Big Data (Adv. Analytics) in 15 Mins. Peter LePine Managing Director Sales Support IM & BI Practice

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1 Big Data (Adv. Analytics) in 15 Mins. Peter LePine Managing Director Sales Support IM & BI Practice

2 Agenda Big Data in 15 Mins. Goal: Provide a basic understanding of; What is Big Data; Why it s important to Customers; How Emtec can help identify Big Data opportunities. What are Big Data & Adv. Analytics How Organizations using Big Data How Emtec can help Emtec Overview

3 Big Data & Adv. Analytics Defined

4 Big Data Some real-world examples Retail Banking The expectedly large and negative consumer reaction to new fees announced by large US banks in late 2011 is a case in point. Hundreds of thousands of accounts were lost in a few days the equivalent of a year of losses under typical market conditions by the time consumer sentiment was fully understood and corrective action could be taken. Hospitality Industry Many airlines treat even their frequent flyers as strangers. We have the data to treat them as they would want to be treated EVP of British Airways Airlines & Hotels have acquired massive amounts of data via loyalty programs, and travel providers have begun to analyze data in ways they hope will increase personalization. 4

5 Big Data Some real-world examples EPM Online Media Analytics available to even the lowest YouTube channel producer now rival the most sophisticated reports CBS, NBC and ABC had available in the 1980s. Apply even better analytical engines to the data from Fitbit pedometers, Google Glass, Smartphones, etc. and you ve got an unprecedented depth and breadth of knowledge available soon to anyone, anywhere. James McQuivey is the author of Digital Disruption: Unleashing the Next Wave of Innovation. VP and Principal Analyst at Forrester Research. Every day, we create 2.5 exabytes of data so much that 90% of the data in the world today has been created in the last two years alone. 2.5Xb = 2,500,000,000,000,000,000 (1018) Bytes of data 5

6 Big Data Defined How BIG is Big Data??? Social Media Facebook reached 1 billion members in 2012 Entertainment Netflix has approx. 30 million subscribers Every download is 3 11 Gbytes Media Many channels of 7*24 streaming media Twitter, LinkedIn, Yelp, MSN, and lots more Google Search 1.2 Billion sites searched in 0.35 secs Corporate , SMS & FTP sent / received per day In 2011, the majority of & SMS users were; (Economist analysis) Asia Pacific accounts for 49% Europe accounts for 22% North America has about 14% Rest of World (15%) accounts for the remainder 6

7 Data Mining Defined The Engine of Advanced or Predictive Analytics Exploratory Data Analysis An iterative exploration & filtering of the result-set Hypothesis Testing Establish the conditions / filters, test / refine the validity of the Hypothesis Data Visualization Heat Map or other representation of the distribution of the Dataset Real-world examples of Data Mining Social Media - Tracking of Likes & Dislikes, reach out to Friends with similar interests Retail & Transportation Pattern matching to develop pricing strategies based on time, usage, family plans, etc. Insurance / Law Enforcement - Heat Map (Identify) based on more granular data than Zip code, more granular time periods, etc. 7

8 Predictive Analytics Defined Statistical function-based Analytics Aggregate a large Dataset of historical detail, filter out the Noise to Predict future; Trends / Behaviors / Events Analytical tools contain Libraries of Built-in Functions e.g. Business Functions NPV, Depreciation, Compound Growth Forecast Statistical Models Linear Regression, K-Factor, Probability modeling Real-world examples of Predictive Analytics On-line Retail Select any Product Product accessories (Associations) Hospitality Pricing based on factors such as; Load / Occupancy, Loyalty & Competitor pressures Transportation Predictive analysis to determine Route optimization, Maintenance schedules, Traffic patterns Big data analytics is done with Software Tools used as part of Advanced Analytics disciplines such as Predictive Analytics and Data Mining. 8

