Predictive HCM Using Machine Learning Data Management Platforms Move the Algorithms; Not the Data!

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1 Predictive HCM Using Machine Learning Data Management Platforms Move the Algorithms; Not the Data! Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics Nancy Estell Zoder Director, Product Strategy, HCM Applications

2 Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle. 2

3 Agenda 1. Concepts / Architecture 2. Product Functionality Overview 3. Predicting Employee Attrition Demo 4. Predictive HCM Application 5. Getting Started

Anticipating Voluntary Employee Attrition HCM Early Warning Systems Can Avoid Huge Costs? X? X? X https://www.indusnet.

4 Anticipating Voluntary Employee Attrition HCM Early Warning Systems Can Avoid Huge Costs? X? X? X Employee-Turnover-1.jpg

5 Predicting Employee Top Performers Important to Correctly Identify the Top Performers?!!

Example Organizations for Predictive HCM

Service, Account Managers Insurance Claims

Medical Researchers Software, IT Developers,

Special Forces Sports Boston Red Sox, New

6 Example Organizations for Predictive HCM Knowledge Workers: Extremely Valuable and Difficult & Costly to Replace Financial Services Financial Advisors, Customer Service, Account Managers Insurance Claims Adjusters, Accountants, Financial Managers Hospitality, Travel, Services Chefs, Property Managers, Pilots, Flight Attendances, Cruise Directors Marketing Mgrs, Creative, Data Scientists Pharm/Life Sciences Researchers, Data Scientists Healthcare Physicians, Nurses, Managers, Medical Researchers Software, IT Developers, Architects, PMs Military Leadership, Pilots, Special Forces Sports Boston Red Sox, New England Patriots, Boston Celtics, Soccer Confidential Oracle Internal/Restricted/Highly Restricted 6

7 Dilbert on Predictive HCM

A1 A2 A3 A4 A5 A6 A7 Identify most important factor (Attribute Importance) Predict customer behavior (Classification) Predict or estimate a value (Regression)

8 What is Machine Learning and Predictive Analytics? Automatically sifting through large amounts of data to create models that find previously hidden patterns, discover valuable new insights and make predictions A1 A2 A3 A4 A5 A6 A7 Identify most important factor (Attribute Importance) Predict customer behavior (Classification) Predict or estimate a value (Regression) Find profiles of targeted people or items (Decision Trees) Segment a population (Clustering) Find fraudulent or rare events (Anomaly Detection) Determine co-occurring items in a baskets (Associations) Copyright 2016, Oracle and/or its affiliates. All rights reserved.

9 Traditional vs. Oracle Machine Learning/Predictive Analtyics Traditional Move the data Don t move the data! 9

10 Traditional vs. Oracle Machine Learning/Predictive Analtyics Traditional Move the data Move the algorithms Simpler, Smarter Data Management + Analytics / Machine Learning Architecture 10

Rapidly Build, Evaluate & Deploy Analytical Methodologies Leveraging a Variety of Data Sources and Types SQL Joins and arbitrary SQL transforms & queries power of SQL Transactional POS data Modeling

11 Rapidly Build, Evaluate & Deploy Analytical Methodologies Leveraging a Variety of Data Sources and Types SQL Joins and arbitrary SQL transforms & queries power of SQL Transactional POS data Modeling Approaches Inline predictive model to augment input data Unstructured data also mined by algorithms Consider: Demographics Past purchases Recent purchases Comments & tweets Generates SQL scripts and workflow API for deployment

Oracle s Machine Learning/Advanced Analytics Platforms Machine Learning Algorithms

Data Scientists, R Users, Citizen Data Scientists BI Analysts, Managers Functional

Cloud Service Oracle Machine Learning Big Data Cloud ORAAH Machine Learning

Execution Database Cloud Oracle Machine Learning Database Edition Machine Learning ,

12 Oracle s Machine Learning/Advanced Analytics Platforms Machine Learning Algorithms Embedded in the Data Management Platforms Analytics Producers Analytics Consumers Data Scientists, R Users, Citizen Data Scientists BI Analysts, Managers Functional Users (HCM, CRM) Data Management + Advanced Analytical Platform Big Data SQL Big Data Cloud Service Oracle Machine Learning Big Data Cloud ORAAH Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed Execution Database Cloud Oracle Machine Learning Database Edition Machine Learning Algorithms, Statistical Functions + R Integration for Scalable, Parallel, Distributed, in-db Execution

