Leading Big Data Analytics Company

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What We Do

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Analyse

  • Cloud DW Consulting

  • Technology Selection

  • Data Discovery
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Build

  • Data Architecture

  • ETL Architecture

  • BI Implementation
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Migrate

  • Technology Migration

  • Data Migration

  • Platform Migration
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Engaging New Audiences through Smart Approach Engaging New Audiences through Smart Approach

Engaging New Audiences through Smart Approach

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Engaging New Audiences through Smart Approach

Services We Can Help You With

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DW Architecture <br> and Consulting

DW Architecture
and Consulting

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DW Architecture <br> and Consulting
Build your DW <br> on Cloud

Build your DW
on Cloud

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Build your DW <br> on Cloud
Cloud DW <br> Integration

Cloud DW
Integration

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Cloud DW <br> Integration
Business Analytics <br> as a Service

Business Analytics
as a Service

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Business Analytics <br> as a Service
Machine Learning / <br> Predictive Analytics

Machine Learning /
Predictive Analytics

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Machine Learning / <br> Predictive Analytics
Big Data <br> Analytics

Big Data
Analytics

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Big Data <br> Analytics

Our Awesome Team

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Josh Buttler

Josh Buttler

CEO & Founder

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Alex Maxwel

Alex Maxwel

Marketing Manager

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Janny Cotller

Janny Cotller

Web Developer

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DR Statham

DR Statham

UX/UI Designer

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We Always Try To Understand Users Expectation

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Have any question about us?

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What Users Saying

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Maxwel Warner

Maxwel Warner

Founder & CEO
Steven Smith

Steven Smith

Web Developer
Luice Lucy

Luice Lucy

Designer

Industries We Serve

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Manufacturing
Healthcare
Automobile
Banking
Real Estate
Logistics
Insurance
Capital Markets

Our Recent News

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Analytics on Clouds – why now?

Analytics on Clouds – why now?

If you recall about 6-7 years ago there was a huge hype of cloud. Everybody was excited by it and not many people knew what it was …same as big data today!! Over the period, some of the corporate applications like CRM and other lightweight applications did move to cloud but nothing more. Cloud was

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Enhancing Customer Life Time Value (CLTV)

Enhancing Customer Life Time Value (CLTV)

On the other hand they may be customers whose premium is less but they are loyal and may buy more policies in the coming years. These customers end up giving more revenue and profits for the organization. Clearly premium amount cannot be the only parameter to decide the valuable customers. A comprehensive approach it to

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How much data you really need?

How much data you really need?

With the advent of Big Data and the advancement of technology to store it, are we justified in holding large quantities of it? What are the drawbacks/benefits of doing so and how can organizations find the right balance. This is more than a decade ago. A large telecom organization was negotiating with its strategic outsourcing

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