9 The Language of Big Data & Predictive Analytics Terminology of Big Data & Predictive Analytics Data Warehouse Appliance Parallel engine & Storage packaged in a single device. Unstructured data Any data that doesn t conform to a Field or Column paradigm (e.g. a Document) MapReduce Research project sponsored by IBM & Google to process very large datasets, in massively parallel platforms (1,000 s of Nodes) Hadoop Research project sponsored by Apache Foundation NoSQL Research project sponsored by UC Berkley & Rackspace Statistical Analysis Use of Statistical functions to Analyze & make data driven decisions from very large Datasets Behavioral Analytics Use of Behavioral (social sciences) functions Pattern Recognition Recognize patterns in very large Datasets 9

10 Summary - Big Data & Predictive Analytics Big Data & Predictive Analytics should not be confused with BI on Steroids Unique Platforms, Analytic Tools & Business usage. Every Client needs some BI capability, whereas Big Data & Predictive Analytics are not on the radar for smaller & mid-sized organizations. 10

11 How Customers are using Big Data & Adv. Analytics

12 Predictive Analytics Predictive Analytics isn t the same as BI Business Specialist / Power User tools that predict future trends using Statistical Analysis techniques. Analytic Result-set is Prioritized / Ranked by frequency / probabilities etc. 12

13 Target Clients / Industry Segments Retail Travel & Hospitality Financial Services Media High-volume Online & Traditional Retail Airlines, Hotels, Timeshare, Rental Cars, Travel Retail Banking, Credit Card, Insurance, Brokerage Mainstream Media Specialized Channels, etc. Growth Strategy Growth Strategy Growth Strategy Growth Strategy Use of Social media Analyze & Predict trends before competitors Customer loyalty Customer loyalty, Operational efficiencies Partner Revenue Acquisition, no single view of the customer Customer retention & Customer capture Cross-sell & Up-sell Media transition to subscription-based News / Topic / Event aggregation Target A&P spend to individual interests Customer Benefits Customer Benefits Customer Benefits Customer Benefits Right product, Right location, Right price Business Benefits Store Operations Product associations Improve Revenue & Margins Customer care / loyalty programs Business Benefits Optimize Operations Improve Revenue & Margins Reduce Costs across Products & Services Business Benefits Patterns of behavior Risk Analysis, Improve Revenue & Margins Narrow-cast News, no longer Broadcast Business Benefits Compete in a 7*24 Global Village Group associations / neighborhoods 13

14 Big Data & Predictive Analytics Landscape Data Mining Adv. Analytics Business Intelligence Analytic Engine Big Data Infrastructure SQL / ODBC or Other Data Drivers Structured Databases Unique Platforms, Analytic Tools & Business usage. Some familiar names IBM, Oracle, SAP & Microsoft Many new names in what is a new & growing market segment 14

15 How Emtec can Help

16 Big Data IM & BI Practice Overview Information Delivery BI, reporting & analysis, advanced analytics & Big Data, KPIs, dashboards & scorecards, business repository Information Management Data infrastructure, information as a corporate asset Data Integration Extract, transform, load from data sources into target DW or data marts Information Architecture Data Quality Data Governance Master Data DW Analysis & Design Content Management Assessments, strategy, roadmaps & blueprints, data architecture Data Integration Custom Application Development Services 16

17 Emtec Adv. Analytics Workflow Discovery Discovery Workshop or Assessment Understand functional (Business) requirements, and Understand the Data (Internal & External) Sources Develop a Strategic Roadmap for Analytics Pilot Project Technical Impl. Pilot Project & Project Planning Limited scope Pilot to test & validate hypothesis Build the business case for Implementation Prove the Technical Infrastructure (Tools & Data infrastructure) Technical Implementation Refine & Filter Analytical results Develop Analytic reports Evaluate how the results Correlate to real-world experience 17

18 Key Sales & Delivery Contacts Delivery Management (Mobile) Sajid Patel Sales Support (Mobile) Peter LePine OBIEE Practice Manager (Mobile) Jamal Syed MSFT BI Practice Manager (Mobile) Deric Selchow 18

19 Collateral specific to Big Data Sales Support Collateral Sales Briefing - Big Data in 15 Mins. Technical & Industry Case Studies Big Data Quick Reference Card Assessment Outline Marketing Collateral Industry Analyst Research papers 19

20 THANK YOU FOR YOUR TIME Please visit us online at

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