Turn business data into trusted intelligence
From data foundations and engineering to analytics and AI readiness.
About service
Data & Analytics
GoPomelo helps organizations modernize data platforms, connect fragmented data, build analytics and BI capabilities, and create AI-ready foundations for faster, more confident decisions.
GoPomelo helps organizations modernize data platforms, connect fragmented data, build analytics and BI capabilities, and create AI-ready foundations for faster, more confident decisions.
The problem
Buyer
triggers
Your data is spread across systems and difficult to trust.
Your data is spread across systems and difficult to trust.
Reports take too long to create and are not consistent across teams.
Reports take too long to create and are not consistent across teams.
You want to use AI or machine learning but need stronger data foundations.
You want to use AI or machine learning but need stronger data foundations.
Leaders need better visibility into performance, operations, customers, or risk.
Leaders need better visibility into performance, operations, customers, or risk.
The outcome
Business
outcomes
Create a reliable data foundation that teams can trust.
Create a reliable data foundation that teams can trust.
Improve decision-making with dashboards, analytics, and self-service insights.
Improve decision-making with dashboards, analytics, and self-service insights.
Reduce reporting effort through automation and better data pipelines.
Reduce reporting effort through automation and better data pipelines.
Prepare data for machine learning, AI, and advanced analytics use cases.
Prepare data for machine learning, AI, and advanced analytics use cases.
Service areas
Why modernize data and analytics with GoPomelo?
Data Platform Modernization
Design and build modern data platforms that support scalable analytics and future AI use cases.
What’s included
Current data landscape assessment
Target architecture and migration plan
Platform build and governance setup
Data Platform Modernization
Design and build modern data platforms that support scalable analytics and future AI use cases.
What’s included
Current data landscape assessment
Target architecture and migration plan
Platform build and governance setup
Data Platform Modernization
Design and build modern data platforms that support scalable analytics and future AI use cases.
What’s included
Current data landscape assessment
Target architecture and migration plan
Platform build and governance setup
Data Engineering
Ingest, transform, model, and manage data from different applications, systems, and sources.
What’s included
Data ingestion and pipeline design
Transformation, modeling, and quality checks
Source integration and automation
Data Engineering
Ingest, transform, model, and manage data from different applications, systems, and sources.
What’s included
Data ingestion and pipeline design
Transformation, modeling, and quality checks
Source integration and automation
Data Engineering
Ingest, transform, model, and manage data from different applications, systems, and sources.
What’s included
Data ingestion and pipeline design
Transformation, modeling, and quality checks
Source integration and automation
BI & Analytics
Build dashboards, reporting experiences, and analytics workflows that support business decisions.
What’s included
KPI definition and dashboard design
Reporting layer and data visualization
User training and insight adoption
BI & Analytics
Build dashboards, reporting experiences, and analytics workflows that support business decisions.
What’s included
KPI definition and dashboard design
Reporting layer and data visualization
User training and insight adoption
BI & Analytics
Build dashboards, reporting experiences, and analytics workflows that support business decisions.
What’s included
KPI definition and dashboard design
Reporting layer and data visualization
User training and insight adoption
AI-Ready Data Foundations
Prepare trusted, governed, and accessible data foundations for machine learning and AI.
What’s included
Dataset readiness and gap assessment
Feature, model, and pipeline preparation
Governance for AI and ML use cases
AI-Ready Data Foundations
Prepare trusted, governed, and accessible data foundations for machine learning and AI.
What’s included
Dataset readiness and gap assessment
Feature, model, and pipeline preparation
Governance for AI and ML use cases
AI-Ready Data Foundations
Prepare trusted, governed, and accessible data foundations for machine learning and AI.
What’s included
Dataset readiness and gap assessment
Feature, model, and pipeline preparation
Governance for AI and ML use cases
Data Governance & Quality
Improve data definitions, lineage, access control, quality, and ownership.
What’s included
Data ownership and definitions
Quality rules and monitoring
Access control and lineage planning
Data Governance & Quality
Improve data definitions, lineage, access control, quality, and ownership.
