Self-service tools are everywhere right now — and the goal sounds great: give more people access to data, and you’ll get better decisions across the org.
But that’s NOT what happens.
IRL: trust breaks down, dashboards multiply, and no one’s quite sure which numbers to believe.
We’re digging in today: What does good self-service look like, and what must change for it to work?
We’re also talking about what happens when data teams get the space to lead (not just react), how supply chain teams are rethinking visibility, and a couple interesting tech updates.
Below, you'll find:
This Month's Recommended Read
Data Signals: Hot Topics
Emerging Tech Insights
Busting Data Myths
LOL Moment
Let's get to it!
P.S. Going to the upcoming Databricks summit in San Fran? We’ll be there too and would love to catch up!
If your dashboards are multiplying but trust in the data keeps shrinking, it’s not a tooling issue — it’s a strategy one. This blog cuts through the noise and gets real about what it takes to make self-service analytics work.
💡Inside the blog:
What effective self-service looks like — and how to recognize when it's working
The four failure modes that quietly derail self-service (it's not just user error)
Step-by-step fixes: from governance to training to AI-driven tools and designated oversight
“When access comes without structure, all you get is faster chaos!” – Pat Ross, Sales Engineer
📡 Data Signals: Hot Topic
1. Data teams are more effective (and happier) with a supply vs demand mentality 🔄
A business-first data team that operates with a “demand” approach will eventually turn data professionals into task-takers who just respond to requests. The most effective data teams will flip that model to a “supply” approach: surfacing trends, sharing ideas, and bringing new insights to the business before they’re asked for.
Food for thought:When data teams have room to explore, experiment, and create solutions (not just deliver prescribed columns) the results are more impactful and a whole lot more interesting.
2. 📦 Supply chain teams want answers — not just alerts
With global logistics in a shaky state, companies are under pressure to track everything — from parts in transit to real-time bottlenecks. How do they get there? It’s not just ‘more data’, and it’s not dashboards that show what already went wrong. They need smarter infrastructure to make sense of it.
That’s why we’re seeing growing demand for solutions that combine streaming IoT data, EDI ingest, and AI-powered insight.
It's a clear signal: logistics teams aren’t just reacting anymore. They're hunting for proactive, data-driven solutions — and fast.
It’s the kind of messy, cross-functional problem we help clients solve all the time — bringing structure to scattered systems and clarity to what’s happening on the ground.
Emerging Tech Insights
A few tech updates on our radar:
🛠️ Databricks Just Made Dev and Ingest Easier Two new updates — one for building, one for ingesting — are aimed at cutting the busywork and making collaboration smoother across your data team.
1. Build and Test Asset Bundles Right in the Workspace
Databricks Asset Bundles can now be created and edited directly in the workspace UI — no local setup required. This new public preview removes the pain of configuring environments just to collaborate or test changes.
Why it matters: Teams can now iterate, commit, and deploy bundles without bouncing between terminals and Git tools. It’s a much cleaner way to manage shared workflows — especially when speed and cross-team collaboration matter.
Beware: It’s still in preview, but this update makes Asset Bundles way more accessible for real-time edits and testing — right where the work happens.
2. Lakeflow Connect Brings Managed Ingestion to More Sources
Databricks also rolled out Lakeflow Connect — a growing set of managed connectors for ingesting data from SaaS apps and databases (like Salesforce, Workday, and SQL Server). Built-in support for schema drift, incremental loads, and SCD Type 2 makes this ideal for real-time, governed pipelines.
Why it matters: Data lands cleanly in the Bronze layer, with Unity Catalog governance and serverless performance baked in.
Bonus: Catch Us IRL
We’ll be at the Databricks Data + AI Summit. We’ve heard whispers of big announcements, and we’re ready to dig in.
Attending? Book a 1:1 planning session with our CTO, Patrick Vinton, to build a custom agenda that fits your goals.
Not going? No worries — we’ll share our top takeaways post-summit on LinkedIn.
Busting Data Myths
🛑 Myth: "Once our data is centralized and metrics are aligned, decision-making gets easy."
Reality: It’s a nice idea — but not quite reality. As our CDO Tony Dahlager put it:
“Even with all the data centralized, metrics defined consistently, and clever dashboards provided, you'll still struggle to come to confident conclusions in analysis.”
Why? Because every business process change — even small ones — reshapes the meaning of your data. A tweak to lead lifecycle stages, a new opportunity type, or a quiet automation update can ripple through your metrics without warning. (ask Tony how he knows 😬)
Data reflects reality imperfectly. Without context and communication, it’s easy to misread what the numbers are telling you.
LOL Moment
One does not simply become a brilliant data scientist!
First, you must survive:
300 Slack threads about what “active user” means
One existential crisis over model drift
And at least three dashboards no one asked for
And then — maybe — you get invited to the strategy meeting. 🧙♂️
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 ;)