13 Oracle s Machine Learning/Advanced Analytics Fastest Way to Deliver Enterprise-wide Predictive Analytics Major Benefits Data remains in Database & Hadoop Model building and scoring occur in-database Use R packages with data-parallel invocations Leverage investment in Oracle IT Eliminate data duplication Eliminate separate analytical servers Deliver enterprise-wide applications GUI for ML/Predictive Analytics & code gen R interface leverages database as HPC engine 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 Copyright 2016, Oracle and/or its affiliates. All rights reserved.

14 Predictive Appl: HCM Cloud Workforce Predictions Complete, Integrated, Embedded, Automated and Interactive Predictive HCM Solution Integrated data management + embedded predictive analytics Full 360 degree employee view Single source of HCM data data Interactive dashboards and What if analysis Customizable if desired to add input variables to predictive models Mobile + Oracle Cloud solutions Additional relevant data and engineered features Historical data Sensor data, Text, unstructured data, transactional data, spatial data, etc. Oracle Database 12c Assembled historical data Historical or Current Data to be scored for predictions Predictions & Insights 1

15 Why Oracle? Because that s where the data is! Larry Ellison, Executive Chairman and CTO of Oracle Corporation 15

16 Product Functionality Overview

17 Dilbert on Big Data 17

data Data analysts, data scientists & developers Drag and drop workflow, R and SQL APIs Extends data management

18 Oracle s Machine Learning/Advanced Analytics Fastest Way to Deliver Scalable Enterprise-wide Predictive Analytics Key Features Parallel, scalable machine learning algorithms and R integration In-Database + Hadoop Don t move the data Data analysts, data scientists & developers Drag and drop workflow, R and SQL APIs Extends data management into powerful advanced/predictive analytics platform Enables enterprise predictive analytics deployment + applications

Network Support Vector Machine CLUSTERING Hierarchical K-Means Hierarchical O-Cluster Expectation Maximization

seasonal Single, Double Exp Smoothing REGRESSION Linear Model Generalized Linear Model Support Vector Machine

Comp Analysis (PCA) Unsupervised Pair-wise KL Div CUR decomposition for row & AI ASSOCIATION RULES A priori/

SQL Aggregates A1 A2 A3 A4 A5 A6 A7 OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined OAA

etc, FEATURE EXTRACTION Principal Comp Analysis (PCA) Non-negative Matrix Factorization Singular Value

Tokenization and theme extraction Explicit Semantic Analysis (ESA) for document similarity STATISTICAL

19 Oracle s Machine Learning & Adv. Analytics Algorithms CLASSIFICATION Naïve Bayes Logistic Regression (GLM) Decision Tree Random Forest Neural Network Support Vector Machine CLUSTERING Hierarchical K-Means Hierarchical O-Cluster Expectation Maximization (EM) ANOMALY DETECTION One-Class SVM TIME SERIES Holt-Winters, Regular & Irregular, with and w/o trends & seasonal Single, Double Exp Smoothing REGRESSION Linear Model Generalized Linear Model Support Vector Machine (SVM) Stepwise Linear regression Neural Network LASSO ATTRIBUTE IMPORTANCE Minimum Description Length Principal Comp Analysis (PCA) Unsupervised Pair-wise KL Div CUR decomposition for row & AI ASSOCIATION RULES A priori/ market basket PREDICTIVE QUERIES Predict, cluster, detect, features SQL ANALYTICS SQL Windows, SQL Patterns, SQL Aggregates A1 A2 A3 A4 A5 A6 A7 OAA (Oracle Data Mining + Oracle R Enterprise) and ORAAH combined OAA includes support for Partitioned Models, Transactional, Unstructured, Geo-spatial, Graph data. etc, FEATURE EXTRACTION Principal Comp Analysis (PCA) Non-negative Matrix Factorization Singular Value Decomposition (SVD) Explicit Semantic Analysis (ESA) TEXT MINING SUPPORT Algorithms support text type Tokenization and theme extraction Explicit Semantic Analysis (ESA) for document similarity STATISTICAL FUNCTIONS Basic statistics: min, max, median, stdev, t-test, F-test, Pearson s, Chi-Sq, ANOVA, etc. R PACKAGES CRAN R Algorithm Packages through Embedded R Execution Spark MLlib algorithm integration EXPORTABLE ML MODELS C and Java code for deployment

