Snowflake 
BigQuery 
Postgres 
dbt 
Fivetran 
Looker Data platform
Bespoke software
The pain
Analysts write brittle custom scripts to pull data from disconnected silos.
What we build
A cloud-native warehouse that ingests, normalizes, and stores data automatically.
Services · Data intelligence & insight systems
Numbers your whole company can trust
Dashboards are worthless when no one trusts the figure. We build the warehouse, the semantic layer, and the observability that makes one number mean one thing everywhere.
Warehouse & ELT Semantic layer Observability
Built with
Snowflake BigQuery Postgres dbt Fivetran Looker
Where it hurts · what we build
Trust is the product. The dashboard is downstream.
BI & dashboards
Parallel system
The pain
End users cannot trust the numbers, so the dashboards go unused.
What we build
A semantic layer that defines core metrics identically across every visualization tool.
Governance & quality
Operational automation
The pain
Manual error checking scales poorly and misses problems before reports ship.
What we build
Software that monitors pipelines and flags anomalies, schema changes, and nulls before executives see them.
One trusted pipeline
Manual, messy ingestion becomes automated, tidy output .
For teams keying the same records by hand and second-guessing every figure that lands in a report.
Raw documents flow through extraction and a semantic layer , so what comes out is structured, governed, and means the same thing in every tool downstream.
Built with SnowflakedbtFivetranPostgres
Where the sector is heading
Data systems are shifting from storage to governance and observability.
Analytics spend · 2026
420 B
Investment in decision support is massive
Global spending on big data and analytics signals serious investment in decision infrastructure.
Source: IDC via Bismart, 2026
Governance · 2026
As code
Policy moves into the pipeline
Governance as code automates policy enforcement directly inside data pipelines.
Source: Bismart, 2026
Incident time · 2023
166 %
Data incidents take far longer to fix
Time to resolve a data quality incident jumped to 15 hours, demanding better monitoring.
Source: Monte Carlo, 2023
The cost of standing still
What poor data quality costs.
When departments define revenue differently, dashboards go unused and decisions stall. These figures describe the data and analytics sector, not Techtiz engagements—and what poor data quality costs.
$12.9 M
Average annual cost of poor data quality per organization
Gartner, 2026
47 %
New data records that contain at least one critical error at entry
MIT Sloan, 2026
3 %
Companies whose data meets basic quality standards
Harvard Business Review, 2026
31 %
Average share of revenue hurt by data quality failures
Monte Carlo, 2023
What we build
What every data build ships with.
Cloud-native warehouse
Snowflake, BigQuery, or Postgres with dbt-driven ELT, built to ingest and normalize automatically.
Semantic layer
Core business metrics defined once, so revenue means the same thing in every tool.
Automated observability
Anomalies, schema changes, and nulls are caught before they reach executive reports.
Data contracts
Formal contracts between software and analytics teams keep upstream changes from breaking reports.
For U.S. SLED prime contractors
Reporting and analytics layers, behind the prime.
For SLED scope under NAICS 518210, we build governed analytics layers as your subcontractor, with PII handled to standard, never facing the agency.
NAICS 518210 541512 541511
See SLED Subcontracting NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.
Parallel analytics layer. ELT runs without altering operational databases, so live applications stay fast.
Lineage you can show. Transparent data lineage, tested pipelines, and strict handling of personally identifiable information.
FAQ
Data intelligence, answered.
How do we ensure departments calculate revenue the same way?
We implement a semantic layer that centralizes the mathematical definition of core business metrics.
Will extracting data slow down our live application?
No. We use read-replicas or scheduled batch processing during off-peak hours to protect application performance.
How quickly can we spot a broken pipeline?
Automated observability tools alert the engineering team the moment a data anomaly or schema change is detected.
Who owns the data structure when systems change?
We establish formal data contracts so software teams and analytics teams share technical accountability.
Our data is a mess. Garbage in, garbage out, right?
Which is exactly why we start with data contracts and lineage tracing, so upstream schema changes do not silently break downstream reporting.
Start the conversation
Make every dashboard say the same thing
Tell us the metric two teams define differently. That is where the semantic layer starts.
Scope a data platform