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Data-Driven on AWS with JCO Analytics


Turn your AWS data estate into always-on business intelligence.

Why a Data-Driven Approach on AWS Matters

Becoming data-driven isn’t about dumping everything into Amazon S3 and hoping insights appear. It’s about designing an AWS data foundation that makes trusted, timely data available to the people who need it, securely and at scale.

With the right architecture on AWS, you can:

 

Break down silos between operational systems, SaaS apps, and on-premise data

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Deliver real-time, self-service analytics to business teams

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Govern and secure sensitive data without slowing innovation

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Power AI, machine learning, and automation on top of clean, well-modeled data

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JCO Data Platform on AWS

JCO designs and implements a modular data platform on AWS that covers the full lifecycle from ingestion to consumption

Govern & Secure

Central catalog, access control, and data protection with AWS Glue Data Catalog, AWS Lake Formation, and AWS-native security services. (docs.aws.amazon.com)

Visualize & Share

Dashboards and interactive reports with Amazon QuickSight and/or integration to existing BI tools like Power BI. (Amazon Web Services, Inc.)

Ingest & Stream

Batch and real-time pipelines from databases, APIs, applications, and IoT into Amazon S3 and streaming services such as Amazon Kinesis.

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Warehouse & Analyze

Modern analytics layer on Amazon Redshift and/or federated query with Amazon Athena for fast SQL analytics at scale. (Amazon Web Services, Inc.)

Store & Organize

Centralized data lake / lakehouse on Amazon S3 with curated zones (bronze / silver / gold) and open formats.

Transform & Orchestrate

Serverless ETL/ELT pipelines with AWS Glue and AWS Lambda, plus workflow orchestration and monitoring. (Amazon Web Services, Inc.)

What JCO Delivers on AWS

1. Multi-Source Data Collection & Integration

Connect ERP, CRM, POS, marketing platforms, and custom apps into a unified AWS data platform.

  • Pre-built patterns for common SaaS and database sources

  • Real-time ingestion with Amazon Kinesis for clickstream, logs & IoT data

  • Change Data Capture (CDC) for efficient replication from transactional systems

2. Trusted, Well-Modeled Data

Explore data visually through interactive dashboard

Turn raw data into business-ready, well-governed datasets.

  • Medallion-style lakehouse layers (raw, refined, semantic) on S3

  • Business-friendly data models in Amazon Redshift and/or Athena

  • Built-in data quality checks, validation rules & audit trails in pipelines and reports.

3. Secure & Governed by Design

  • Fine-grained permissions using IAM, Lake Formation, and column/row-level security

  • Encryption in transit and at rest, key management, and logging

  • Data cataloging and lineage for compliance and impact analysis

4. Analytics, AI & Machine Learning Ready

  • BI dashboards for executives, operations, finance, marketing, and supply chain

  • Feature-ready datasets for Amazon SageMaker and other ML tools

  • Support for RFM scoring, forecasting, churn prediction, recommendation models, and more

5. Operated Like a Product, Not a Project

  • DataOps practices: CI/CD for data pipelines, automated testing, and monitoring

  • Cost optimization and capacity planning on AWS

  • Runbooks, training, and knowledge transfer for your internal teams

No Matter Your Industry, We Focus on Your Use Cases

Whether you’re in retail, F&B, membership organizations, education, healthcare, or manufacturing, JCO starts from your business questions:

Where are we leaking margin across the value chain?

Which customers are at risk of churn?

Which products drive profitable growth?

How can we automate manual reporting and reconciliations?

How We Work with You

1. Discover & Assess

 Review your current data sources, AWS footprint, and reporting pain points.

2. Design the AWS Data Blueprint

 Review your current data sources, AWS footprint, and reporting pain points.

3. Build the Core Platform

Stand up ingestion, lakehouse, warehouse, and core dashboards.

4. Scale with Data Products

Add use-case–specific models, APIs, and analytics apps.

5. Enable & Support

Train your team, establish DataOps, and provide ongoing enhancement support.

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