Just a surprising insight: librarians might hold the key to your next big data breakthrough.
This month, we're diving into why Library & Information Science is making waves in data management circles — and how borrowing from their playbook can simplify your data governance strategy.
Plus, we've got the usual mix of advice for quick wins, myth-busting, and memes — because data should never be dull.
Frustrated by overly complex data governance that fails to deliver? A data governance strategy doesn’t need to be rigid — it should balance structure with flexibility.
The real key to seeing ROI on your governance efforts is designing a program that matches the needs of your organization… do more than that, and you’ll scare people away.
💡Inside the blog:
Data Governance Defined: Understand what a practical, results-driven data governance strategy looks like — and why you need one.
Tailoring Governance to Your Business: Tips to build a flexible yet structured approach that aligns governance with your unique organizational needs.
Step-by-Step Guides: Practical guidance for taking an enterprise-wide or iterative approach to data governance, including clear actions, quick wins, and ways to demonstrate ROI.
📡 Data Signals: Hot Topic
Librarians Can Teach Us a Lot About Data Governance
You might not have thought about the similarities between librarians and data pros, but Library & Information Science (LIS) principles are reshaping the way companies approach data governance. Librarians have long mastered categorizing, managing metadata, and making vast collections easy to navigate — exactly the challenges data teams face today.
Applying LIS methods helps you:
Simplify data discovery through structured, thoughtful categorization.
Boost governance effectiveness with clear metadata and controlled vocabularies.
Ensure data consistency across teams by using standardized definitions and frameworks.
Think of it as turning your data into a well-organized library: clear, accessible, and far easier to manage.
🛠️ Takeaway from the Field
Use Quick Wins to Build Trust
(Especially when stakeholders aren't technical)
Sometimes, technical value can get lost in translation with non-technical stakeholders. On a recent project, here’s what our team did to show quick value and demonstrate that technical work was directly benefitting the business:
🤑 Optimize Expensive Processes: Converted heavy dbt models to incremental updates, cutting hourly dbt run times by 37% and Snowflake costs by 14.5% within two weeks.
🧽 Clean Up Unused Assets: Removed outdated schemas and tables in Snowflake, providing immediate clarity and tidiness.
💨 Automate Metadata Insights: Developed a custom Python script to identify unused and outdated Mode reports, streamlining their reporting environment.
📝 Document Consistently: Regularly documented and shared findings and recommendations, keeping stakeholders visibly engaged, even when decisions had to wait.
Bottom line: Quick wins like these demonstrate clear, immediate value — especially crucial when stakeholders are non-technical or focused on the bottom line. Tangible improvements, even small ones, can quickly build trust and credibility for longer-term engagements.
Emerging Tech Insights
A few tech updates on our radar:
1. 🧠 OpenAI's Response API: Big News for Agentic AI
OpenAI just dropped a major update: the new Response API makes it easier than ever to build smart, autonomous AI agents. This release simplifies how developers create AI systems that can independently complete tasks, reason through problems, and interact with multiple tools and data sources seamlessly. Expect the rest of the market to follow closely, rolling out similar capabilities soon — making agentic AI accessible across your favorite data platforms.
Why it matters: Building AI agents is about to get quicker, easier, and more integrated — opening up exciting opportunities to automate, streamline, and enhance your data workflows.
2. 🚀Sigma Data Apps: Finally, a Way Out of SaaS Sprawl
Enterprise SaaS solutions can solve pointed problems, but they often leave you with siloed data, rising costs, and bloated workflows. Sigma’s new Data Apps flip that model — letting teams build and use real-time apps directly in their cloud data warehouse, no extra integration tools or complicated workflows required.
The payoff: faster decisions, fewer silos, and full control over your data all in one place. It’s in contrast to what we’re seeing across much of the SaaS industry landscape — everyone else is focused on AI getting packed into their individual product roadmaps but are still departmentally deployed software solutions with little-to-no strategy to avoid data silos.
Data governance as an AI-enabler remains a focus across recent product updates, with vendors rolling out new features aimed at better metadata management, improved consistency, and scalable governance.
Among them, Hex introduced Semantic Model Sync, enhancing governance and consistency for different types of data consumers on their platform (a timely update as we recently became a Hex partner). Coalesce announced its acquisition of CastorDoc and Coalesce Catalog, reinforcing a broader strategy than their original scope of data transformation to be at the center of structured, scalable data governance and management for organizations.
As AI becomes more embedded in modern data tools, the role of data governance is shifting from compliance to making analytics more reliable and accessible — regardless of how you choose to create and consume information.
Busting Data Myths
🛑 Myth: "A Well-Organized Data Warehouse is Enough for AI."
Reality: AI demands more than neatly stacked data. Without robust metadata, semantic clarity, and purposeful data modeling, your AI solutions risk delivering shallow insights — or worse, misleading answers.
💡 Takeaway: Don't just aim for tidy; aim for meaningful. Build out metadata and modeling practices that transform organized data into genuinely powerful AI-driven insights.
LOL Moment
"There are two types of people in this world: Those who can extrapolate from incomplete data..."
...and those who keep waiting for the rest of this sentence.
If you laughed, congrats — you're officially a data person. 🤓😹
Have any good data memes or jokes to share with the group? Send them our way – if you get featured, we’ll send you some swag ;)
