1 Big Data and Predictive Analytics: Fiserv Data Mining Case Study [CON8631] Data Warehouse and Big Data Miguel Barrera - Director, Risk Analytics, Fiserv, Inc. Julia Minkowski - Risk Manager, Fiserv, Inc. Charlie Berger, MS Eng, MBA, Sr. Director Product Management, Data Mining and Advanced Analytics
2 Agenda R Big Data and Predictive Analytics: Fiserv Data Mining Case Study [CON8631] 1. Oracle Advanced Analytics Overview Charlie Berger, Sr. Director Product Management, Data Mining and Advanced Analytics, Oracle Corporation 2. Fiserv Data Mining Case Study Miguel Barrera - Director, Risk Analytics, Fiserv, Inc. Julia Minkowski - Risk Manager, Fiserv, Inc. Copyright 2014 Oracle and/or its affiliates. All rights reserved. 2
3 Planning for Future Growth of Data Exponentially Greater than Growth of Data Analysts! Conclusion Data Analysis platforms need to be Extremely Easy to Learn, yet.. Extremely Powerful and Automated as much as possible! Copyright 2014 Oracle and/or its affiliates. All rights reserved. Oracle Confidential Internal/Restricted/Highly Restricted 3
4 Oracle Advanced Analytics Database Evolution 7 Data Mining Partners Analytical SQL in the Database Oracle acquires Thinking Machine Corp s dev. team + Darwin data mining software Oracle Data Mining 9.2i launched 2 algorithms (NB and AR) via Java API Oracle Data Mining 10g & 10gR2 introduces SQL dm functions, 7 new SQL dm algorithms and new Oracle Data Miner Classic wizards driven GUI ODM 11g & 11gR2 adds AutoDataPrep (ADP), text New algorithms (EM, PCA, SVD) Predictive Queries SQLDEV/Oracle Data Miner 4.1 SQL script generation, JSON Query node & SQL Query node (R integration) mining, perf. improvements SQLDEV/Oracle Data Miner OAA/ORE work flow GUI adds NN, Stepwise, launched scalable R algorithms Integration with R and Oracle Adv. Analytics introduction/addition of for Hadoop Connector Oracle R Enterprise launched with parallel implementations of R Product renamed Oracle algorithms (NN, LM, NMF) Advanced Analytics (ODM + ORE) Copyright 2014 Oracle and/or its affiliates. All rights reserved.
5 Oracle Advanced Analytics Database Option History & High Level Architecture Development has spend 15 years stem-celling analytics and workhorse machine learning algorithms into Oracle Database 1999: Thinking Machines acquisition of Darwin data mining software Darwin killed Instead developed new within the SQL kernel, in-database implementations of popular & cutting edge data mining algorithms that leverage the strengths of the database counting, conditional probabilities, sort, rank, partition, group-by, collections, etc. Today, in 12c, the Database has become an Analytical Database Nearly 20 cutting edge machine learning algorithms and 50+ statistical functions implemented as true SQL functions When building models, leverage existing scalable technology (e.g., parallel execution, bitmap indexes, aggregation techniques) and add new core database technology (e.g., recursion within the parallel infrastructure, IEEE float, etc.) A data mining model is a schema object in the database, built via a PL/SQL API and scored via built-in SQL functions. True power is evident when scoring models using built-in SQL functions e.g. Exadata smart scan scoring Also expose data mining functions via OAA/Oracle R Enterprise integration. OAA/ORE also extends OAA with ability to run and call R packages. R Copyright 2014 Oracle and/or its affiliates. All rights reserved.
6 Oracle Advanced Analytics Performance and Scalability with Low Total Cost of Ownership Traditional Analytics Data Import Data Mining Model Scoring Data Prep. & Transformation Data Mining Model Building Data Prep & Transformation Data Extraction Hours, Days or Weeks Oracle Advanced Analytics avings Model Scoring Embedded Data Prep Model Building Data Preparation Secs, Mins or Hours Data remains in the Database Scalable, parallel Data Mining algorithms in SQL kernel Fast parallelized native SQL data mining functions, SQL data preparation and efficient execution of R open-source packages High-performance parallel scoring of SQL data mining functions and R open-source models Fastest way to deliver enterprise-wide predictive analytics Integrated GUI for Predictive Analytics Database scoring engine Lowest TCO Eliminate data duplication Eliminate separate analytical servers Leverage investment in Oracle IT Copyright 2014 Oracle and/or its affiliates. All rights reserved.
7 Oracle Advanced Analytics Database Architecture Component of Oracle Database SQL Functions SQL Developer R Client OBIEE Applications Oracle Database Enterprise Edition Oracle Advanced Analytics Native SQL Data Mining/Analytic Functions + High-performance R Integration for Scalable, Distributed, Parallel Execution Copyright 2014 Oracle and/or its affiliates. All rights reserved.
