About the Author :
My name is Cory Woytasik, and I lead the Data Governance program for Flock Safety.
In my 20+ year career I’ve grown from being a Data Analyst, to a Data Architect Lead, to now Data Governance Lead. A role I truly love, as it connects the exceptional work of data teams with the needs of consumers and the organization, while also establishing processes and frameworks for scalability and optimal outcomes.
About Flock Safety :
Flock Safety is an all-in-one technology solution to eliminate crime and keep your community safe. Its intelligent platform combines the power of communities at scale – including cities, businesses, schools, and law enforcement agencies – to shape a safer future together. Flock Safety’s full-service, maintenance-free technology solution is trusted by more than 5,000 communities across the country to help solve and deter crime in the pursuit of safer communities for everyone.
To schedule a demo or learn more, visit www.flocksafety.com.
Behind Flock’s powerful mission is a fast-moving, ambitious team with an amazing “make it happen” culture. At Flock Safety, we don’t just build technology, we strive to make communities safer through innovating, including improving the way we work internally.
Practical Data Governance: Starting with First Principles
When we started thinking about our Data Governance initiative, it was clear we needed a plan that didn’t just make sense on paper but also delivered real, practical benefits people could see and feel. We knew that to make this work, we had to show how it could help everyone make better decisions and, ultimately, move the company forward.
The challenge
How do we build a framework that’s effective but doesn’t feel like a heavy-handed process? Something that delivers quick, impactful wins, builds trust, and strengthens relationships; not through a hard “sell” but through solid “results.”
We landed on Data Quality and Discovery as the best starting point. The benefits were obvious:
- Use the same set of agreed-upon, consistent data to make decisions.
- Ensure best practices and high-quality data are always used.
- Safeguard our decision-making process.
- Maximize ROI from our Data & Analytics team and overall data investment.
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Getting buy-in from the Analytics Engineers was a natural process. They were eager to collaborate with Data Governance to implement tools and practices that foster trust and confidence in monitoring the right data. We also recognize the critical need to enhance data discovery for our business partners, ensuring they have confidence that they’re accessing and working with accurate, reliable data.
The ROI: Trusted Data Pays Dividends
And here’s the magic: once people trust that we have implemented the right data quality measures, they’re far more likely to lean into the analytics and final outputs of our data warehouse. They know we’ve done A, B, C, and D to ensure accuracy and quality.
Trust, once established, pays dividends:
Engagement and data consumption skyrocket.
When people recognize how data solves their real-world challenges, they engage with it naturally and enthusiastically. Instead of feeling overwhelmed or disconnected, teams feel empowered to explore insights and integrate data into their decisions. Over time, this growing reliance on data amplifies its perceived value, encouraging more frequent and thoughtful usage, ultimately driving smarter business outcomes.
Collaboration becomes the norm.
Accessible and trustworthy data breaks down barriers between teams, creating a shared foundation for problem-solving. Instead of operating in silos, teams align around consistent metrics and insights to address challenges collectively. This fosters open communication and enables diverse perspectives to contribute to more innovative and well-rounded solutions.
And honestly, it’s just a pleasure to watch how far we can go together.
There’s something deeply rewarding about seeing what teams can achieve when they embrace data and collaboration. Whether it’s uncovering new opportunities, solving long standing problems, or streamlining processes, the progress feels transformative. Watching this growth unfold demonstrates the power of shared goals, clear insights, and a unified drive toward success.
.svg)
The alternative, when trust isn’t there, is a path no one wants to take. People might shrug and say: “I know how to work with source system data myself,” or “I can trust what I pull on my own.”
And that’s where things can go off the rails. Instead of leveraging the expertise and efficiency of a specialized data team, you can end up with rogue queries and sub-par products. Basic mistakes creep into reports, decisions are made based on faulty data, and those decisions can impact the company’s output, morale, and bottom line.
Bad data practices due to a lack of trust is a slippery slope no organization can afford.