20 Manage and Analyze All Your Data Data Scientists, R Users, Citizen Data Scientists Architecturally, Many Options and Flexibility SQL / R Boil down the Data Like Big Data SQL / R Object Store Engineered Features Derived attributes that reflect domain knowledge key to best models e.g: Counts Totals Changes over time 20

Oracle Advanced Analytics 12.2 Model Build Time Performance Unofficial T7-4 (Sparc & Solaris) X5-4 (Intel and Linux) OAA 12.2 Algorithms Rows (Ms) Model Build Time (Secs / Degree of Parallelism) Wow!

21 Oracle Advanced Analytics 12.2 Model Build Time Performance Unofficial T7-4 (Sparc & Solaris) X5-4 (Intel and Linux) OAA 12.2 Algorithms Rows (Ms) Model Build Time (Secs / Degree of Parallelism) Wow! That s Fast! Attributes Importance s / s / 72 K Means Clustering s / s / 144 Expectation Maximization s / s / 144 Naive Bayes Classification s / s / 72 GLM Classification s / s / 144 GLM Regression s / s / 144 In 24 hours, could build new predictive models for entire United States Population, for 400 attributes, 4 times! Support Vector Machine (IPM solver) s / s / 144 Support Vector Machine (SGD solver) s / s / 72 The way to read their results is that they compare 2 chips: X5 (Intel and Linux) and T7 (Sparc and Solaris). They are measuring scalability (time in seconds) with increase degree of parallelism (dop). The data also has high cardinality Copyright categorical 2016, Oracle columns and/or which its affiliates. translates All rights in reserved. 9K mining attributes (when algorithms require explosion). There are no comparisons to 12.1 and it is fair to say that the 12.1 algorithms could not run on data of this size.

1-Click, 100% Automated, SQL Predictive Queries Oracle s Machine

immediate machine learning model build AND model apply as a SQL

Clustering query Anomaly query Feature extraction query 1-Click

Automatically creates multiple anomaly detection models

22 1-Click, 100% Automated, SQL Predictive Queries Oracle s Machine Learning Accelerates New Possibilities Predictive Queries 1-Click immediate machine learning model build AND model apply as a SQL query Classification & regression Multi-target problems Clustering query Anomaly query Feature extraction query 1-Click Predictions! Automatically creates multiple anomaly detection models Grouped_By and scores by partition via powerful highly automated SQL query Copyright 2016, Oracle and/or its affiliates. All rights reserved.

23 Machine Learning Define a Business Problem Find Top Performers 2. Assemble the Right Input Data and Engineered Features Engineered Features Employee Variables Age Income Income Growth % over past n time periods # dependents Healthcare Marital Status Mobile spouse? Outside work interests Machine Learning/ Model Prediction Family Variables Y = F(X1, X2,.Xn)? Work Variables Years same manager % travel Environmental Variables Other local employers hiring? Benefits vs. local employers HCM Predictive Workforce already does this for you! Copyright 2016, Oracle and/or its affiliates. All rights reserved.

36 3

37 Build Predictive Models on an Attribute Oracle s Machine Learning Accelerates New Possibilities Machine Learning Model Function(X 1, X 2,.X) Y (LTV_BIN); Probability

38 Build Predictive Models on an Attribute Oracle s Machine Learning Accelerates New Possibilities Machine Learning Model Function(X 1, X 2,.X) Y (LTV_BIN); Probability

39 Build Predictive Models on an Attribute Oracle s Machine Learning Accelerates New Possibilities Machine Learning Models Function(X 1, X 2,.X) Y2 (BankFunds Y (LTV_BIN); Probability