8 Oracle Advanced Analytics Database Option Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics Key Features In-database data mining algorithms and open source R algorithms SQL, PL/SQL, R languages Scalable, parallel in-database execution Workflow GUI and IDEs Integrated component of Database Enables enterprise analytical applications Copyright 2014 Oracle and/or its affiliates. All rights reserved.
9 Be Specific in Problem Statement Poorly Defined Better Data Mining Technique Predict employees that leave Based on past employees that voluntarily left: Create New Attribute EmplTurnover O/1 Predict customers that churn Target best customers How can I make more $$? Which customers are likely to buy? Who are my best customers? How can I combat fraud? Based on past customers that have churned: Create New Attribute Churn YES/NO Recency, Frequency Monetary (RFM) Analysis Specific Dollar Amount over Time Window: Who has spent $500+ in most recent 18 months What helps me sell soft drinks & coffee? How much is each customer likely to spend? What descriptive rules describe best customers? Which transactions are the most anomalous? Then roll-up to physician, claimant, employee, etc. Copyright 2014 Oracle and/or its affiliates. All rights reserved.
10 Responders More Data Variety Better Predictive Models Increasing sources of relevant data can boost model accuracy 100% 100% Model with Big Data and hundreds -- thousands of input variables including: Demographic data Purchase POS transactional data Unstructured data, text & comments Spatial location data Long term vs. recent historical behavior Web visits Sensor data etc. 0% Population Size Naïve Guess or Random Model with 20 variables Model with 75 variables Model with 250 variables Copyright 2014 Oracle and/or its affiliates. All rights reserved.
11 Predicting Behavior Identify Likely Behavior and their Profiles Consider: Demographics Past purchases Recent purchases Customer comments & tweets Copyright 2014 Oracle and/or its affiliates. All rights reserved.
12 SOURCES Oracle Big Data Management System Oracle Big Data SQL Oracle Database Cloudera Hadoop Oracle NoSQL Database Oracle R Advanced Analytics for Hadoop Oracle R Distribution Big Data Appliance B Oracle Big Data Connectors Oracle Data Integrator Oracle Oracle Industry Database Models Oracle Advanced Oracle Advanced Security Analytics Oracle Advanced Oracle Analytics Spatial & Graph Oracle Spatial & Graph Oracle Exadata select cust_id from customers where region = US and prediction_probability(churnmod, Y using *) > 0.8; Copyright 2014 Oracle and/or its affiliates. All rights reserved.
13 R Widely Popular R is a statistics language similar to Base SAS or SPSS statistics R environment Strengths Powerful & Extensible Graphical & Extensive statistics Free open source Challenges Memory constrained Single threaded Outer loop slows down process Not industrial strength Copyright 2014 Oracle and/or its affiliates. All rights reserved.
14 Oracle Advanced Analytics Oracle R Enterprise Compute Engines 1 R Engine Other R packages Oracle R Enterprise packages SQL Results 2 3 Oracle Database R Open Source User tables?x R Results R Engine Other R packages Oracle R Enterprise packages User R Engine on desktop Database Compute Engine R Engine(s) spawned by Oracle DB R-SQL Transparency Framework intercepts R functions for scalable in-database execution Function intercept for data transforms, statistical functions and advanced analytics Interactive display of graphical results and flow control as in standard R Submit entire R scripts for execution by database Scale to large datasets Access tables, views, and external tables, as well as data through DB LINKS Leverage database SQL parallelism Leverage new and existing in-database statistical and data mining capabilities Database can spawn multiple R engines for database-managed parallelism Efficient data transfer to spawned R engines Emulate map-reduce style algorithms and applications Enables lights-out execution of R scripts Copyright 2014 Oracle and/or its affiliates. All rights reserved.
15 Integrated Business Intelligence Enhance Dashboards with Predictions and Data Mining Insights In-database predictive models mine customer data and predict their behavior OBIEE s integrated spatial mapping shows location All OAA results and predictions available in Database via OBIEE Admin to enhance dashboards Oracle BI EE defines results for end user presentation Oracle Advanced Analytics data mining results available to Oracle BI EE Copyright 2014 Oracle and/or its affiliates. All rights reserved.
16 Oracle Communications Industry Data Model Predictive Analytics Applications Pre-Built Predictive Models Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics OAA s clustering and predictions available in-db for OBIEE Automatic Customer Segmentation, Churn Predictions, and Sentiment Analysis Copyright 2014 Oracle and/or its affiliates. All rights reserved.
