From data chaos to clarity.
Organizations are drowning in data but starved for insight. Spreadsheets, siloed databases, and inconsistent reporting block decision-making. AI projects fail without the right foundations. We help clients build a reliable, governed data layer and apply AI where it drives outcomes.
- Fragmented systems and manual reporting
- No single source of truth
- Data quality & governance issues
- AI experiments that don’t scale or deliver ROI
Our
Approach
Modern Data Platforms
Cloud-native data lakes and warehouses (Azure Synapse, AWS Redshift, GCP BigQuery) with governance at the core.
AI & Machine Learning
Predictive models, natural language insights, computer vision, and generative AI using Azure ML, OpenAI, or TensorFlow.
Business Intelligence & Visualization
Real-time dashboards with Power BI, Tableau, or Looker to unlock self-service insights.
What You Get
- Data strategy & maturity assessment
- Data warehouse/lakehouse setup (Azure Synapse/Fabric, Redshift or BigQuery)
- ETL/ELT pipelines with Azure Data Factory, SSIS, or dbt
- AI/ML model development, training, and deployment
- Power BI dashboards & reports with row-level security
- Data governance frameworks, lineage, and catalogs (Purview, Collibra)
- Advanced analytics (forecasting, churn, anomaly detection)
- Enablement & handover documentation
Key Metrics of Data & AI Success
From faster insights to AI-driven growth, these are the results our clients achieve when data becomes a strategic asset.
90%
Faster time-to-insight with automated pipelines
70%
Reduction in manual data prep effort
2–4 weeks
AI models deployed into production
40%
Increase in reporting accuracy & trust
25%
Cost savings from optimized data storage & compute
3x
More business units using self-service BI
AI-Powered Forecasting for Growth
A retail client implemented Azure Synapse, Power BI, and custom ML models, cutting reporting time by 80% and improving demand forecasts by 30%.
Read Case StudyFrequently asked questions
Here are some common questions about our company.
Azure (Synapse, ML, Purview, Fabric, Power BI), AWS (Redshift, SageMaker), and GCP (BigQuery, Vertex AI), with hybrid integration options.
We are Microsoft specialists but also integrate open-source (Spark, dbt, TensorFlow) and multi-cloud tools where it best serves the client.
Through governance frameworks, automated data quality checks, lineage tracking, and stewardship policies.
Yes. We align AI investments to clear business cases — from customer engagement to fraud detection — ensuring ROI and adoption.
We embed compliance into the data platform design: encryption, retention, audit logs, data subject rights, and access controls.
Dashboards can be live in 4–6 weeks. AI/ML use cases typically deliver measurable outcomes in 8–12 weeks.
Yes — from Power BI self-service workshops to MLOps enablement, we ensure your team can sustain and scale solutions.
We apply responsible AI frameworks (Fairness, Transparency, Explainability) and monitor models post-deployment for drift and bias.
Yes. We connect Dynamics, SAP, Salesforce, and others into the data platform for unified reporting and analytics.
Yes. We can co-manage pipelines, BI platforms, and AI models, with SLA-backed support and continuous improvements.
Ready to make data your competitive advantage?
Partner with Heights Insights to modernize your data platform, unlock AI, and put trusted insights in every decision.