Uninterrupted Operational Intelligence and Business Analytics

Transcription

1 Atre Group, Inc. Uninterrupted Operational Intelligence and Business Analytics To provide value from all data to all users from all channels by sensing and responding ASAP March 19, 2014 San Mateo, California, USA Shaku Atre Atre Group, Inc. 366 West 11 th Street, Suite 7D, New York, NY th Avenue, Santa Cruz, CA Copyright Copyright 2003 Present 2003-Present Atre Atre Group, Group, Inc. Inc.

2 Today s Agenda Uninterrupted Operational Intelligence and Business Analytics What is Big Data? Using Big Data to improve customer experience Using uninterrupted operational intelligence applications to monitor, analyze, and respond ASAP Using Big Data to improve Key Performance Indicators (KPIs) in a synergistic way with your Business Intelligence systems 2

3 The V⁵ Function To qualify as a Big Data System, a system must be a function of V⁵ Big Data f Variety of Data, Various Interactions due to correlations between data, Velocity of data arrival and departure Volume of data, Providing big Value to the business via Business Analytics Big Data Business Analytics Must: Provide business performance comparisons Use Big Data as actionable information to improve performance of the business 3

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6 The Three Principles of Analytics First principle of analytics Based on comparison Better or worse? Than what? Second principle of analytics Based on evidence Facts Because data doesn t lie What we sold How we sold Why we sold or didn t sell What we were able to sell compared to the goal set? Third principle of analytics Provide actionable information with multiple variables Why, how, what, when where, how much What action should we be taking or should we keep it status quo? 6

7 Big Data Must Address User Needs For a system to qualify as a Big Data System it must address the needs of all types of users, their varied demands, and various technologies they are using. Percentage of technology-savvy users and customers Percentage of business-savvy users and customers Percentage of young customers < 25 years of age is falling is rising...is rising rapidly in the business environment in the operational business environment One of the big reasons is easy access to social media! 7

8 Uninterrupted Operational Intelligence Differs From Traditional BI Environments Traditional BI Suite of data access tools Support power users because BI apps are built by power users Predict future performance based on past data Require data warehouses and data containing past data Use KPIs and evaluate results based on predefined goals Uninterrupted Operational Intelligence Embedded in OLTP applications Support mostly operational users Implement decisions in an uninterrupted way, based on real-time on-the-spot, retrieved data Use real-time data from OLTP systems so users monitor activity as it happens for making decisions in an uninterrupted way Support real-time decisions with apps built by trained developers that interact with OLTP systems Predict what is going to happen and make it happen Reports what has happened is the safe, but low-value path Enables you to stick your neck out in an intelligent way! 8

9 Today s BI Analytics Systems Based on identifying Key Performance Indicators (KPIs) and evaluating results based on associated goals Help you analyze and predict what may happen Should be automated to receive immediate benefits on an ongoing basis The missing component today is automation Of Decision Making If you would like to have these slides please write to me at shaku@atre.com 9

10 Monitoring, Analyzing and Responding ASAP Implementing situationbased decisions Alerting appropriate people and systems Monitoring Discovering Deciding on Action Analyzing Discarding Understanding 10

11 Progressive Advancement from Business Analytics to Predictive Analytics to Make It Happen Analytics Operational Intelligence and Analytics Make it happen to the population based on a random sample Predictive Analytics What may happen based on a random sample Business Analytics What happened based on a random sample 11

12 Transforming Data to Decisions with Big Data Monitoring, Analyzing and Responding DATA INFORMATION DECISIONS Point of Sale Financial Market Basket Associations When product X is bought, product Y is bought Patterns Product Y is bought A% of time if product X is purchased Trends US sales increased this quarter by B% of product Y vs. last year s quarter Facts Product Y has $M in sales Marketing How do I advertise product X and provide discount of C% on Y? Sales How do I place products X and Y on the shelves? Next to which products? Manufacturing How many of products X and Y should we make this month? 12

