Turn raw data into decisions you can trust.
Business intelligence, data engineering and predictive analytics that give every team one reliable view of the truth.
What are data analytics services?
Data analytics services turn raw data into decisions. GloberixAI builds data pipelines, cloud warehouses, business intelligence dashboards in tools like Tableau, Power BI and Looker, and predictive models, so every team works from one trusted source of truth.
- Deliverables
- Pipelines, warehouse, dashboards, predictive models
- Tools
- Snowflake, BigQuery, Airflow, dbt, Tableau, Power BI
- Timeline
- 6 to 10 weeks for focused projects, 12 to 20 for a platform
- Governance
- Access control, lineage and privacy by design
Last updated
The full data stack, from source to insight.
We build the foundations and the finished views, so your numbers are trusted and your people can use them.
Business intelligence dashboards
Executive-ready dashboards in Tableau, Power BI or Looker with real-time KPIs, drill-down and mobile views.
- Real-time KPI monitoring
- Self-serve exploration
- Role-based views
Data engineering and pipelines
Fault-tolerant ETL and ELT pipelines that move data from every source into one trusted warehouse.
- Idempotent, monitored pipelines
- Batch and streaming
- Automated data-quality tests
Predictive analytics and ML
Forecasting, churn and risk models trained on your history and deployed into the workflow where decisions happen.
- Demand forecasting
- Churn and risk scoring
- Accuracy tracked over time
Text and language analytics
NLP pipelines turn reviews, support tickets and social posts into sentiment, topics and intent you can act on.
- Sentiment and topic mining
- Entity and intent extraction
- Alerts on emerging issues
Data governance and quality
Lineage, access control, PII detection and cataloging, designed with GDPR, CCPA and HIPAA requirements in mind.
- Lineage and ownership
- Role-based access
- Automated PII detection
Data platform architecture
A warehouse or lakehouse blueprint on the cloud you already use, sized to your needs and your budget.
- Cloud cost planning
- Warehouse and lakehouse design
- Migration roadmaps
Climb from what happened to what to do next.
Most teams start at step one. We build the foundations that make each next step possible.
Descriptive
What happened?
Trusted dashboards and reports built on one agreed definition of every metric.
Diagnostic
Why did it happen?
Drill-downs, cohort analysis and root-cause views that explain movements in your numbers.
Predictive
What will happen?
Forecasts and risk scores that look ahead, with accuracy tracked and reported over time.
Prescriptive
What should we do?
Recommendations and automated actions that turn predictions into decisions at scale.
Analytics built around your industry.
The questions differ by sector. So do the models and the dashboards.
Finance and fintech
Fraud detection, credit risk scoring and trading signal analysis that surface problems earlier and reduce false alarms for your analysts.
- Fraud and anomaly detection
- Credit and risk scoring
- Regulatory reporting
Healthcare
Patient outcome prediction, operational capacity forecasting and clinical analytics designed with privacy and consent in mind.
- Outcome prediction
- Bed and staff forecasting
- Privacy-first design
Retail and e-commerce
Recommendation engines, dynamic pricing, demand forecasting and customer lifetime value models that lift margin and loyalty.
- Personalization
- Price optimization
- Demand forecasting
Manufacturing
Predictive maintenance, quality anomaly detection and yield optimization built on sensor and machine data.
- Predictive maintenance
- Quality monitoring
- Yield optimization
Logistics
Route, demand and capacity forecasting with live visibility into delays, costs and service levels.
- Demand and capacity planning
- Delay prediction
- Cost-to-serve analytics
Tools chosen for your data, not our habits.
- BI
- Tableau, Power BI, Looker, Metabase
- Warehouses
- Snowflake, BigQuery, Redshift, Databricks
- Pipelines
- Airflow, dbt, Kafka, Fivetran, Spark
- Machine learning
- Python, scikit-learn, PyTorch, Prophet
- Governance
- Data catalog, Lineage, Great Expectations, Access control
- Cloud
- AWS, Microsoft Azure, Google Cloud
How we deliver insight.
From a clear picture of today to a platform your team can run.
Audit
We review your data sources, quality issues, gaps and opportunities, and agree the questions that matter most.
Architect
We design a platform blueprint and choose the right mix of cloud services for your goals and budget.
Build
We build tested ETL and ELT pipelines, models and dashboards, with monitoring from the first release.
Operate
We monitor, tune and extend the platform, and train your team to get more from it.
Analytics that people actually open.
Good analytics is as much about adoption as it is about engineering.
One source of truth
We align teams on shared metric definitions so meetings stop starting with arguments about the numbers.
Business questions first
Every dashboard answers a real decision, so there is no wall of charts nobody uses.
Quality is tested
Automated tests catch broken pipelines and bad data before they reach a report.
Governed by design
Access, lineage and privacy controls are planned from the start rather than added after an audit.
Self-serve for your team
Clean models and documentation let analysts answer their own questions without waiting on engineers.
Documented and handed over
You receive clear documentation and training, and keep full ownership of everything we build.
Data analytics services that turn information into an advantage.
GloberixAI is a data analytics and engineering company that helps organizations build reliable data platforms, business intelligence dashboards and predictive models. We start by understanding the decisions your teams need to make, then work backwards to the data, pipelines and reports that support them.
On the engineering side we design fault-tolerant ETL and ELT pipelines with tools such as Airflow, dbt and Kafka, and land clean, documented data in a warehouse like Snowflake, BigQuery or Redshift. On the analytics side we build dashboards in Tableau, Power BI or Looker, forecasting and risk models in Python, and language analytics that mine reviews, tickets and social conversations for patterns.
Governance runs through everything: role-based access, lineage, automated checks and privacy controls that help you meet GDPR, CCPA and HIPAA requirements. Tell us which decisions you wish you could make with more confidence and we will reply with a clear plan, a realistic timeline and a quote.
Data analytics, answered.
What is big data analytics and how can it help my business?
It means collecting data from many sources, cleaning it and analyzing it at scale to find patterns that guide decisions. Typical results are better forecasts, lower costs, faster reporting and earlier warning of risks and opportunities.
How long does a data analytics project take?
A focused predictive analytics project often takes 6 to 10 weeks from discovery to deployment, while a full data platform build typically takes 12 to 20 weeks depending on complexity and the number of sources.
How do you keep our data secure and compliant?
We use encryption, role-based access, audit trails and private network setups, and work with your compliance team toward GDPR, CCPA and HIPAA requirements. Sensitive fields can be masked or removed before analysis.
Which BI tool should we choose: Tableau, Power BI or Looker?
It depends on your existing stack, budget and users. Power BI fits Microsoft-centered teams, Looker fits teams with a strong modeling layer, and Tableau excels at visual exploration. We recommend one after a short assessment.
Can you work with our existing data warehouse?
Yes. We regularly build on Snowflake, BigQuery, Redshift, Databricks and traditional databases, and only recommend a migration when it clearly pays off.
Do you build real-time dashboards?
Yes. With streaming tools such as Kafka and a suitable warehouse, we can deliver near-real-time views. We also tell you honestly when daily or hourly refresh is enough and cheaper.
Who owns the models, pipelines and dashboards?
You do. All code, models, dashboards and documentation are delivered to accounts and repositories you control.
Which decision do you wish you could make with more confidence?
Tell us and we will reply with a clear data plan, a realistic timeline and a quote.