How Big Insights and Watson Explorer Raise New Abilities to HR Departments

How BigInsights and Watson Explorer

Raise new Abilities for HR Departments

(1240A)

Jean-Baptiste Martin, Capgemini

Laurent Sergueenkoff, IBM

October 26, 2015

2 Copyright © Capgemini 2015. All Rights Reserved

How BigInsights and Watson Explorer Raise new Abilities for HR Departments (1240A) | October 2015

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Agenda

HR Challenges

Capgemini’s People Analytics

Solution Architecture

Implementation Feedback

Benefits

Next Steps

HR Challenges



HR Challenges

How do I build an

efficient team to start

my project?

I have thousands of

candidates available;

how to find the right

one?

What skills is the

market looking for?

How should I train my

people to fill the gap?

How do I save time

and reduce costs in

my recruitment

process ?

How can I early

detect and coach my

best talents?

How can I find the

best projects for

employees currently

available?

How do I improve the

employability of my

employees?

To top it off, I need to get reliable answers faster!





+

Integration & Storage Analytics Engine Visualization

People

Management

External

Recruitment

Skill

Management 1 2 3

Big Data & Analytics HR Management Solution



A Little Bit of History

What were the needs?

Difficulties to match job descriptions and candidate profiles

Lots of customers said they did not find the right solution

on the market

Why using Big Data technologies?

Lots of unstructured data: CVs in various formats, emails,

external sources

Huge number of combinations between jobs and CVs

Thanks to the Capgemini / IBM Alliance, Capgemini team

met IBM team at Insights 2014 to discuss a business

case

Decision to go together as soon as possible



A Little Bit of History

Project started in November 2014

A first round of demos and presentations revealed a strong

interest for the solution

New use case ideas

Measure the gap between the employees’ knowledge and

the company strategy

Do employees skills follow a good trend ?

All the use cases have been developed or on-going

The product is a foundation

It can be adapted to the industry

It is evolving to follow technologies such as Spark




Introduction Video

Solution Architecture



People Analytics Architecture

HDFS

Store

WEX

AppBuilder

Analytics

Engine

Data

Reconciliation

Watson Explorer

Enterprise

Repo

Availability

Data

HR Data

Supply

and

Demand

Repository

BigInsights

WEX Engine

Data Indexing

Vis

ua

liza

tio

n

Data Access



BigInsights

5,000,000 x 5,000,000

Small Small

Big

Why a Big Data platform?

Small Data Storage, Big Data Processing

Why BigInsights?

ODPi

Big R

BigSQL



Watson Explorer

First thoughts: use Cognos BI?

Why Watson Explorer?

Search Engine Features

AppBuilder Freedom

WEX Content Analytics



Technical Architecture

Entry Level Configuration (0-300K documents)

Virtual Server

• Linux RHEL 6 64-bits

• 500 Gb Disk Space

• 64 Gb RAM

• 8 vCPU

1 node as Driver, 1 node shared with Watson Explorer

Business-oriented Capacity Planning

A document: CV-like content, job-like content

1 document ~400Kb

1 node per additional 300K documents

x 2

Implementation Feedback



Analytical Processing Considerations



Analytical Processing Considerations



Spark Migration Benefits

1,000 people

300

assignments

X

Standalone Scripts for Data

Processing

.sh

Big Data Standalone Server

Initially

R Pig Java

Duration: one night



Spark Migration Benefits

60K people

3,500

assignments

X

Unified Analytics Engine

Big Data Cluster (2 Nodes)

Finally Duration: 15 minutes



Spark Feedback

rdd.flatMapValues(x => x)

rdd.flatMapValues( new Function< List<Tuple2<Tuple3<String, Double, Double>, Tuple3<String, Double, Double>>>, Iterable<Tuple2<Tuple3<String, Double, Double>, Tuple3<String, Double, Double>>> >() { public Iterable<Tuple2<Tuple3<String, Double, Double>, Tuple3<String, Double, Double>>> call( List<Tuple2<Tuple3<String, Double, Double>, Tuple3<String, Double, Double>>> arg0) throws Exception { return arg0; } })

Using Java

Java 7 is... verbose

Same in Scala:

Object-oriented programming & parallel programming

Object serialization

MLlib vs. Custom Implementation



Playing with Watson Explorer (AppBuilder)

<% #erb ruby view if !params['jFilter'].nil? jParameters = JSON.parse(params['jFilter']); query =

query.where(field('x').is(jParameters['minGrade'])) query.entities.requesting(10).each do ...

Strong UI Customization Capabilities:

HTML5, CSS3, JS, JQuery, D3.js

Filtering with Custom Widgets

Crawling Data from HDFS

Benefits



Benefits for Business

Learn more about the

Benefits for HR during

Session #1066A

Improving HR

processes with

Advanced Analytics

and Big Data

For an HR manager

• Scale of time is definitively not the same

– It takes many hours for a human to read, analyze

and select profiles

– It takes seconds to minutes for a machine

• Completeness

– The machine will analyze ALL the profiles without

any discrimination

– A benchmark showed that the system proposed the

same profiles PLUS others that the HR manager

had not seen

• Extended scope

– The seeking scope is much larger: emails, external

sources

For the company

• It provides a global view of the employee skills



Technical Benefits

Scalability

Hadoop can scale by adding new hardware easily

Hardware cost stays reasonable

Capgemini and IBM are both members of ODPi

For better software integration and consistency

Next Steps



Product Roadmap

June 2016

January 2016

June 2015

* Developed in partnership with a final client

Team Staffing

People Retention

Skill Management

External Recruitment

Internal Mobility & Staffing



Technical Roadmap

Code migration to Scala

Partnerships on external recruitment (on-going

discussions with industry leaders)

Use of Watson services (speech recognition, language

translation,...)



Business Status

Several projects and pilots on-going for clients in

various industries, such as:

Capgemini Group

Car manufacturing

Travel and transportation

US Roadshow: Ask for a demo!



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