MIBTY

DATA

Data analytics & mining

Modern data stacks — warehouses, models, dashboards, and the governance to keep them honest.

Book a consultationFrom raw logs to decisions executives trust.

The problem

Most companies have more data than insight. Dashboards proliferate; trust evaporates.

We build the spine: warehouse → modeling → governance → consumption — and we cut what doesn't earn its place.

Workflow

  1. 01

    Audit

    Where does data come from, which numbers do leaders trust, and which numbers are quietly contradicting each other?

  2. 02

    Warehouse + model

    ELT into a single warehouse, dbt models with tests, lineage from source to dashboard.

  3. 03

    Govern

    Definitions versioned, SLAs on freshness, access policies in code.

  4. 04

    Enable

    Self-serve analytics for the teams that need it — with guardrails, not chaos.

Benefits

  • One source of truth

    Metrics defined once, consumed everywhere. The CEO and the analyst see the same number for the same word.

  • Tested transformations

    dbt tests run on every model. Bad data fails the build, not the boardroom.

  • Cost-aware by design

    Warehouse cost monitored per query and per dashboard. No silent six-figure surprises.

Technologies

  • Snowflake / BigQuery / Postgres
  • dbt
  • Fivetran / Airbyte
  • Metabase / Hex / Lightdash
  • Dagster
  • Great Expectations
  • Cube
  • DuckDB

Industries served

  • Enterprise
  • Fintech
  • Government
  • Healthcare
  • Retail

Frequent questions

  • Do you replace existing BI?

    Rarely. Most of the time we strengthen the layer underneath — the existing dashboards become more trustworthy without anyone re-learning a tool.

  • What about ML on top of this?

    A clean warehouse is the prerequisite for useful ML. We do both, but in that order.