17 Fusion HCM Predictive Workforce Predictive Analytics Applications Fusion Human Capital Management Powered by OAA Oracle Advanced Analytics factoryinstalled predictive analytics Employees likely to leave and predicted performance Top reasons, expected behavior Real-time "What if?" analysis Copyright 2014 Oracle and/or its affiliates. All rights reserved.
18 Oracle Advanced Analytics Database Option Oracle Data Miner 4.0 Summary New Features Oracle Data Miner/SQLDEV 4.1 (for Oracle Database 11g and 12c) New Graph node (box, scatter, bar, histograms) SQL Query node + integration of R scripts Automatic SQL script generation for deployment JSON Query node (added in 4.1) Oracle Advanced Analytics 12c features exposed in Oracle Data Miner New SQL data mining algorithms/enhancements Expectation Maximization clustering algorithm PCA & Singular Vector Decomposition algorithms Improved/automated Text Mining, Prediction Details and other algorithm improvements) Predictive SQL Queries automatic build, apply within SQL query Copyright 2014 Oracle and/or its affiliates. All rights reserved.
19 12c New Features New Server Functionality Predictive Queries Immediate build/apply of ODM models in SQL query Classification & regression Multi-target (nested) problems Clustering query Anomaly query Feature extraction query Results/Predictions! R OAA automatically creates multiple anomaly detection models Grouped_By and scores by partition via powerful SQL query Copyright 2014 Oracle and/or its affiliates. All rights reserved.
20 Oracle Advanced Analytics OAA/ORACLE DATA MINER QUICK 2 MINUTE DEMO Copyright 2014 Oracle and/or its affiliates. All rights reserved.
21 Take a Test Drive! Vlamis Software, Oracle Partner Offers FREE Test Drives on the Amazon Cloud Step 1 Fill out request Go to Step 2 Connect Connect with Remote Desktop Step 3 Start Test Drive! Oracle Database + Oracle Advanced Analytics Option SQL Developer/Oracle Data Miner GUI Demo data for learning Follow Tutorials Copyright 2014 Oracle and/or its affiliates. All rights reserved. Oracle Confidential Internal/Restricted/Highly Restricted 21
22 New book on Oracle Advanced Analytics available Book available on Amazon Predictive Analytics Using Oracle Data Miner: Develop for ODM in SQL & PL/SQL Copyright 2014 Oracle and/or its affiliates. All rights reserved. Oracle Confidential Internal/Restricted/Highly Restricted 22
23 OAA Links and Resources Oracle Advanced Analytics Overview: Link to presentation Big Data Analytics using Oracle Advanced Analytics In-Database Option OAA data sheet on OTN Oracle Internal OAA Product Management Wiki and Workspace YouTube recorded OAA Presentations and Demos: Oracle Advanced Analytics and Data Mining at the YouTube Movies (6 + OAA live Demos on ODM r 4.0 New Features, Retail, Fraud, Loyalty, Overview, etc.) Getting Started: Link to Getting Started w/ ODM blog entry Link to New OAA/Oracle Data Mining 2-Day Instructor Led Oracle University course. Link to OAA/Oracle Data Mining 4.0 Oracle by Examples (free) Tutorials on OTN Take a Free Test Drive of Oracle Advanced Analytics (Oracle Data Miner GUI) on the Amazon Cloud Link to SQL Developer Days Virtual Event w/ downloadable VM of Oracle Database + ODM/ODMr and e-training for Hands on Labs Link to OAA/Oracle R Enterprise (free) Tutorial Series on OTN Additional Resources: Oracle Advanced Analytics Option on OTN page OAA/Oracle Data Mining on OTN page, ODM Documentation & ODM Blog OAA/Oracle R Enterprise page on OTN page, ORE Documentation & ORE Blog Oracle SQL based Basic Statistical functions on OTN Business Intelligence, Warehousing & Analytics BIWA Summit 15, Jan 27-29, 2015 at Oracle HQ Conference Center Copyright 2014 Oracle and/or its affiliates. All rights reserved.
24 BIWA Summit January 27-29, 2015 Oracle HQ Conference Center Copyright 2014 Oracle and/or its affiliates. All rights reserved.
25 Faster than a Mouse: Turn Data Mining Strategy into Action Miguel Barrera, Director of Risk Analytics, Fiserv Inc. Julia Minkowski, Risk Manager, Fiserv Inc.
26 Use Case : Fraud Prevention in Online Payments Agenda Best Practices : Turning Data Mining Strategy into Action Fiserv, Inc. or its affiliates.
