Big Data & Data Science at DesignMind

©2014 DesignMind. All Rights Reserved.
Big Data and Data Science at DesignMind

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DesignMind’s Expertise and Offering
Power BI
Applications
Databases
Data Warehousing
Big Data
BI & Data Visualization
Information Sharing & Collaboration
Cloud Computing
Data Science

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Our Clients

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Agenda
Big Data
Data Science
Big Data and Data Science at DesignMind
Partners & Products
The Team

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What is Big Data?
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Large data sets
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excessive retrieval and processing time

structured and unstructured collections
BIG DATA

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SQL vs. Big Data

volume velocity variety

New tools and methodologies are needed!
Volume Velocity VarietySQLBIG DATA

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Examples of Big Data Applications
Vertical
Use Case
Financial Services
New Accounts Risk Assessment
Loans and Credit Cards Application Processing
Fraud Prevention
Insurance Underwriting
Cross-sell and Up-sell Consideration
Trading Risk
Telecom
Call Detail Records (CDRs)
Real-Time Bandwidth Allocation
Infrastructure Investment
New Product Development
Next Product to Buy
Retail
360' View of the Customer
Brand Sentiment Analysis
Localized, Personalized Promotions
Website Optimization
Optimal Store Layout
Manufacturing
Supply Chain and Logistics
Assembly Line Quality Assurance
Proactive Maintenance
Services
Resources Allocation
Quality Assurance
Workflows Routing

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Big Data Analysis vs. Traditional Data Analysis

different tools and methodologies

same underlying process
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common goal
1. Collect
2. Prepare
3. Examine
4. Model
5. Decide
6. Act
Decision Cycle

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Why Data Science?

broader skillset
Technology
Business
Statistics & Math
IT Professionals
DATA
SCIENTISTS

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BI vs. Data Science

Broader skillsetbroaderanalyticsspectrum
SQL Analytics
Descriptive Analytics
Data Mining
Predictive Analytics
Simulation
Optimization
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Count
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Mean
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OLAP
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Univariate Distribution
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Central tendency
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Dispersion
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Association rules
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Clustering
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Features extraction
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Classification
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Regression
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Forecasting
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Spatial
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Machine learnings
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Text analytics
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Monte Carlo
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Agent-based modeling
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Discrete event modeling
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Linear optimization
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Non-linear optimization

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Our Goal

to help our customers to fully exploit their big data
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high-quality data
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tools
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services

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High-Quality Data is …
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T

R
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A

C

E

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Tools for…
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D

R
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E

A
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M

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Services to better exploit data and use tools …
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O
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P
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T

I

C

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Cloudera is the leader in Apache Hadoop-based software and services and offers a powerful new data platform that enables enterprises and organizations to look at all their data (structured as well as unstructured) and ask bigger questions for unprecedented insight at the speed of thought.
Platfora's Big Data Analytics Platform masks the complexity of Hadoop, making it easy for customers to understand all the facts in their business across events, actions, behaviors and time.
Tableau makes it easy for people to rapidly transform data into smart business analytics.
Windows Azure HDInsight is Microsoft’s easy to manage, agile, and open Enterprise-ready Hadoop in the cloud.
Enterprises can combine both relation and non-relational data with the new PolyBase, available in SQL Server 2012 Parallel Data Warehouse.
Partners

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Products
Pig

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
big data better questions better answers  better decisions
big data to convert the invisible into the visible

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Big Data & Data Science at DesignMind

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