40 Build Predictive Models on an Attribute Oracle s Machine Learning Accelerates New Possibilities Machine Learning Models Function(X 1, X 2,.X) Y2 (BankFunds Y (LTV_BIN); Probability

41 More Data Variety Better Predictive Models Engineered Features Derived attributes/variable that reflect domain knowledge key to best models 100% Increasing sources of relevant data can boost model accuracy Naïve Guess or Random Responders 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. 100% Model with 20 variables Model with 75 variables Model with 250 variables 0% Population Size

HCM Predictive Workforce Predictive Analytics Applications Human Capital Management Powered by OAA Oracle Advanced Analytics factoryinstalled predictive analytics Employees likely to leave and

43 HCM Predictive Workforce Predictive Analytics Applications 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 Employee Attrition Model: Y = Function(X 1, X 2,.X) Link to Oracle HCM on O.com HCM Predictive Workforce demo

44 Attributes Used in Workforce Predictions (can be extended) Age Amount of leave taken in the previous year Amount of sickness in the current year Amount of sickness in the previous year Average salary change Average time in each department Average time in each grade Average time in each job Average time in each position Average time with each manager Change in current performance Current appraising manager Current assignment status Current department Current enterprise Current grade Current job Current legal employer Current location Current manager performance rating Current or most recent manager Current performance rating Current performance self rating Current position Difference between manager rating and self rating Disabled Ethnicity FTE Gender Has a second passport Home city Home country Increase in sickness absences over previous year Increase in sickness over previous year Latest salary change Legislation Length of service Manager's performance Marital status Nationality Normal end time Normal start time Normal working hours Number of different departments Number of different grades Number of grade changes in the last 2 years Number of manager changes in the last 5 years Number of sickness absences in the previous year Number of stock options compared to others in the same grade Potential profit on stock Previous manager performance rating Ratio of vested to unvested options Reason for latest salary change Time in current department Time in current grade Time in current job Time in current position Time since last leave Time since last marital status change Time since last probation ended Time since last received options Time since last sickness Time since latest salary change Time until contract expiration Time until next salary review Time until the next performance review Time until work permit or visa expiration Time with current manager Tobacco user Total enterprise leave Willing to relocate domestically Willing to relocate internationally Worker category Worker is a rehire Worker is an employee Worker's performance compared to peers Worker's stock options compared to peers Copyright 2016, Oracle and/or its affiliates. All rights reserved. 44

45 Machine Learning & Advanced Analytical Methodologies Data Preparation & Adv. Analytical Processes Runs In-Data Mgmt Platform Additional relevant data and engineered features Historical or Current to be Oracle scored Database for predictions 12c Historical data Assembled historical data Predictions & Insights Sensor data, Text, unstructured data, transactional data, spatial data, etc.

CLAIMS_SET values ('PREP_AUTO','ON'); commit; begin dbms_data_mining.

47 Fraud Prediction Demo Automated In-DB Analytical Methodology drop table CLAIMS_SET; exec dbms_data_mining.drop_model('claimsmodel'); create table CLAIMS_SET (setting_name varchar2(30), setting_value varchar2(4000)); insert into CLAIMS_SET values ('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES'); insert into CLAIMS_SET values ('PREP_AUTO','ON'); commit; begin dbms_data_mining.create_model('claimsmodel', 'CLASSIFICATION', 'CLAIMS', 'POLICYNUMBER', null, 'CLAIMS_SET'); end; / -- Top 5 most suspicious fraud policy holder claims select * from (select POLICYNUMBER, round(prob_fraud*100,2) percent_fraud, rank() over (order by prob_fraud desc) rnk from (select POLICYNUMBER, prediction_probability(claimsmodel, '0' using *) prob_fraud from CLAIMS where PASTNUMBEROFCLAIMS in ('2to4', 'morethan4'))) where rnk <= 5 order by percent_fraud desc; Automated Monthly Application! Just add: Create View CLAIMS2_30 As Select * from CLAIMS2 Where mydate > SYSDATE 30 Time measure: set timing on;

Predicted Performance for Individual Worker Copyright

analytics Employees likely to leave and predicted performance Top reasons, expected

