Mat Hughes
Analytics Practitioner · Thinking about replacing Tableau modernizing Alteryx escaping vendor lock-in decision-centric tools AI without the hype escaping the dashboard trap why we still export to Excel dashboards that do something the insight-to-action gap tech stack rationalization why PBI isn't the answer Tableau governance white-labeled analytics AI-assisted analysis automated actions enabling rather than replacing breaking free from read-only what's next rethinking self-service AI that amplifies people (no really) aligning metrics across the org (no really) making analytics human (no really) being data-driven (no really) a single source of truth (no really) activating data™ hydrating the ontology™ democratizing insights™ leveraging data assets™ maturing data culture™ operationalizing metrics™ unlocking data value™ synergizing the semantic layer™ becoming insights-led™ accelerating time-to-insight™
I spent a decade at InterWorks working with Fortune 500 companies on why their analytics investments weren't paying off. That meant evaluating platforms, building training programs for hundreds of practitioners, and designing embedded analytics systems that people actually want to use.
These days I'm writing about what comes after the dashboard era, and about the AI question that's tangled up in it. Most organizations are adopting AI in ways that impress their boards but don't actually help their people. I'm interested in the alternative: practical AI that amplifies human judgment rather than replacing it. Still working through what that looks like.
What I'm Interested In
Human-Centered AI
Not the hype. The gap between AI demos and AI that actually helps people do their jobs. I'm interested in use case evaluation, practical implementation, and knowing when not to automate.
Use cases · Practical implementation · Augmentation over replacementEmbedded Analytics
Multi-tenant, white-labeled, RLS-secured. The hard stuff that looks easy in demos but breaks in production.
Multi-tenant · RLS · White-label · ISVLearning at Scale
AI transformation requires building data and AI competencies faster than traditional L&D can deliver. Personalized pathways, learning analytics that reveal what's actually landing, and measurement frameworks that tie training to outcomes.
Learning Analytics · LMS/LMX · Competency MeasurementPlatform Migrations
Moving off legacy BI? The real challenge isn't the technology—it's building the business case, managing license costs, and bringing users along. Doing more with less without losing capability.
Tableau → Sigma · On-prem modernization · Alteryx modernizationExperience
InterWorks
2014 – 2025
11 years
4 leadership roles
500+ practitioners trained
Fortune 500 clients
Product Architecture Lead
Designed BI 3.0 platform assessment strategies for Fortune 500 organizations modernizing to cloud-native analytics. AI strategy work including use case evaluation, solution design, and implementation roadmaps.
Director of Solutions Architecture
Built solutions architecture team and delivery systems. Developed approaches for emerging analytics technology partners.
Analytics Practice Lead
Led teams of architects and analysts. Designed Analytics Pathways Program—competency-based progression model.
Earlier Roles
Analytics Team Lead → Regional Practice Lead → Team Lead (Platforms). Founded the practice specializing in Tableau Server & Enterprise Self-Service.
Tableau · Sigma · dbt · Alteryx · Snowflake · Databricks · Azure · AWS
What's Next
The question I keep coming back to: how do organizations adopt AI in ways that make their people more effective, rather than just cutting costs or chasing trends? Not anti-AI—but pro-human. I'm working through that here.
If you're thinking about the same questions, I'd like to hear from you.
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