To keep our big goals and decisions grounded in solid data, we take the work we do, and the trust we build, very seriously. We make sure everyone, from our leadership team to individual contributors, is aligned and reporting off the same accurate data sets.
Ultimately, trust enables us to establish and follow best practices, leading to better outcomes across the board.
Choosing the Right Tool to Build Trust and Drive Adoption
When we evaluated tools, it was clear any solution we chose had to:
- Be intuitive and easy for the Analytics team to adopt, aligning with the needs of both data engineers and data consumers identified during our data discovery tool selection process. This includes capabilities like schema exploration, column-level lineage, data quality alerting, and seamless integration with our existing data stack.
- Ensure alerts are actionable and tailored to the needs of data consumers and engineers, highlighting specific issues with clear context and links to relevant tables, views, or processes. Include options for custom alerts and seamless integration with existing workflows like Jira for task creation.
- Enable business users to quickly locate data elements by name, tag, owner, or definition. Support intuitive exploration of schemas, data types, tests, and table definitions, helping non-technical users confidently navigate the data ecosystem.
- Foster trust by openly sharing data quality metrics, including test failures, freshness indicators, and lineage visualizations. For data consumers, transparency into “data quality dirty laundry” demonstrates accountability and ensures confidence in the team’s commitment to continuous improvement.
Elementary checked those boxes and then some!
As a dbt-native tool, it was an easy win for our team. Flock Safety is a forward-thinking company, and dbt is at the core of our transformation stack. Elementary’s simplicity perfectly matched the lean, efficient approach our team values.
And let’s talk about Elementary’s data catalog. It’s a cloud-only option, and it sealed the deal for us. It gives our data producers and analytics engineers instant visibility into the impact of their changes.
With Elementary, we’re testing, tagging, and producing a ton of high-quality metadata identifiers, everything we need to establish that golden standard of trust.
There’s so much more to say (stay tuned for another blog post!), but the bottom line is:
Trust drives everything. When trust is in place, the possibilities are endless.
Contributors
About the Author :
My name is Cory Woytasik, and I lead the Data Governance program for Flock Safety.
In my 20+ year career I’ve grown from being a Data Analyst, to a Data Architect Lead, to now Data Governance Lead. A role I truly love, as it connects the exceptional work of data teams with the needs of consumers and the organization, while also establishing processes and frameworks for scalability and optimal outcomes.
About Flock Safety :
Flock Safety is an all-in-one technology solution to eliminate crime and keep your community safe. Its intelligent platform combines the power of communities at scale – including cities, businesses, schools, and law enforcement agencies – to shape a safer future together. Flock Safety’s full-service, maintenance-free technology solution is trusted by more than 5,000 communities across the country to help solve and deter crime in the pursuit of safer communities for everyone.
To schedule a demo or learn more, visit www.flocksafety.com.
Behind Flock’s powerful mission is a fast-moving, ambitious team with an amazing “make it happen” culture. At Flock Safety, we don’t just build technology, we strive to make communities safer through innovating, including improving the way we work internally.
Practical Data Governance: Starting with First Principles
When we started thinking about our Data Governance initiative, it was clear we needed a plan that didn’t just make sense on paper but also delivered real, practical benefits people could see and feel. We knew that to make this work, we had to show how it could help everyone make better decisions and, ultimately, move the company forward.
The challenge
How do we build a framework that’s effective but doesn’t feel like a heavy-handed process? Something that delivers quick, impactful wins, builds trust, and strengthens relationships; not through a hard “sell” but through solid “results.”
We landed on Data Quality and Discovery as the best starting point. The benefits were obvious:
- Use the same set of agreed-upon, consistent data to make decisions.
- Ensure best practices and high-quality data are always used.
- Safeguard our decision-making process.
- Maximize ROI from our Data & Analytics team and overall data investment.