51 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 Link to Oracle HCM on O.com HCM Predictive Workforce demo

ML Model Deployment for Real-Time Scoring

Recommendations On-the-fly, single record

as age, 'Married' as marital_status, 250

52 ML Model Deployment for Real-Time Scoring Real-Time Scoring, Predictions and Recommendations On-the-fly, single record apply with new data (e.g. from call center) Oracle Cloud Select prediction_probability(clas_dt_1_15, 'Yes' USING 7800 as bank_funds, 125 as checking_amount, 20 as credit_balance, 55 as age, 'Married' as marital_status, 250 as MONEY_MONTLY_OVERDRAWN, 1 as house_ownership) from dual; Call Center Social Media Get Advice Branch Office R Likelihood to respond: Web Mobile

Information, Insights and Predictions Built-in Predictive HCM What if?

53 HCM Workforce Predictions vs. Traditional Aproaches Single Analytical Platform vs. Sum of Parts Oracle HCM Workforce Predictions (Single, Integrated Solution) Simpler, More Powerful Integrated Architecture Immediate Information, Insights and Predictions Built-in Predictive HCM What if? Analysis Traditional Approaches (SAS, SPSS, R, Data Scientist, Consultant) Data movement Data Duplication Slow Lacks security Sum of Parts at best Outer loop for Predictive HCM Application No interactive What if? possible Confidential Oracle 5

54 Machine Learning & Advanced Analytics Getting Started

Getting Started Oracle ML/AA Resources & Links Oracle Advanced Analytics Overview Information Oracle's Machine Learning and Advanced Analytics 12.2c and Oracle Data Miner 4.

Product Management Wiki and Beehive Workspace (contains latest presentations, demos, product, etc.

2 New Features YouTube video Library of YouTube Movies on Oracle Advanced Analytics, Data Mining, Machine Learning (7+ live Demos e.g. Oracle Data Miner 4.

) Overview YouTube video of Oracle s Advanced Analytics and Machine Learning Getting Started/Training/Tutorials Link to OAA/Oracle Data Miner Workflow GUI Online (free) Tutorial Series on OTN Link to

55 Getting Started Oracle ML/AA Resources & Links Oracle Advanced Analytics Overview Information Oracle's Machine Learning and Advanced Analytics 12.2c and Oracle Data Miner 4.2 New Features preso Oracle Advanced Analytics Public Customer References Oracle s Machine Learning and Advanced Analytics Data Management Platforms white paper on OTN Oracle INTERNAL ONLY OAA Product Management Wiki and Beehive Workspace (contains latest presentations, demos, product, etc. information) YouTube recorded Oracle Advanced Analytics Presentations and Demos, White Papers Oracle's Machine Learning & Advanced Analytics 12.2 & Oracle Data Miner 4.2 New Features YouTube video Library of YouTube Movies on Oracle Advanced Analytics, Data Mining, Machine Learning (7+ live Demos e.g. Oracle Data Miner 4.0 New Features, Retail, Fraud, Loyalty, Overview, etc.) Overview YouTube video of Oracle s Advanced Analytics and Machine Learning Getting Started/Training/Tutorials Link to OAA/Oracle Data Miner Workflow GUI Online (free) Tutorial Series on OTN Link to OAA/Oracle R Enterprise (free) Tutorial Series on OTN Link to Try the Oracle Cloud Now! Link to Getting Started w/ ODM blog entry Link to New OAA/Oracle Data Mining 2-Day Instructor Led Oracle University course. Oracle Data Mining Sample Code Examples Send an now to charlie.berger@oracle.com and my away message will send you many of these links Additional Resources, Documentation & OTN Discussion Forums 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 Oracle R Advanced Analytics for Hadoop (ORAAH) on OTN Analytics and Data Summit, All Analytics, All Data, No Nonsense. March 20-22, 2018, Redwood Shores, CA

57 Confidential Oracle 58

Machine Learning Data Management Platforms

Machine Learning Data Management Platforms Move the Algorithms; Not the Data! Charlie Berger, MS Engineering, MBA Sr. Director Product Management, Machine Learning, AI and Cognitive Analytics charlie.berger@oracle.com