way to ensure documentation is readily available so that team members can locate and understand data independently. By focusing on accessibility, Rudy believes data governance can transform data from a centralized asset into a shared resource that empowers decision-making across the company.

Amine emphasized scalability as a core aspect of their data governance framework. IQVIA’s healthcare-focused data work involves large, sensitive data sets, and Amine’s team developed a governance structure that supports cross-functional access. For IQVIA, data governance is about equipping each department with the necessary tools to autonomously access and leverage data insights relevant to their roles, without overwhelming the data team with routine support requests.

The business impact of governance strategies

Implementing a data governance strategy that aligns with business goals, rather than imposing strict regulations, was a recurring theme in the panel discussion.

As a high-growth startup, Loop Returns initially perceived governance as potentially bureaucratic. Rudy and her team shifted this perspective by creating accessible documentation, making data easy to find and understand for non-technical team members. The result is a culture of self-service that fosters agile decision-making, enabling faster, data-informed choices without overloading the data team with requests. With this strategy, governance supports Loop’s growth and scalability by empowering teams to explore data independently.

They have integrated "tech debt days" into their data governance framework. “no documentation is another form of tech debt” she aptly mentioned. This initiative involves dedicating specific days for the data team to address technical debt—accumulated inefficiencies and outdated processes within their data systems. The implementation of tech debt days has led to several benefits:

• Improved Data Quality: Regularly addressing technical debt ensures that data systems remain robust and accurate, reducing errors and inconsistencies.
• Enhanced Efficiency: By proactively managing technical debt, the data team can streamline processes, leading to more efficient data operations.
• Support for Governance Goals: This practice aligns with Loop Returns' broader data governance objectives by maintaining a well-structured and reliable data environment.

Amine described how IQVIA’s self-service model allows departments to access relevant data with minimal dependence on the data team, which is especially valuable in the healthcare sector where rapid insights are a critical requirement. IQVIA’s governance strategy has allowed the data team to focus on high-priority projects rather than managing routine queries, directly contributing to improved operational efficiency and faster innovation cycles.

Kapil explained how data governance at TransformCo allows them to maintain a single source of truth across various brands. For example, their governance practices support consistent data definitions, lineage tracking, and access control, making it easier to perform critical analyses, like repricing insurance portfolios or refining pricing models. Governance is fundamental for managing risk and maximizing data-driven decisions, especially in a legacy system environment where data quality can vary significantly across portfolios.

Kapil discussed the company's strategic initiative to migrate from traditional on-premises data systems to a modern cloud-based architecture. This transition aimed to address the challenges of managing vast amounts of unstructured and poorly organized data in their data swamp inherited from legacy systems.

TransformCo, overseeing brands like Sears and Kenmore, faced the complexities of consolidating data from multiple sources, each with its own structure and governance practices. The primary objective was to transform this fragmented data landscape into a cohesive, well-governed data lake that would serve as a single source of truth across the organization.

To achieve this, Kapil's team implemented a robust data governance framework that included:

• Standardizing Data Definitions: Ensuring consistent terminology and data structures across all business units to facilitate seamless integration.
• Implementing Data Lineage Tracking: Utilizing tools like Secoda to monitor data flow from source to destination, enhancing transparency and trust in data processes.
• Establishing Access Controls: Defining clear data ownership and access policies to maintain data integrity and security.

This migration not only mitigated the risks associated with data swamps—such as inefficiencies and potential security vulnerabilities—but also enabled the company to harness data-driven insights more effectively.

Measuring and quantifying the success of your data governance program

Rudy's team at Loop Returns tracks success through metrics Secoda provides such as the number of monthly active users engaging with their data platform and the volume of ad hoc data requests. A significant increase in platform usage and a decrease in ad hoc requests indicate that stakeholders are effectively utilizing self-service data resources, demonstrating the program's success.

Kapil emphasized the importance of establishing a "single source of truth" across TransformCo's diverse brands. By standardizing data definitions and implementing robust data lineage tracking, the company ensures consistency and accuracy in its data assets. This approach minimizes the risk of discrepancies and errors that can arise from inconsistent data sources, thereby safeguarding the integrity of business operations. The effectiveness of this strategy is reflected in improved decision-making processes and operational efficiencies.

Amine's approach at IQVIA involves monitoring the frequency of data-related questions directed to the data team. A reduction in these inquiries suggests that users are independently accessing and understanding data, reflecting the effectiveness of their data governance framework. Additionally, they assess the program's impact on operational efficiency and the ability to scale data processes across the organization.

Tools and processes for embedded data governance workflows

The panelists agreed that embedding data governance into daily workflows requires the right mix of tools and best practices, and each organization has employed various approaches to ensure data governance is not only present but integrated seamlessly into everyday tasks.