27 27 Use Case : Fraud Prevention in Online Payments
28 Risk Fiserv Electronic Payments We prevent $200M in losses every year using data to monitor, understand and anticipate fraud We manage risk for $24BB in transfers, servicing 2,000+ US financial institutions, including the 5 of the top 10 banks A department of 5 people, we operated in start-up mode until we were acquired in 2011 by Fiserv We build our risk models, supervise their installation & develop the next-generation of strategies for risk mitigation 2014 Fiserv, Inc. or its affiliates.
29 What is special about Fraud Prevention? Fraud is performed by organized criminal groups using sophisticated technologies and logistics Hard to detect: target has low frequency (2 in 10,000) The cost of mistakes is very high $ Losses if you fail to detect fraud 60% increase in customer attrition if you miss-classify The environment changes fast, so you need to adapt quickly Fraud prevention is a great field for the application of predictive analytics 2014 Fiserv, Inc. or its affiliates.
30 Analytics for Fraud Prevention Explore & Understand Anticipate & Control Monitor 2014 Fiserv, Inc. or its affiliates.
31 Risk Management: Goals and Constraints Goals: Help the business to expand to more profitable markets (on-line booking, real time payments), while keeping loss rates constant, and customers happy Constraints: Build a flexible system that adapts to new fraud patterns Service the existing client base Minimize the time that the production systems will be off-line or reset Build the next-generation of strategies with very limited resources 2014 Fiserv, Inc. or its affiliates.
32 Data Miner Survey 2013 by Rexer Analytics While 6 out 10 data miners report the data is available for analysis within days of capture, the time to deploy the models takes substantially longer. For 60% of the respondents the deployment time will range between 3 weeks and 1year Fiserv, Inc. or its affiliates.
33 The problem we faced We had the Algorithms (SAS + Angoss) Decision Trees + Gradient Boosting GLM and Logistic Regression SVM Bayesian Estimates But implementation took too long 3 months turn-around to estimate + deploy Logistic Regression (2008) 1 month to estimate and deploy Trees and GLM (2010) 1 week to estimate, 1 week to install rules in online application (2012) 1 day to estimate and deploy Trees + GLM models (2014) 2014 Fiserv, Inc. or its affiliates.
34 In Fraud-Mitigation Speed is the Key How long can you wait to deploy a solution? 2014 Fiserv, Inc. or its affiliates.
35 Why we liked Oracle Advance Analytics? Accuracy Agility Scalability The algorithms fit are as good as more complex algorithms The loss reduction from timely deployment (hours) compensates for model fit No data transfer needed (in-database) New opportunities to combine structured data with unstructured data The integration with our DB replication makes re-fit inexpensive The same algorithm can scale-up for all other clients 2014 Fiserv, Inc. or its affiliates.
36 Oracle Advanced Analytics Time Value 2012, Oracle Corporation 2014 Fiserv, Inc. or its affiliates.
37 Accuracy + Agility vs. Cost to Deploy Pick the best combination of: Less days to deployment High model accuracy Lower Cost Application Deploy (Days) Accuracy Total Cost SAS Server x5 ODM SAS Ba s e % Angos s % 2014 Fiserv, Inc. or its affiliates.
38 ODM Oracle Data Miner GUI Built-In in Oracle SQL Developer Tool Downloadable free on OTN version 4.0 or latest Easy to use GUI; explore data; work-flows Powerful multiple algorithms and data transformations; 100% in-db; build, evaluate and apply data mining models Deployable Shared analytical workflows; Generates PMML and SQL scripts for automation 2012, Oracle Corporation 2014 Fiserv, Inc. or its affiliates.
39 ODM Oracle Data Miner GUI Oracle Data Miner Nodes 2012, Oracle Corporation 2012, Oracle Corporation 2014 Fiserv, Inc. or its affiliates.
40 Oracle Data Miner Algorithms Identify most important risk factors (Attribute Importance) Predict Fraudsters behavior (Classification) Find profiles of bad transfers (Decision Trees) Predict Fraud Risk Probability (Regression) Segment overall population (Clustering) Find fraudulent transactions (Anomaly detection) Determine co-occuring items in baskets (Associations) Reduce a large dataset into representative new attributes (Feature Extraction) 2012, Oracle Corporation 2014 Fiserv, Inc. or its affiliates.
41 Turning Data Mining Strategy into Action
42 Best Practices in Analytics 1. Identify Benefits & Constraints Involve Key Stakeholders 2. Develop the Strategy Collect & Process Data Run Descriptive Analytics Identify patterns 3.Turn Strategy into Action Select Best Option(s) Business Manager Data Scientist IT Manager Success Factors and Constraints Align your Team s Incentives Provide Actionable Insights Test Predictive Models Simulate scenarios (Monte Carlo) Score models on KPI Install into Production Run A/B testing Start Small and Increase Gradually Select the Appropriate Infrastructure DB Architecture Modeling techniques Estimate Impact for the Business ROI /Cost Profitability Operations Track Benefits and KPI 2014 Fiserv, Inc. or its affiliates.
