Advanced Analytics The Next Wave in Business Intelligence

1. Advanced AnalyticsThe Next Wave in Business Intelligence Balram Parappil Practice Head, BI&DW Zensar Technologies Ltd.

3. The Human Migration path Historical human migration patterns mapped by analyzing DNA samples from hundreds of thousands of people around the world

4. 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!

5. Advanced Analytics – Some Typical Business applications

6. What is at stake… 6x to 7x 50% 96% The number of times more expensive to gain a customer than to retain one % 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% 2x 83% % of Customers who will remain loyal after a complaint is resolved Increase in profits from a 5% increase in customer retention Growth of Businesses which have a reputation for excellent customer relations Source: Bain & Co in HBS; Entrepreneur Business Centre's Information Resource Centre

7. The CrispDM 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 • Neural Networks • Decision Trees • Machine learning • Sequencing • Clustering • Association • Regression • Classification

8. 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 • KXenoffers wide range of solutions • TIBCO with Spotfire 3.1 Source: Forrester Wave : Predictive Analytics and Data Mining Solutions 2010

9. 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!

10. 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!

11. 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

12. The Challenge of Unstructured Data Sales Info Customer feedback Service Info Analytical Process ? Blog entries Online reviews Decisions?? • 92% of Consumers search for Information online • 46% them are influenced to purchase • 43% deterred from purchasing Survey feedback ( Source – ChannelAdvisor- Consumer Shopping Habits Suvey 2010

13. 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 • Major Vendors – IBM, SAS, Offer focused Text Analytics solutions • Listening Post Services for Sentiment Analysis Picture Courtesy - IBM

14. Social Media – Consumers & Producers

15. 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, Attentioetc are some popular tools • Advanced, Predictive Capabilities getting enhanced

16. Sample screenshot - Clarabridge

17. 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, Hadoopetc • Aster Data, ParAccel, Teradata etc focused in this area

20. Thank You