Each organization highlighted Secoda’s role as an integral part of their data governance toolkit. Secoda’s platform capabilities, such as data lineage tracking, data cataloging, and integration with cloud platforms like Google Cloud and Snowflake, make it a valuable resource for unifying data practices. Kapil noted that Secoda helps TransformCo ensure consistent data definitions across brands, reducing misinterpretations and improving data transparency.

At Loop Returns, Rudy’s team has built a suite of self-service resources, including a “data sources starter kit” and a “dashboard documentation best practices guide.” By creating comprehensive, accessible documentation within Secoda, Loop’s team members can find answers independently, reducing the need for repetitive support from the data team. This approach not only fosters self-service but also promotes a culture where governance is seen as a helpful, empowering resource.

Before implementing Secoda, the data team at Loop Returns faced a significant challenge: over 90% of their time was consumed by ad hoc data requests. This reactive approach hindered their ability to focus on strategic initiatives and proactive data analysis. By integrating Secoda, the team established a centralized repository for data assets, including comprehensive documentation and metadata. This shift enabled stakeholders to self-serve their data needs, significantly reducing the volume of ad hoc requests directed at the data team.

Some of the benefits they saw include:

• Time Reallocation: With a decrease in ad hoc requests, the data team could reallocate their efforts toward strategic projects and in-depth analyses, enhancing overall productivity.
• Improved Data Literacy: Stakeholders gained the ability to independently access and understand data, fostering a culture of data literacy and empowerment within the organization.
• Enhanced Data Governance: The centralized documentation facilitated by Secoda ensured consistent data definitions and usage, strengthening data governance practices across Loop Returns.

To further encourage self-service, Loop also established a dedicated Slack support channel where stakeholders can receive quick answers and guidance on using Secoda’s resources. Automated workflows in Slack help guide users to existing documentation first, reducing unnecessary requests and reinforcing self-service as the primary method for data access.

Managing data governance at scale

Amine identified the inability to scale as a business risk directly related to data governance. If a company’s data infrastructure and governance practices are not scalable, it becomes increasingly challenging to handle larger volumes of data, support diverse data sources, and accommodate growing data needs across departments.

Amine highlighted that without scalable systems, organizations risk inefficiencies in data processing, inconsistent data practices, and data silos, all of which hinder timely decision-making and reduce productivity. As companies grow, data complexity increases, and without a scalable governance framework, data teams can become overwhelmed by routine tasks, leading to technical debt and limiting focus on strategic initiatives. Prioritizing scalability in data governance lays a foundation that evolves with the organization, mitigating risks of costly overhauls and ensuring smooth, sustainable growth.

Secoda enabled IQVIA to centralize and streamline their data assets, providing a unified repository for data documentation, lineage, and metadata. IQVIA empowered various departments to independently access and utilize data, reducing reliance on the data team for routine inquiries. This approach not only enhanced operational efficiency but also allowed the data team to focus on strategic initiatives, thereby supporting the company's growth and scalability objectives.

Best practices when rolling out a data governance program

The panelists collectively recommended a "go deep before wide" approach to implementing data governance. They suggested that organizations focus initially on developing deep, robust governance practices within key areas of their data ecosystems, rather than attempting to apply governance broadly across all data assets from the outset. This approach allows teams to establish clear, effective governance practices that can later be scaled to other areas.

Kapil emphasized the importance of starting with core datasets and ensuring that these are well-governed, standardized, and trusted across the organization. He pointed out that by focusing on high-impact data first, teams can demonstrate the value of governance to stakeholders early on, helping to secure further buy-in for expanding governance practices.

Rudy discussed how Loop Returns initially focused governance efforts on datasets most frequently used by the business, especially those tied to critical decision-making. By ensuring these datasets were accurate, accessible, and well-documented, they were able to reduce the volume of ad hoc requests and empower teams to find answers independently, showcasing a clear ROI on their data governance program.

Amine highlighted that at IQVIA, going deep in governance means prioritizing foundational frameworks that ensure scalability and efficiency. By first establishing solid governance structures around high-priority data assets, IQVIA’s team could create a scalable model that they could later replicate for other datasets without compromising quality.

Data Leaders Forum: Data Governance Reimagined

The Data Leaders Forum underscored the significance of data governance as both a regulatory and strategic imperative. Panelists demonstrated that effective governance doesn’t just protect data integrity—it drives business outcomes by empowering teams to make informed, data-driven decisions. 

From defining governance principles that align with business goals to deploying tools like Secoda to simplify access and documentation, the forum highlighted practical steps for embedding data governance into enterprise workflows. For modern enterprises, data governance is not about control for control’s sake; it’s about enabling a flexible, scalable framework that maximizes the value of data across all functions. By building a self-service culture supported by clear documentation and user-friendly tools, companies can embed data governance in a way that enhances productivity and fuels innovation.