43 Involve the Right Stakeholders Business Manager IT Manager Data Scientist Preserve Service Level Agreement Reduce Operational Risk Preserve Budget 2014 Fiserv, Inc. or its affiliates.
44 Conflict of Interests? Cannot agree on success factors? Wonder why? 2014 Fiserv, Inc. or its affiliates.
45 IT Manager s Strategy Preserve Service Level Agreements Stable systems Ease of roll-back Minimize Operational risk Control Costs 2014 Fiserv, Inc. or its affiliates.
46 Data Scientist s Mind Estimate the Best Model Possible Improve Detection Rates Better Algorithms, Faster Hardware Big(ger) Data! Explore New Algorithms Source: Ayasdi 2014 Put some power behind it!! 2014 Fiserv, Inc. or its affiliates.
47 Business Manager s Mind Maximize Productivity: Build for specific needs What is the cost? Why does it take so long? What is the impact on customer experience? And: Don t talk to me in Tech-Speak! First we ran a chi- square test, and then we converted the categorical data to ordinal, next we ran a logistic regression, and then we lagged the economic data by a year Source: Davenport, Tom (2013), Keep up with your quants", Harvard Business Review, Issue July-August Fiserv, Inc. or its affiliates.
48 Communication issue 2014 Fiserv, Inc. or its affiliates.
49 Managing the Quants Define clearly the objective and constraints Implement SMART* goal setting Get familiar with basic analytics concepts Establish a time-line for delivery then multiply x 2 Make sure you understand enough to explain to other executives you will champion this initiative and negotiate the budgets Source: Davenport, Tom (2013), Keep up with your quants", Harvard Business Review, Issue July-August Fiserv, Inc. or its affiliates.
50 Taking Care of Business (tips for Data Scientists) Communicate clearly business level information When and what is the expected result Present the key concept in 2 phrases Avoid technical language for communication If asked for more details, then present the How Provide a Business Dashboard Provide the $$ metrics profit/loss reduction Show the impact of algorithms deployed / provided Current vs. Historical Pick the right model - the model that maximizes the ROI Source: Davenport, Tom (2013), Keep up with your quants", Harvard Business Review, Issue July-August Fiserv, Inc. or its affiliates.
51 From Paper to Execution Bring that Escher guy here NOW!! 2014 Fiserv, Inc. or its affiliates.
52 Success Factors in Fraud Mitigation Accuracy: Low False Negative Rate (How much fraud $ you miss) Low False Positive Rates (How many people you bother with additional identity verification) Agility Minimize reaction-time to fraud attacks Make your updates easy to implement Scalability: Automate processes to keep variable cost down Invest in infrastructure and replicate across clients 2014 Fiserv, Inc. or its affiliates.
53 Selecting the Right Tools Easy to Use and Deploy Can combine structured data with unstructured data - new trend Tools that integrate in the DB Allow for Fast Model Fitting and Re-estimation Minimize data transport across systems In-House Algorithms Develop algorithms that can run directly in DB for fast estimation and execution 2014 Fiserv, Inc. or its affiliates.
54 Tracking Performance: Dashboard Our dashboards tracked the key performance metrics: Historical Trends for Fraud Rates and Losses (Business KPI) Percentage of Transfers affected by Risk Mitigation (Business KPI) % of population affected by policy and % of fraud prevented (KPI for Analytics) Fraud detection rates for rules installed (KPI for Analytics) 2014 Fiserv, Inc. or its affiliates.
55 Key Takeaways On Fraud Modeling When dealing with fraud, the speed to implement a new model is the most important factor Improvements in accuracy may be lost due to delays in deployment; systems with fast turnaround have better ROI than complex algorithms with long implementation times. Select the right tools that will enable fast analysis and deployment. Turning Strategy into Action Involving the key stakeholders early in the process maximizes your chance for success. Once you have aligned the incentives for the team, selecting the appropriate techniques, tools and infrastructure becomes much simpler It is crucial for business managers to correctly define the problems and objectives, asking the right questions and learning the basic analytical concepts For data scientists it is important to select their models and projects based on the expected business impact and to translate their findings into the relevant metrics 2014 Fiserv, Inc. or its affiliates.
56 If you have further questions or comments, please contact: Julia Minkowski Lead Risk Analyst, Fiserv Inc Source: Kdnuggets, Fiserv, Inc. or its affiliates.
