big data sharing experience - jacques wieczorek

Big Data sharing experience

NRB Mainframe day 2016

Jacques WIECZOREK, Head of BI/Analytics Solutions NRB 26/05/2016

Brussels

J.Wieczorek NRB Mainframe Day 2016


2. Goals

3. The Data

7. Big Data Hub with the Walloon Region

6. Strategy

8. Challenges

9. Conclusion

5. Definition of the Big Data concept

4. The context of Big Data

1. Introductory statement

Introductory statement


Goals


The Data


101101101

Data is based on :Some basics rules,taxonomy

Crossing the Data increasesthe knowledge

Out of context :Lost of itsMeaning,Lost of its value

Organizational framework of the Data :Who owns it ?Where are the Data ?Who updates it ?Data governance

OmnichannelsData contentCookies, Sessions time,..Mobile behaviorSocial MediaBehavior, groups

ID RegistrationEmails, Gender,Type of channelRegistration,…

E-catalogProductsWhish listsPrice segments…

Some features of the Data : New dimensions

A digital context



Digital transformation : we are all learning


A product/ a service experience

Customer experience is the new strategic battlefield

CustomerExperience

Use of Technology is exploding

Power of the Mass

The customer decides


We carry our consumers habits and expectations into any workplace !

B2CB2BG2C

Scope of the customer experience

Definition of Big Data



There is no universal definition of the concept of Big Data


Anyway there is a massive volume of both structured and unstructured data

Store

Capture

Store Treatment

Visualization

This huge volume is too big, or moves too fastor exceeds current processing capacity.


This explosive growth creates new opportunities : Customer experience

Customer centric : serving customers at every touchpoint

Marketing or content strategy to increase loyalty and decrease churn.

Product or services issues

Process gaps

Feature/ service requests

No Revolution


Big Data : tentative to define the concept of Big Data

BIGDATA

a set of activities that allows deeper and more complex treatment of datasets to generate the data asset into something of value with new enabling technologies


With Big Data you can unlock the value of hidden data?


What is hidden ?..

Label: connectedType :married



Label: connectedType : married

Label: shareholder

Are they potential conflict of interests ? :Direct conflicts ?Indirect conflicts

Which company could be involved ?Direct ? Indirect ?

Strategy



Customer phases

Discovery ProjectsStrategicfocus

Phase 1

Maintenance IOT

Phase 2 Phase 3 Phase 4


Other uses cases we are developping

FRAUDDETECTION

PREDICTIVEMAINTENANCE

To determine« fraud patterns » :

manually Automatically Ring Fraud Suspicious

behaviours…..

360° VIEW(Churn/loyalty)

To determine and to plan preventivemaintenance tasks

• To determine shared customercharacteristics :• Loyalty definition,• Churn understanding• Churn trends• Churn models• Recommandation system• Bundling based on

recommandations• ….


3 basic questions

What is the question you want to answer with big data ?1

2

3

Do you have the data to answer that question ?

If you could answer the question, could you use the answer ?


How to measure success ?

Success


4 major steps


Accroître Revenu

RéduireCharges

DuCapital

Accroîtrele Cash Flow

RéduireCoûts

Réduire Capital Utilisé

Réduire Coût du Capital

FondRoulement

Immob

BiensD’équipements

CoûtServices

Prix Ventes

VolumesVentes

FraisAdministratifs

CoûtsCommerciaux

Churn

Fraud

Predictive Maintenance

ROI to be associated with the use case


Vulnerabilities

To ensure the sustainability of the business by managing the risks of :

Compliance

Security

Privacy

Data Governance

Executive

Drivers


THE BIG DATA HUB WITH THE WALLOON REGION


DATACOMPETITIVENESS JOB CREATIONS COMMERCIALISATION

Allowing all companies of

the Walloon Region to get

access to one single entity

for any Big Data activity

the would like to start with

(R&D projects, Big Data

projects)

Getting the 6

competitiveness clusters

ahead of competition by

applying Big Data

techniques to create value,

to improve operations and

to make faster and more

intelligent decisions.