Getting buy-in from the Analytics Engineers was a natural process. They were eager to collaborate with Data Governance to implement tools and practices that foster trust and confidence in monitoring the right data. We also recognize the critical need to enhance data discovery for our business partners, ensuring they have confidence that they’re accessing and working with accurate, reliable data.
The ROI: Trusted Data Pays Dividends
And here’s the magic: once people trust that we have implemented the right data quality measures, they’re far more likely to lean into the analytics and final outputs of our data warehouse. They know we’ve done A, B, C, and D to ensure accuracy and quality.
Trust, once established, pays dividends:
Engagement and data consumption skyrocket.
When people recognize how data solves their real-world challenges, they engage with it naturally and enthusiastically. Instead of feeling overwhelmed or disconnected, teams feel empowered to explore insights and integrate data into their decisions. Over time, this growing reliance on data amplifies its perceived value, encouraging more frequent and thoughtful usage, ultimately driving smarter business outcomes.
Collaboration becomes the norm.
Accessible and trustworthy data breaks down barriers between teams, creating a shared foundation for problem-solving. Instead of operating in silos, teams align around consistent metrics and insights to address challenges collectively. This fosters open communication and enables diverse perspectives to contribute to more innovative and well-rounded solutions.
And honestly, it’s just a pleasure to watch how far we can go together.
There’s something deeply rewarding about seeing what teams can achieve when they embrace data and collaboration. Whether it’s uncovering new opportunities, solving long standing problems, or streamlining processes, the progress feels transformative. Watching this growth unfold demonstrates the power of shared goals, clear insights, and a unified drive toward success.
The alternative, when trust isn’t there, is a path no one wants to take. People might shrug and say: “I know how to work with source system data myself,” or “I can trust what I pull on my own.”
And that’s where things can go off the rails. Instead of leveraging the expertise and efficiency of a specialized data team, you can end up with rogue queries and sub-par products. Basic mistakes creep into reports, decisions are made based on faulty data, and those decisions can impact the company’s output, morale, and bottom line.
Bad data practices due to a lack of trust is a slippery slope no organization can afford.
To keep our big goals and decisions grounded in solid data, we take the work we do, and the trust we build, very seriously. We make sure everyone, from our leadership team to individual contributors, is aligned and reporting off the same accurate data sets.
Ultimately, trust enables us to establish and follow best practices, leading to better outcomes across the board.
Choosing the Right Tool to Build Trust and Drive Adoption
When we evaluated tools, it was clear any solution we chose had to:
- Be intuitive and easy for the Analytics team to adopt, aligning with the needs of both data engineers and data consumers identified during our data discovery tool selection process. This includes capabilities like schema exploration, column-level lineage, data quality alerting, and seamless integration with our existing data stack.
- Ensure alerts are actionable and tailored to the needs of data consumers and engineers, highlighting specific issues with clear context and links to relevant tables, views, or processes. Include options for custom alerts and seamless integration with existing workflows like Jira for task creation.
- Enable business users to quickly locate data elements by name, tag, owner, or definition. Support intuitive exploration of schemas, data types, tests, and table definitions, helping non-technical users confidently navigate the data ecosystem.
- Foster trust by openly sharing data quality metrics, including test failures, freshness indicators, and lineage visualizations. For data consumers, transparency into “data quality dirty laundry” demonstrates accountability and ensures confidence in the team’s commitment to continuous improvement.
Elementary checked those boxes and then some!
As a dbt-native tool, it was an easy win for our team. Flock Safety is a forward-thinking company, and dbt is at the core of our transformation stack. Elementary’s simplicity perfectly matched the lean, efficient approach our team values.
And let’s talk about Elementary’s data catalog. It’s a cloud-only option, and it sealed the deal for us. It gives our data producers and analytics engineers instant visibility into the impact of their changes.
With Elementary, we’re testing, tagging, and producing a ton of high-quality metadata identifiers, everything we need to establish that golden standard of trust.
There’s so much more to say (stay tuned for another blog post!), but the bottom line is:
Trust drives everything. When trust is in place, the possibilities are endless.