13 Business Value of Uninterrupted Operational Intelligence from Big Data Business Value of Uninterrupted Operational Intelligence from Big Data f Automating discovery of problems and opportunities Noticing and responding ASAP Refining operations on a continual basis 13

14 Using Uninterrupted Operational Intelligence to Make Better Business Decisions Operational Intelligence applications evaluate the effects of alternate decisions Optimum solutions are implemented in real-time External Streaming Data External Streaming Data OLTP Sales CRM ERP Financials Operational Intelligence Applications Read-only Interactive dashboards Operational Intelligence dashboards, spreadsheets, graphs and displays Operational Real-Time Data Operational Real-Time Data 14

15 Real-Time or Near-Real-Time Visibility into Events is Uninterrupted Operational Intelligence Business Case Business Issue KPI Customer churn Generic vs. prescription drugs Big Data vs. proverbial wisdom Lost profits from profitable customers that churned Profit implications of generic drug strategies Saving the lives of premature infants % of profitable customers lost Increased use and revenues of generics Number of saved preemies Online shopping Shopping cart abandonment Number of abandoned carts Credit card fraud Car rental companies Market basket analysis Card use by unauthorized persons Use GPS to determine when vehicles are roaming and deviating from contract routes Products purchased in sequence Number of fraudulent incidents Number of accidents and stolen cars % of washer purchases followed by dryer purchases 15

16 Noticing and Responding Systems Data-Driven Features Data Data acquisition Big Data sources Integrated view of all data Complex-event processing CEP All operational data sources Operational technologies Acquisition must be very fast, even with huge volumes Seamless integration of Hadoop, social media, web statistics, demographics and corporate data Heterogeneous sources must be integrated ERPs, CRMs, websites Demographics Nielsen et al, websites, social media, credit data Data sources Oracle, SQL Server, spreadsheets, Word documents Without integration, if a customer has five bank accounts, the bank sees the customer as five people and not one. Including event correlation, positive as well as negative correlations especially the correlations that are not intuitive Should integrate all types of information Survey results, tweets, purchasing data, campaign response rates... Monitor and manage the operation of physical equipment Electrical grids, HVAC, refrigerators, aircraft, water usage, medical devices, oil refinery operations 16

17 Noticing and Responding Systems User Experience-Driven Features User Experience Fast results Interactive dashboards Visual data mining Access by existing apps Alerts and triggers App usage patterns Transfer raw data into analysis in minutes without IT intervention. Easy-to-use advanced and predictive analytics tools should optimize the marketing efforts. Graphics, text, maps, diagrams with schemas, and numbers in tables running on browsers and various types of mobile devices Highly visual and friendly environment that enables users to explore and profile data, and find obvious and counter-intuitive correlations Easy access to Big Data and other new sources by legacy reports, dashboards and systems Trigger and send automated , text and other notifications to users, devices and applications Analyze web session logs for usage patterns and issues Estimate when buyers will abandon their carts based on click patterns and shopping habits 17

18 Noticing and Responding Systems Analytics-Driven Features Analytics Trend detection Pattern detection Predictive analytics Application templates On both traditional and Big Data assets: Detect temporal trends Mine data for unknown and counter-intuitive correlations Make time-series forecasts On both traditional and Big Data assets: Detect within and among products, divisions, regions, people, etc. Mine data for unknown and counter-intuitive patterns Evaluate the predictability and accuracy of patterns and judgments Find relationships in past data and predict current and future risks Let users ask questions whose answers are in predictive analytics Provide prebuilt, customizable apps that address business scenarios Parameters for selection of data sources Predefined, easy-to-select KPIs Dashboards and charts Sample data for testing apps after they are customized 18

19 Operational Intelligence Monitoring Software Features of Noticing and Responding Systems If you would like to have these slides please write to me at Application requirements of noticing and responding systems can be driven by Data User Experience Analytics 19