Securing Data Privacy for

some specific sectors like

the Healthcare sector and

the Space sector

Leading to job creation and

turning R&D outcomes into

production

Vision of the Walloon Region : sustaining the Marshall Plan


Major components of the Walloon Big Data hub

HardwareSoftware

Researchand Development

Commercialentity

Multi tenant

Dynamic sizing

Large portofolio of Big Data tools

Daas

Agreement with Cenearo

to get some extra capacity

The DGO6 ( one of the seven operational directorates (DGO) of the Walloon Public Service is the key policy-design and implementing body for regional research and innovation policy.

It also supports the

Walloon actors in

business networks.

Legal entity in the form of a SCRL

Provide competitiveness clusters with Big Data support for :

R&D projects,

Big Data projects


4 use cases have already been identified within the R&D projects

MEDICAL RESEARCHHEALTHCARE AEROSPACE ICT

Real time monitoring

of patients in intensive

care which will supply

a warning system for

doctors

Acceleration of

research results on the

genome and

identification of factors

promoting the

development of

cancers

Preventive

maintenance of test

benches for aircraft

engines

Preventive

maintenance to

anticipate failure to

hardware components

VIS

ION

DIMINUTION

Morbidity

Mortality

Healthcare

Costs

SAVINGS

Alarms

Help to

decision

DATA

INTERPRETATIO

N

BY

CLINICAL

ALGORITHM

LAYER

•Integrator

•Synthesizer

•Pertinent Filters

•Medical AlgorithmsBig Data

BIOCORDER

MOBILE

HEALTHCARE

CLINICAL

ASSISTANT

The DIM 3 Project



PIT EORegions !

Partners : Centre Spatial de Liège Ulg, Ecole Royale Militaire, I-Mage Consult, NRB

Services

Dynamic earth observation services by delivering geospatial information based on a regular period of observations.

This will help to determine any change in the the state of vegetation, landslide, mine shaft…


NRB benefits from 3 business models to sell some Big Data activities :

▪ NRB’s own business environment▪ Research Projects

▪ Commercial entity dedicated to the Walloon Region

Three channels to sell Big Data activities

Challenges



StatisticsSporadic

mistakeerror

Recurring Cognitive

Category of challenges


Confirmation bias

Simpson’s Paradox

mistakeerror

StatisticsSporadic Recurring

Confounding variable

Challenges


Homer Edward

Same name, different first names, different fates…

Simpson’s Paradox


Statistical indicators : Survival rate / Period : 10 days

Emergency Services : global indicator of the performances

Identical criteria for :

Hospital A

Hospital B

Additional Information :

Hospital Total Survivors Deaths SurvivalRate

Hospital A 1000 800 200 80 %

Hospital B 1000 900 100 90 %


Statistical Paradox : a trend that appears in different groups of data disappears or reverses when these groups are combined

ill

Wounded

?

Clarification :• mathematical points of view

• The number of deaths is low within the “ill” population at both Hospitals and Hospital B has more inpatients into this category

• There are more deaths within the “wounded”population and there are more inpatients into this category at the Hospital A/

• It is not correct to add up those categories

• Statistics – medical statistics point of view

• the severity risks factors are not similar for the two categories

• Prior to the measure a set of criteria should have been applied. Therefore this lack of selection degrades the quality of the observation.

.


Apophenia

Inference fallacy

Confirmation bias

Simpson’ s Paradox

Mistakeerreur

StatisticsSporadic Recurring Cognitive


listen to your intuition

Theater ticket + parking ticket = 1,1€

The Theater ticket costs one euro more

than the parking ticket

The Parking ticket

?

Conclusion



Take aways

o Having more data doesn’t susbtitue for thinking hard, recognizinganomalies and exploring deep truths,

o Elaborate your « use case » with a multidisciplinary team

o Don’t fall victim to SOS ( Shiny Object Syndrome ) => do POCS !

o Sherlock Holmes was a data scientist :

« my name is Sherlock Holmes . It is my business to knowwhat other people don’t know ».


Questions