Enterprise
Clorox
Built the data infrastructure and analytics behind a consumer-products portfolio.
Data engineering & analytics
Turn data into momentum.
We build your database and warehouse foundation and put dependable analytics on top of it, bringing business data together so your team can answer its own questions.
Founder-led consulting, from the first conversation through handoff.
The path
Services
From scattered data to a system your team can use.
- 01
Build the foundation
Database and warehouse setup, source integration, data modeling, and quality checks that bring business data together for reporting.
- 02
Put the analytics layer to work
Stand up your analytics tooling, define shared metrics and semantic models, configure access, and build reporting around the questions the business needs to answer.
- 03
Make it yours
Modernize legacy reporting, document the setup, train the team, and provide a clear handoff.
- 04
Make AI real on your data
Find the AI use cases worth building on your data, roadmap them, and stand up a first proof-of-concept — ask-your-data reporting, automated narratives, and anomaly alerts — on your existing Snowflake, Looker, and Omni stack, governed so every answer traces back to your defined metrics.
AI is only as good as the data behind it. A governed, well-modeled foundation is what turns AI from guesswork into answers — and we can carry it into your first AI use case when you're ready.
AI integration
How we bring AI into your data, without the hype.
AI only pays off when the data underneath it is trustworthy and the use case is real. This is the practical path we follow to get a first AI flow into production on your stack — and to leave your team able to run it.
- 01
Start with the data, not the model
We audit lineage, quality, access controls, and gaps first — to see whether the use case can actually be supported before anyone touches a model.
- 02
Choose the use case worth building
Not every problem needs AI. We weigh candidate use cases against data readiness and business value, and pick the one or two worth building first.
- 03
Build a governed proof-of-concept
We stand up a working prototype — ask-your-data reporting, automated narratives, or anomaly alerts — on your existing Snowflake, Looker, and Omni stack, with answers that trace back to defined metrics.
- 04
Evaluate it honestly
We measure the prototype against the manual process it replaces, document where it is reliable and where it is not, and decide what is worth scaling.
- 05
Hand off and enable your team
We document the pipeline, set the guardrails, and train your engineers to extend it — so AI becomes part of how your team works, not a black box.
BI migration
Move from Looker to Omni without losing what works.
A BI migration is not a re-pointing exercise — it is a chance to keep the definitions your business trusts and shed the ones that accumulated by accident. We move teams from Looker and other platforms to Omni with the semantic layer, governance, and reporting intact.
LookerOmni
Tableau · Power BI · ModeOmni
Preserve the semantic layer
We translate your LookML models, metrics, and dimensions into Omni modeling — so the definitions your business relies on carry over, not just the charts.
Rebuild dashboards with parity
We recreate the dashboards and reports people actually use and check the numbers against the old system before anyone switches.
Keep access and governance intact
We mirror row-level security, permissions, and embedded analytics so the move does not widen or break who can see what.
Cut over on your schedule
We run old and new in parallel, migrate team by team, and decommission the old platform only when parity and confidence are confirmed.
Hand off a documented system
We document the new models and train your team to extend them — so Omni is yours, not a consultant black box.
Approach
A clear path from setup to handoff.
- 01
Understand
Clarify business questions, source systems, users, and priorities.
- 02
Build
Set up the foundation and connect the analytics stack.
- 03
Validate
Check the data, metrics, access, and reporting with the team.
- 04
Enable
Document the system and help people use it confidently.
Every foundation we build is AI-ready — modeled and governed, so that when you add ask-your-data tools, automated reporting, or alerts, the answers stay trustworthy.
Experience
Hands-on experience, from infrastructure to insight.
Founded by Marty Hauck, Iznota brings more than a decade of experience building data infrastructure, analytics teams, and reporting systems. Marty's work spans enterprise and high-growth companies — helping build tech stacks and data systems for Clorox, Everlywell, and Theralogix, and taking newer brands from 0 to 1 during their pre-sales process.
Platforms
10+ years
- Looker
- Snowflake
- Fivetran
5+ years
- Omni Preferred vendor
- MotherDuck
Also fluent in dbt, SQL, and analytics modernization across legacy and modern stacks.
Selected work
Companies Marty has helped build tech stacks and data systems for, plus early-stage brands taken from 0 to 1.
Health
Everlywell
Stood up the tech stack and data systems for a high-growth at-home health-testing company.
Wellness
Theralogix
Shaped the data foundations and reporting for a science-led supplement brand.
Early stage
0 → 1 pre-sales
Took pre-launch brands from zero to their first tech stack and data systems.
Also worked with BrainMD, Relaxium, and other health and wellness brands. Selected experience of founder Marty Hauck, including work before Iznota.
About
Good engineering. Useful answers. People who care.
Marty Hauck founded Iznota to do the hands-on work: connecting business questions to dependable systems, and helping teams own what gets built. Every engagement is led by the person doing the engineering, from the first conversation through handoff.
The measure of good work here is practical. The data lines up, the definitions are shared, and people can answer their own questions without chasing a spreadsheet or waiting on an analyst.
Data that moves your business forward.
That's the moment everything we build is working toward.
Contact
What could better data help you do?
Tell us what you're trying to build, fix, or understand. We'll work through the next step together.