Data Science & Analytics
AI projects stall on data far more often than on models. Fields that mean different things in different systems, a nightly job nobody owns, three definitions of "active customer". We build the layer underneath: ingestion, a warehouse with agreed definitions, quality tests that fail loudly, and reporting people trust enough to argue with. It is the least exciting part of the work and the part that determines whether anything else succeeds.
How we work on this
- Data audit — what you have, what it costs you, what it blocks
- Warehouse build — modelled, tested, documented
- Pipeline modernisation — replacing the jobs nobody wants to touch
- Analytics enablement — so your team stops queueing for reports
Our Capabilities
Comprehensive Data Science capabilities backed by deep expertise and proven methodologies.
Technologies We Use
We leverage industry-leading tools and frameworks to build robust, scalable solutions.
What we would tell you in the first meeting
Before any AI proposal, ask one question: could we answer this with a SQL query if the data were clean? Often the answer is yes, and the honest recommendation is to fix the data and skip the model entirely.
Data Science — Frequently Asked Questions
What does a Glomax data science engagement look like end-to-end?+
We follow a six-phase process: data audit → architecture design → pipeline development (ETL/ELT with dbt, Airflow) → analytics layer (Snowflake, Databricks) → visualisation (Tableau, Power BI, custom D3.js) → governance and continuous optimisation. Each phase produces a working deliverable reviewed with your team.
Can Glomax migrate our legacy BI tools to a modern cloud data stack?+
Yes. We migrate enterprises from on-premise Oracle and Cognos setups to modern stacks like Snowflake + dbt + Looker or Databricks + Power BI. Migrations are zero-downtime with parallel runs until data quality is validated at the destination.
How do you handle data security and privacy compliance?+
We implement data encryption at rest and in transit, role-based access control, column-level masking of PII, and data lineage tracking via open catalogs. We are experienced with GDPR, India PDPB, HIPAA, and PCI-DSS requirements and integrate compliance checks directly into pipeline quality gates.
What is the difference between descriptive BI and predictive analytics?+
Descriptive BI (dashboards, reports) tells you what happened. Predictive analytics uses statistical models and machine learning to tell you what will happen — demand forecasts, churn probabilities, fraud scores, and dynamic pricing. Glomax delivers both within a unified data platform so one feeds the other.
Products built on this work
Software we run in production, not a slide about someone else's project.
All productsRelated Services
Ready to Get Started with Data Science?
Tell us what you are trying to build and what is in your way. We will tell you how we would approach it — including when the answer is that you do not need us.
Talk to an engineer