57 Copyright 2014 Oracle and/or its affiliates. All rights reserved.
Oracle Advanced Analytics 12c & SQLDEV/Oracle Data Miner 4.0 New Features Charlie Berger, MS Eng, MBA Sr. Director Product Management, Data Mining and Advanced Analytics firstname.lastname@example.org www.twitter.com/charliedatamine
Transform Big Data into Bigger Insight with Oracle Exadata and Oracle Advanced Analytics Charlie Berger, Senior Director, Product Mgt. OAA Marcos Arancibia, Product Manager, OAA Michael Bramley, Science
Oracle Advanced Analytics Oracle R Enterprise & Oracle Data Mining R The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated
Fraud and Anomaly Detection Using Oracle Advanced Analytic Option 12c Charlie Berger Sr. Director Product Management, Data Mining and Advanced Analytics email@example.com www.twitter.com/charliedatamine
Starting Smart with Oracle Advanced Analytics Great Lakes Oracle Conference Tim Vlamis Thursday, May 19, 2016 Vlamis Software Solutions Vlamis Software founded in 1992 in Kansas City, Missouri Developed
Getting Started with Oracle Data Miner 11g R2 Brendan Tierney Scene Setting This is not about DB log mining This is an introduction to ODM And how ODM can be included in OBIEE (next presentation) Domain
Data Analysis with Various Oracle Business Intelligence and Analytic Tools Session ID: 108680 Prepared by: Tim and Dan Vlamis Vlamis Software Solutions www.vlamis.com @TimVlamis Agenda What we will talk
Preventing Fraud with Through Analytics Satya Bhamidipati Data Scientist Business Analytics Product Group Copyright 2014 Oracle and/or its affiliates. All rights reserved. 2 Tax Fraud in Increasing 27%
Big Data Analytics with Oracle Advanced Analytics Making Big Data and Analytics Simple O R A C L E W H I T E P A P E R J U L Y 2 0 1 5 Disclaimer The following is intended to outline our general product
Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data
HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within
ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION EXECUTIVE SUMMARY Oracle business intelligence solutions are complete, open, and integrated. Key components of Oracle business intelligence
Up Your R Game James Taylor, Decision Management Solutions Bill Franks, Teradata Today s Speakers James Taylor Bill Franks CEO Chief Analytics Officer Decision Management Solutions Teradata 7/28/14 3 Polling
Introducing Oracle Exalytics In-Memory Machine Jon Ainsworth Director of Business Development Oracle EMEA Business Analytics 1 Copyright 2011, Oracle and/or its affiliates. All rights Agenda Topics Oracle
Big Data Analytics with Oracle Advanced Analytics In-Database Option Charlie Berger Sr. Director Product Management, Data Mining and Advanced Analytics firstname.lastname@example.org www.twitter.com/charliedatamine
1 Copyright 2011, Oracle and/or its affiliates. FPO In-Database Analytics: Predictive Analytics, Data Mining, Exadata & Business Intelligence Charlie Berger Sr. Director Product Management, Data Mining
An Oracle White Paper September 2009 Data Mining with Oracle Database 11g Release 2 Competing on In-Database Analytics Executive Overview... 1 In-Database Data Mining... 1 Key Benefits of Oracle Data Mining...
Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution
Mike Maxey Senior Director Product Marketing Greenplum A Division of EMC 1 Greenplum Becomes the Foundation of EMC s Big Data Analytics (July 2010) E M C A C Q U I R E S G R E E N P L U M For three years,
Big Data Are You Ready? Thomas Kyte http://asktom.oracle.com The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated
September 9 11, 2013 Anaheim, California Extend your analytic capabilities with SAP Predictive Analysis Charles Gadalla Learning Points Advanced analytics strategy at SAP Simplifying predictive analytics
Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??
III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution
IBM Software Information Management White Paper Harnessing the power of advanced analytics with IBM Netezza How an appliance approach simplifies the use of advanced analytics Harnessing the power of advanced
An Oracle White Paper February 2012 Oracle Data Mining 11g Release 2 Competing on In-Database Analytics Disclaimer The following is intended to outline our general product direction. It is intended for
High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances Highlights IBM Netezza and SAS together provide appliances and analytic software solutions that help organizations improve
Oracle Database 12c Plug In. Switch On. Get SMART. Duncan Harvey Head of Core Technology, Oracle EMEA March 2015 Safe Harbor Statement The following is intended to outline our general product direction.