What’s included
Data ownership and definitions
Quality rules and monitoring
Access control and lineage planning
Data Governance & Quality
Improve data definitions, lineage, access control, quality, and ownership.
What’s included
Data ownership and definitions
Quality rules and monitoring
Access control and lineage planning
Featured use cases
How a manufacturing customer centralized data for faster reporting
Unified
Operational data foundation
Dashboards
Business reporting views
The Challenge
A manufacturing customer needed to bring operational data from multiple systems into one reliable environment and reduce delays in reporting.
What We Did
GoPomelo built a data pipeline into BigQuery, modernized analytics workloads, and created dashboards for business teams.
Outcome
Teams gained easier access to historical data and a more consistent foundation for reporting and visualization.
How a telecom customer unified marketing data for reporting
One source
Centralized marketing data
BI-ready
Consistent reporting across tools
The Challenge
A telecom customer needed to consolidate marketing data from several systems and give teams a more consistent way to analyze performance.
What We Did
GoPomelo built an ETL pipeline into a cloud data environment and connected it to reporting tools for analysis.
Outcome
Marketing data became easier to access and manage, helping teams create reports more efficiently.
How a manufacturing customer centralized data for faster reporting
Unified
Operational data foundation
Dashboards
Business reporting views
The Challenge
A manufacturing customer needed to bring operational data from multiple systems into one reliable environment and reduce delays in reporting.
What We Did
GoPomelo built a data pipeline into BigQuery, modernized analytics workloads, and created dashboards for business teams.
Outcome
Teams gained easier access to historical data and a more consistent foundation for reporting and visualization.
How a telecom customer unified marketing data for reporting
One source
Centralized marketing data
BI-ready
Consistent reporting across tools
The Challenge
A telecom customer needed to consolidate marketing data from several systems and give teams a more consistent way to analyze performance.
What We Did
GoPomelo built an ETL pipeline into a cloud data environment and connected it to reporting tools for analysis.
Outcome
Marketing data became easier to access and manage, helping teams create reports more efficiently.
How we work
A clear path from assessment to continuous improvement
01/
Assess
Design and build modern data platforms that support scalable analytics and future AI use cases.
01/
Assess
Design and build modern data platforms that support scalable analytics and future AI use cases.
02/
Design
Define the architecture, data model, governance approach, KPIs, and user experience.
02/
Design
Define the architecture, data model, governance approach, KPIs, and user experience.
03/
Build
Create pipelines, platforms, dashboards, models, and integrations.
03/
Build
Create pipelines, platforms, dashboards, models, and integrations.
04/
Adopt & Optimize
Enable users, improve data literacy, and expand useful analytics over time.
04/
Adopt & Optimize
Enable users, improve data literacy, and expand useful analytics over time.
Why GoPomelo
Why modernize data and analytics with GoPomelo?
GoPomelo connects data engineering, analytics, governance, and business context so information can support decisions and AI.
From data to decisions
Link platform design and engineering to the reporting and decisions teams need.
AI-ready architecture and governance
Build for quality, access, lineage, scale, and future model use.
Adoption for business teams
Define KPIs, reporting experiences, and enablement so insights are used.
From data to decisions
Link platform design and engineering to the reporting and decisions teams need.
AI-ready architecture and governance
Build for quality, access, lineage, scale, and future model use.
Adoption for business teams
Define KPIs, reporting experiences, and enablement so insights are used.
FAQs
Questions
before we talk
Do we need a full data warehouse before starting analytics?
Do we need a full data warehouse before starting analytics?
Do we need a full data warehouse before starting analytics?
Can you help define our business metrics?
Can you help define our business metrics?
Can you help define our business metrics?
How does this connect to AI?
How does this connect to AI?
How does this connect to AI?
Can GoPomelo support existing BI tools?
Can GoPomelo support existing BI tools?
Can GoPomelo support existing BI tools?
Technology ecosystem
Start with a 30-min call
Ready to make your data more useful?
Tell us what decisions, reports, or AI use cases you want to improve. GoPomelo will help you define the right data path.