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Advanced Analytics The Next Wave in Business Intelligence. Balram Parappil Practice Head, BI&DW Zensar Technologies Ltd. The Human Migration path. Historical human migration patterns mapped by analyzing DNA samples from hundreds of thousands of people around the world. - PowerPoint PPT Presentation
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Advanced AnalyticsThe Next Wave in Business Intelligence
Balram Parappil
Practice Head, BI&DW
Zensar Technologies Ltd.
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The Human Migration path
Historical human migration patterns mapped by analyzing DNA samples from hundreds of thousands of people around the world
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Advanced Analytics – some pointers
Focused on finding patterns & relationships in data, and using that to predict future behaviour
“What will happen?” “Why is this happening?” “What can happen” etc. Discovery, Actionable Insight
Extremely complex(often SQL driven) queries & usage of statistical & predictive models & techniques
Usually involve processing large volumes of data – and quite often specially extracted/prepared data as well.
Usually demands high levels of expertise from users to define the models involved, and to infer the output
Mostly Expensive!
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Advanced Analytics – Some Typical Business applications
Churn
Loyalty
Retention Cross Sell/ Upsell
Loss Pervention
Anti-Fraud
Segmentation Market Basket Analysis
Survival Analysis
Drug Discovery
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What is at stake…
6x to 7x 96%50%
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The number of times more expensive to gain a customer
than to retain one
% of customers lost by US Companies every 5 years
# of negative pieces of advertising from one disgruntled
customer
% of customers who don't complain when they have a
problem, but don’t come back
50%% of customers who tell the
business they are "fairly satisfied" but won't be repeat buyers
25 to 95% 83%Increase in profits from a 5%
increase in customer retention
% of Customers who will remain loyal after a complaint is resolved
2xGrowth of Businesses which have a
reputation for excellent customer relations
Source: Bain & Co in HBS; Entrepreneur Business Centre's Information Resource Centre
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The CrispDM process
Problem Definition
• Initial Data gathering• First understanding of data
• Preparing Data for modeling tool
• Cleansing/transformation
• Modeling technique Selection
• Reevaluate data needs if reqd
• Model Evaluation against business needs
• Deployment of model, gain insight
• Clustering• Association• Regression• Classification
• Neural Networks• Decision Trees• Machine learning• Sequencing
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Major Technology players
Source: Forrester Wave : Predictive Analytics and Data Mining Solutions 2010
• SAS leads the pack, highest market share, best spread of solutions
• IBM integrating SPSS with Cognos suite
• Oracle leverages Oracle Data Mining tightly integrated with database
• KXen offers wide range of solutions
• TIBCO with Spotfire 3.1
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Advanced Analytics - Trends Increased Attention and focus for Advanced Analytics – hot priority item for the next 2-3 years
Increased Pervasiveness -> Moving on from the domain of PhDs and statistician to regular information workers . New vendors offering lower cost solutions will add to this
Text Analytics will become mainstream technology – initially overlapping with social media, but will extend to other domains as well
Social Media Analytics still evolving, a lot of players in the space right now
Technology Vendors scrambling for incorporating Advanced Analytics capabilities as part of main solution stack
Big Data Analytics focus – moving away from the constraint of DW driven predictive analytics
Analytics in the cloud – increased acceptance , mostly in SMBs
R language – increased acceptance, leading to lower-cost solutions
In-Memory Analytics gaining momentum
Source – various analysts & industry observers
Predictive Analytics is the next big battleground in the BI Market!
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Moving from Experts to Information Workers? Info workers want smarter , more predictive apps Packages that can be used by everyone Complexity hidden inside the tool Higher levels of usability Include visualization and embedded predictive models
with apps Info workers don’t want to know they have analytics
– they just want to have the right answers!
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R – game changer ?
Programming Language for Statistical Computing & Analysis – Open source
Offers a fascinating low-cost option compared to industry leaders
Still evolving, in a continuous improvement mode In-memory features are a big advantage Big bets being placed on R by many vendors
SAS, Information Builders, Netezza, Jaspersoft – joining the R bandwagon
Expected to be picked up and integrated by most predictive analytics vendors to enhance capabilities
Next 2-3 years will see R evolving and being accepted in the mainstream – once rough edges are polished
Developed in 1993
• Highly Extensible, with additional packages being built continuously
• Uses a command line interface, several GUIs are available too
• Variety of Statistical and graphics techniques
• Multiple versions/modes available
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Survey feedback
The Challenge of Unstructured Data
Sales InfoCustomer feedback
Service Info
Analytical Process
Decisions??
Blog entries
Online
reviews
?• 92% of Consumers search for Information online• 46% them are influenced to purchase• 43% deterred from purchasing
( Source – ChannelAdvisor- Consumer Shopping Habits Suvey 2010
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Text Analytics/Text Mining -Increasing Relevance and Adoption
Linguistic, statistical and machine learning techniques to structure and model information content from textual sources– Information Retrieval– Pattern Recognition– Entity recognition– Co-references– Sentiment Analysis
Picture Courtesy - IBM
• Major Vendors – IBM, SAS, Offer focused Text Analytics solutions
• Listening Post Services for Sentiment Analysis
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Social Media – Consumers & Producers
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Social Media Analytics –an evolving discipline
A number of players in the market Typically covers the common social media
content like blogs, social networking sites, Discussion forums etc
Primary Objective : Get insight into products/brands, understand user sentiment and behaviour, perception etc.
Clarabridge, Radian6, ScoutLabs. Alterian, Attentio etc are some popular tools
Advanced, Predictive Capabilities getting enhanced
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Sample screenshot - Clarabridge
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Big Data Analytics
Analytics Involving possibly Petabytes of data Pressure taken off traditional Data Warehouses and similar data sources
for analytics Separate Analytics Database focusing on massive query performance Unshackles from the limitations the existing data warehouse design has in
terms of performance and scaleability Columnar vs Row-based? Two schools of thought MPP capabilities are leveraged to the hilt Leverages frameworks like MapReduce, Hadoop etc Aster Data, ParAccel, Teradata etc focused in this area
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Thank You