ORACLE OLAP KEY FEATURES AND BENEFITS FAST ANSWERS TO TOUGH QUESTIONS EASILY KEY FEATURES & BENEFITS World class analytic engine Superior query performance Simple SQL access to advanced analytics Enhanced
This presentation is for informational purposes only and may not be incorporated into a contract or agreement. The following is intended to outline our general product direction. It is intended for information
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
Building and Deploying Customer Behavior Models February 20, 2014 David Smith, VP Marketing and Community, Revolution Analytics Paul Maiste, President and CEO, Lityx In Today s Webinar About Revolution
EMC Greenplum Driving the Future of Data Warehousing and Analytics Tools and Technologies for Big Data Steven Hillion V.P. Analytics EMC Data Computing Division 1 Big Data Size: The Volume Of Data Continues
Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure Requirements... 5 Solution Spectrum... 6 Oracle s Big Data
Bonus Chapter Ten Major Predictive Analytics Vendors In This Chapter Angoss FICO IBM RapidMiner Revolution Analytics Salford Systems SAP SAS StatSoft, Inc. TIBCO This chapter highlights ten of the major
Oracle9i Data Warehouse Review Robert F. Edwards Dulcian, Inc. Agenda Oracle9i Server OLAP Server Analytical SQL Data Mining ETL Warehouse Builder 3i Oracle 9i Server Overview 9i Server = Data Warehouse
Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data: The Datafication Of Everything Thoughts Devices Processes Thoughts Things Processes Run the Business Organize data to do something
Grow Revenues and Reduce Risk with Powerful Analytics Software Overview Gaining knowledge through data selection, data exploration, model creation and predictive action is the key to increasing revenues,
Oracle Big Data SQL Technical Update Jean-Pierre Dijcks Oracle Redwood City, CA, USA Keywords: Big Data, Hadoop, NoSQL Databases, Relational Databases, SQL, Security, Performance Introduction This technical
Oracle Business Analytics Overview Markus Päivinen Business Analytics Country Leader, Finland May 2014 1 Presentation content What are the requirements for modern BI Trend in Business Analytics Big Data
Big Data and Data Science: Behind the Buzz Words Peggy Brinkmann, FCAS, MAAA Actuary Milliman, Inc. April 1, 2014 Contents Big data: from hype to value Deconstructing data science Managing big data Analyzing
v2 High Performance Data Management Use of Standards in Commercial Product Development Jay Hollingsworth: Director Oil & Gas Business Unit Standards Leadership Council Forum 28 June 2012 1 The following
Data sheet HP Vertica OnDemand Enterprise-class Big Data analytics in the cloud Enterprise-class Big Data analytics for any size organization Vertica OnDemand Organizations today are experiencing a greater
Advanced Analytics The Way Forward for Businesses Dr. Sujatha R Upadhyaya Nov 2009 Advanced Analytics Adding Value to Every Business In this tough and competitive market, businesses are fighting to gain
Big Data: Are You Ready? Kevin Lancaster Director, Engineered Systems Oracle Europe, Middle East & Africa 1 A Data Explosion... Traditional Data Sources Billing engines Custom developed New, Non-Traditional
Oracle Data Mining Hands On Lab Material provided by Oracle Corporation Vlamis Software Solutions is one of the most respected training organizations in the Oracle Business Intelligence community because
IBM Software IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster Solve your toughest challenges with data mining Imagine if you could
SAP Brief SAP HANA Objectives Transform Your Future with Better Business Insight Using Predictive Analytics Dealing with the new reality Dealing with the new reality Organizations like yours can identify
Turning Data into Value Hadoop & SAS Data Loader for Hadoop Sebastiaan Schaap Frederik Vandenberghe Agenda What s Hadoop SAS Data management: Traditional In-Database In-Memory The Hadoop analytics lifecycle
Big Data, Cloud Computing, Spatial Databases Steven Hagan Vice President Server Technologies Big Data: Global Digital Data Growth Growing leaps and bounds by 40+% Year over Year! 2009 =.8 Zetabytes =.08
WHITE PAPER Harnessing the Power of Advanced How an appliance approach simplifies the use of advanced analytics Introduction The Netezza TwinFin i-class advanced analytics appliance pushes the limits of
Using OBIEE for Location-Aware Predictive Analytics Jean Ihm, Principal Product Manager, Oracle Spatial and Graph Jayant Sharma, Director, Product Management, Oracle Spatial and Graph, MapViewer Oracle
Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise
Oracle University Contact Us: Local: 1800 103 4775 Intl: +91 80 40291196 Oracle Big Data Essentials Duration: 3 Days What you will learn This Oracle Big Data Essentials training deep dives into using the
PAGE 1 l Teradata Magazine l Q1/2011 l 2011 Teradata Corporation l AR-6309 It s going mainstream, and it s your next opportunity. by Merv Adrian Enterprises have never had more data, and it s no surprise
