Homebot takes the lead in data quality with Secoda’s scoring system

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Homebot is a leading customer engagement platform that helps homeowners build wealth by maximizing their home equity. Homebot provides users with insights into their home’s value, market conditions, and refinancing options.

As Homebot’s organization expanded, so did the volume and complexity of their data. With more data came more questions, making data quality and governance increasingly critical to their operations. Ensuring accurate and comprehensive documentation became essential for maintaining data integrity across teams.

Homebot was already utilizing Secoda to streamline data cataloging, but as their needs evolved, the introduction of Secoda’s Data Quality Score (DQS) provided the solution they were seeking. Enter Secoda DQS. As early adopters of the DQS feature, Homebot leveraged this powerful tool to standardize their data quality processes, improve efficiency, and set new standards for documentation and data governance. The integration of DQS enabled them to create a more structured, reliable approach to managing their growing data landscape.

The results

• Improved data quality standards by utilizing Secoda's Data Quality Score (DQS), which provided clear and measurable metrics to enhance documentation and ensure that data models met high standards.
• Reduced documentation time by 50%, allowing the team to cut the time spent on data documentation in half and focus on more strategic initiatives.
• Improved data accuracy and consistency across all assets through the adoption of DQS, establishing standardized processes for data validation and enhancing governance practices that foster trust and compliance among stakeholders at Homebot.
• Reduced the BI team’s workload significantly by implementing DQS and self-service tools, allowing team members to transition from reactive troubleshooting tasks to proactive data analysis and strategy development that directly supports business objectives.

The goal

Homebot set out to enhance data quality and documentation practices across their data models by adopting a solution that provided clear, quantifiable metrics. Their goal was to streamline their data operations, improve efficiency, and maintain high standards of data governance.

The challenge

The rapid growth of Homebot’s data team, combined with evolving organizational structures, introduced several challenges. As ownership of the Secoda platform shifted between team members, maintaining consistency in managing data and documentation became increasingly difficult. At one point, the engineering team played a significant role, but changing priorities left the BI team to handle the platform independently, introducing hurdles to maintaining consistent, up-to-date documentation.

At the same time, managing a growing volume of data added complexity. The team needed to ensure that all data models met the necessary documentation standards while keeping pace with the organization’s expansion. This balancing act made it challenging to maintain the level of data governance Homebot aspired to achieve.

Centralizing data knowledge to break down silos

Homebot’s journey with Secoda began when Matthew DiRe, the manager of Homebot’s BI team, recognized the potential of Secoda for data cataloging and discovery. Matt and his team became strong champions of the product, initially adopting it to streamline data cataloging. However, the team faced challenges early on, particularly during periods of organizational change and shifting ownership of the platform. These challenges were gradually overcome as the team gained more experience with Secoda.

A key turning point came when Homebot decided to fully leverage Secoda’s Questions feature as a central hub for all data-related inquiries.

DiRe noted, "We opened up the Questions feature as a way to funnel questions from the entire company into one spot for us to manage, which has been really helpful in organizing and ensuring that people have a platform to broadly ask questions."

By centralizing their documentation through these questions, Homebot steadily improved their data management practices. Secoda’s Questions feature allowed team members to reference previous questions and answers, minimizing redundant work and significantly speeding up documentation processes. The integration of Secoda’s automatic documentation suggestions—powered by AI—further accelerated the process. These suggestions provided ready-made descriptions for tables and columns, helping the team maintain consistent documentation standards without needing to manually input every detail. Together, these features helped Homebot reduce their documentation time by 50%, allowing the BI team to focus on more strategic tasks.

They soon recognized that proactively managing questions directly impacted their Data Quality Score (DQS). The scoring system awarded points based on the percentage of answered questions, incentivizing prompt, thorough responses. This connection between Questions and DQS emphasized the importance of open communication and comprehensive documentation—two pillars essential to Homebot's data governance strategy.

Centralizing these interactions allowed Homebot to unify their data knowledge, streamline communication, and, with DQS, measure the quantitative impact of these efforts. The integration of Questions into DQS provided clear, actionable metrics that reinforced the value of their centralized approach to managing data inquiries.

Building a solid foundation with Secoda’s Data Quality Score

The introduction of Secoda’s Data Quality Score (DQS) was transformative for Homebot, providing a clear threshold for documentation quality and streamlining data operations. DQS offered measurable, actionable insights, making it easier for the team to ensure their data models were consistently documented and met high standards. This prescriptive approach improved both the quality and efficiency of their documentation efforts. As DiRe noted, "For me as a manager, if I tell someone to go document something, they could take that in a million different ways. But to say we need to reach a certain threshold on the DQS makes things a lot easier."

Despite initial challenges due to ownership shifts and managing an increasing volume of data, DQS provided the structure Homebot needed to build more reliable data models. The system’s clear metrics, combined with well-managed data-related questions, helped the BI team overcome these obstacles and fully realize the value of Secoda’s platform. As DiRe emphasized, "Once we can really curate the data we want to be exposed to people in DQS, it just builds that base really solidly, where we don’t have to worry about anything."

In addition to building a strong data foundation, DQS brought significant efficiency gains to Homebot’s BI team. By reducing repetitive tasks and minimizing ticket volume through self-service tools, the team could focus on more strategic and impactful work.

DiRe shared, "The biggest ROI for us is preserving our resources, particularly time. DQS helps us streamline work, reducing the amount of ticket volume via self-service tools, and letting us focus on more proactive work that makes a bigger impact."

The direct link between the management of questions and DQS scoring reinforced the importance of centralizing data-related inquiries. This streamlined workflow supported Homebot’s goal of reducing ticket volume and fostering a more proactive work culture.

Looking ahead

As Homebot continues to refine its data management practices, Matthew and his team are excited about the future possibilities with Secoda. They are particularly looking forward to further integrating DQS with Secoda's AI capabilities, which will allow them to enhance the accuracy and relevance of their data models.

Reflecting on their journey with Secoda, Matthew emphasized the importance of starting small and building a strong foundation before expanding the use of new features like DQS. This approach has allowed Homebot to effectively manage the complexities of data documentation and ensure that their data models are of the highest quality.

Homebot’s experience as early adopters of Secoda’s Data Quality Score feature highlights the transformative power of quantifiable metrics in data management. By embracing DQS, Homebot not only improved the quality of their documentation but also enhanced the efficiency of their operations. This success underscores the value of their partnership with Secoda, empowering them to continuously refine their data management practices and drive future growth. As they continue to innovate and integrate new features, Homebot is well-positioned to lead the way in data quality and governance, setting a strong example for other organizations to follow.