Advanced Analytics The Next Wave in Business Intelligence

Advanced Analytics The 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 3

Advanced Analytics – some pointers q Focused on finding patterns & relationships in data, and using that to predict future behaviour q “What will happen? ” “Why is this happening? ” “What can happen” etc. Discovery, Actionable Insight q Extremely complex(often SQL driven) queries & usage of statistical & predictive models & techniques q Usually involve processing large volumes of data – and quite often specially extracted/prepared data as well. q Usually demands high levels of expertise from users to define the models involved, and to infer the output q Mostly Expensive! 4

Advanced Analytics – Some Typical Business applications Loss Pervention Survival Analysis Cross Sell/ Upsell Retention Loyalty Churn Anti-Fraud Market Basket Analysis Segmentation Drug Discovery 5

What is at stake… 6 x to 7 x 50% The number of times more expensive to gain a customer than to retain one 96% % of customers lost by US Companies every 5 years % of customers who don't complain when they have a problem, but don’t come back 55 50% # of negative pieces of advertising from one disgruntled customer % of customers who tell the business they are "fairly satisfied" but won't be repeat buyers 25 to 95% 2 x 83% Increase in profits from a 5% increase in customer retention Growth of Businesses which have a reputation for excellent customer relations % of Customers who will remain loyal after a complaint is resolved Source: Bain & Co in HBS; Entrepreneur Business Centre's Information Resource Centre 6

The Crisp. DM process • Initial Data gathering • First understanding of data Problem Definition • Preparing Data for modeling tool • Cleansing/transformation • Modeling technique Selection • Reevaluate data needs if reqd • Deployment of model, gain insight • Model Evaluation against business needs • Clustering • Association • Regression • Classification • Neural Networks • Decision Trees • Machine learning • Sequencing 7

Major Technology players • 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 Source: Forrester Wave : Predictive Analytics and Data Mining Solutions 2010 8

Advanced Analytics - Trends q Increased Attention and focus for Advanced Analytics – hot priority item for the next 2 -3 years q Increased Pervasiveness -> Moving on from the domain of Ph. Ds and statistician to regular information workers. New vendors offering lower cost solutions will add to this q Text Analytics will become mainstream technology – initially overlapping with social media, but will extend to other domains as well q Social Media Analytics still evolving, a lot of players in the space right now q Technology Vendors scrambling for incorporating Advanced Analytics capabilities as part of main solution stack q Big Data Analytics focus – moving away from the constraint of DW driven predictive analytics q Analytics in the cloud – increased acceptance , mostly in SMBs q R language – increased acceptance, leading to lower-cost solutions q In-Memory Analytics gaining momentum Source – various analysts & industry observers Predictive Analytics is the next big battleground in the BI Market! 9

Moving from Experts to Information Workers? q Info workers want smarter , more predictive apps q Packages that can be used by everyone q Complexity hidden inside the tool q Higher levels of usability q Include visualization and embedded predictive models with apps q Info workers don’t want to know they have analytics – they just want to have the right answers! 10

R – game changer ? q Programming Language for Statistical Computing & Analysis – Open source q Offers a fascinating low-cost option compared to industry leaders q Still evolving, in a continuous improvement mode q In-memory features are a big advantage q Big bets being placed on R by many vendors q SAS, Information Builders, Netezza, Jaspersoft – joining the R bandwagon q Expected to be picked up and integrated by most predictive analytics vendors to enhance capabilities q 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 11

The Challenge of Unstructured Data Sales Info Customer feedback Service Info Analytical Process ? Decisions? ? • 92% of Consumers search for Information online • 46% them are influenced to purchase • 43% deterred from purchasing Bl en og tri es ne s i l On view re Surve y feedb ac k ( Source – Channel. Advisor- Consumer Shopping Habits Suvey 2010 12

Text Analytics/Text Mining -Increasing Relevance and Adoption q 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 • Major Vendors – IBM, SAS, Offer focused Text Analytics solutions • Listening Post Services for Sentiment Analysis Picture Courtesy - IBM 13

Social Media – Consumers & Producers 14

Social Media Analytics –an evolving discipline q A number of players in the market q Typically covers the common social media content like blogs, social networking sites, Discussion forums etc q Primary Objective : Get insight into products/brands, understand user sentiment and behaviour, perception etc. q Clarabridge, Radian 6, Scout. Labs. Alterian, Attentio etc are some popular tools q Advanced, Predictive Capabilities getting enhanced 15

Sample screenshot - Clarabridge 16

Big Data Analytics q Analytics Involving possibly Petabytes of data q Pressure taken off traditional Data Warehouses and similar data sources for analytics q Separate Analytics Database focusing on massive query performance q Unshackles from the limitations the existing data warehouse design has in terms of performance and scaleability q Columnar vs Row-based? Two schools of thought q MPP capabilities are leveraged to the hilt q Leverages frameworks like Map. Reduce, Hadoop etc q Aster Data, Par. Accel, Teradata etc focused in this area 17

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