IBM SPSS Modeler Professional Make Better Decisions Through Predictive Intelligence Highlights Easily access, prepare and model structured data with this intuitive, visual data mining workbench Rapidly
Predictive Analytics Powered by SAP HANA Cary Bourgeois Principal Solution Advisor Platform and Analytics Agenda Introduction to Predictive Analytics Key capabilities of SAP HANA for in-memory predictive
Customer Insight Appliance Enabling retailers to understand and serve their customer Customer Insight Appliance Enabling retailers to understand and serve their customer. Technology has empowered today
High-Performance Analytics David Pope January 2012 Principal Solutions Architect High Performance Analytics Practice Saturday, April 21, 2012 Agenda Who Is SAS / SAS Technology Evolution Current Trends
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice
IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model
Page 1 of 10 Quick Links Home Worldwide Search Microsoft.com for: Go : Home Product Information How to Buy Editions Learning Downloads Support Partners Technologies Solutions Community Previous Versions
SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market
IBM SPSS Modeler Three proven methods to achieve a higher ROI from data mining Take your business results to the next level Highlights: Incorporate additional types of data in your predictive models By
Big Data Oracle Big Data Strategy Simplified Infrastrcuture Selim Burduroğlu Global Innovation Evangelist & Architect Education & Research Industry Business Unit Oracle Confidential Internal/Restricted/Highly
Using Tableau Software with Hortonworks Data Platform September 2013 2013 Hortonworks Inc. http:// Modern businesses need to manage vast amounts of data, and in many cases they have accumulated this data
News and trends in Data Warehouse Automation, Big Data and BI Johan Hendrickx & Dirk Vermeiren Extreme Agility from Source to Analysis DWH Appliances & DWH Automation Typical Architecture 3 What Business
Predictive Analytics: Turn Information into Insights Pallav Nuwal Business Manager; Predictive Analytics, India-South Asia email@example.com +91.9820330224 Agenda IBM Predictive Analytics portfolio
SOLUTION BRIEF Understanding Your Customer Journey by Extending Adobe Analytics with Big Data Business Challenge Today s digital marketing teams are overwhelmed by the volume and variety of customer interaction
An Oracle White Paper June 2013 Oracle: Big Data for the Enterprise Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure
April 2016 JPoint Moscow, Russia How to Apply Big Data Analytics and Machine Learning to Real Time Processing Kai Wähner firstname.lastname@example.org @KaiWaehner www.kai-waehner.de LinkedIn / Xing Please connect!
Oracle Data Integration: CON7926 Oracle Data Integration: A Crucial Ingredient for Cloud Integration Julien Testut Principal Product Manager, Oracle Data Integration Sumit Sarkar Principal Systems Engineer,
INSIGHT Oracle's All- Out Assault on the Big Data Market: Offering Hadoop, R, Cubes, and Scalable IMDB in Familiar Packages Carl W. Olofson IDC OPINION Global Headquarters: 5 Speen Street Framingham, MA
Ganzheitliches Datenmanagement für Hadoop Michael Kohs, Senior Sales Consultant @mikchaos The Problem with Big Data Projects in 2016 Relational, Mainframe Documents and Emails Data Modeler Data Scientist
IBM Software Business Analytics IBM SPSS Modeler Solve Your Toughest Challenges with Data Mining Use predictive intelligence to make good decisions faster Solve Your Toughest Challenges with Data Mining
Oracle Data Mining In-Database Data Mining Made Easy! Charlie Berger Sr. Director Product Management, Data Mining and Advanced Analytics Oracle Corporation email@example.com www.twitter.com/charliedatamine
September 9 11, 2013 Anaheim, California Exploring the Synergistic Relationships Between, BW and HANA Sheldon Edelstein SAP Database and Solution Management Learning Points SAP Business Planning and Consolidation
Predictive Analytics How many of you used predictive today? 2015 SAP SE. All rights reserved. 2 2015 SAP SE. All rights reserved. 3 How can you apply predictive to your business? Predictive Analytics is
IBM Software IBM Cognos TM1 Enterprise planning, budgeting and analysis Highlights Reduces planning cycles by as much as 75% and reporting from days to minutes Owned and managed by Finance and lines of
Understanding Data Warehouse Needs Session #1568 Trends, Issues and Capabilities Dr. Frank Capobianco Advanced Analytics Consultant Teradata Corporation Tracy Spadola CPCU, CIDM, FIDM Practice Lead - Insurance
Fusion Applications Overview of Business Intelligence and Reporting components This document briefly lists the components, their common acronyms and the functionality that they bring to Fusion Applications.
The Oracle Data Mining Machine Bundle: Zero to Predictive Analytics in Two Weeks Collaborate 15 IOUG Presentation #730 Tim Vlamis and Dan Vlamis Vlamis Software Solutions 816-781-2880 www.vlamis.com Presentation
Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing