Jean-Michel Franco Innovation Director

MDM and Big Data : the wining association

Copyright 2007, Information Builders. Slide 1

Business & Decision : a global Consulting and System Integration company

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Founded in 1992 Revenue 2012 : 221,9 M€

2 500 employees 16 countries

BI / EPM

BI & EPM Services Europe MarketScope

Digital Marketing

Interactive Design Agency Overview, Europe, 2013

Magic Quadrant for CRM Services

CRM

EIM

MDM / BRMS / Search / ECM Consulting, Change Management , enterprise

software design, training

CONSULTING

5 expertise recognized by global independent industry analysts

Business & Decision and MDM

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Master Data Management dedicated practice 80 consultants globally Strong market research and partnership relationship management Closely linked to other group expertise (CRM, BI, e-bus)

Proven Agile MDM approach, closely linked to the business with engagement on costs and time to deliver

Versatile consultants with BI and IT skills

50 % delivered through a fixed priced approach 90% of the business targeting large accounts End to end engagement Deep knowledge of technology solutions

MDM and Information

Management

specialist

Proven iterative

Design &

implementation

methodology

Innovation, engagement ,

expertise

Expertise across industries

and MDM domains

Know your customer

Expand your service portfolio

Value Your ecosystem

Are you ready to get value from your data assets ? lessons learned from Amazon.com.

Source : Faber Novel

Some answers are in your data

– if only you could take advantage of them

Knowledge is Profit

• Communication and global sharing • Mutualization • Centralization/ federation and collaboration •Horizontal

Organization • Transparency

From data retention to data sharing New organizational and technological paradigm

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Knowledge is Power

• Retention and data silos

• Heterogeneity and « best-of-breed »

• Decentralization et autonomy • Vertical

organization • Opaqueness

Business needs are becoming more and more precise and urgent

Leveraging your data assets are a must, not an option,

to tackle current Business challenges

Time to market

IT system convergence & consolidation

Customer Knowledge

Information Hub

Business Glossary

Master data Repository

Governance, Risk and Compliance

Extended enterprise

Data Quality

Integration

Deinterleaving of IT systems for deregulated

markets

From data integration to Information Governance

From a siloed, IT driven model (Data Management)…

Métiers

…to a federated, and shared responsibility model

(Information Governance)

IT Lines of businesses

Business define their need and use information within enterprise applications

IT designs, implements, runs and manages

Ongoing conflicts on data quality and relevancy, lack of autonomy, slow time to market...

Line of businesses define their needs, administrate the information, document them, sometimes mashes them up and contribute to their relevancy and maintenance

IT accompanies, controls, rolls out, delivers “as a service”, secure and manages

Master Data Management 101

Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications.

The Master Data Management aims to develop the processes, organization and tools to collect, reference, manage and share the Master Data and links between them across organizations, people, processes and systems

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Clients

Citizen

User

Supplier

Employees

Partners

Products

Services

Assets

Rates

Points de

vente

Les

Lieux

Entre-

pots

Charts of

accounts

Operational and

legal organization

Territories

Countries

Real estate

Stores

Warehouse

Business rules

Configurations

Standard

codifications

SLA

The party/

persons The products

The

organizations The places

The Shared

assets

Subsidiaries

33% 44% 3 % 21 %

MDM isn’t self sufficient : disciplines of Enterprise Information Management

Document, eliminate

data redundancies

Improve and certify data quality and relevance

Transfer information,

secure it, trace it

Expose the information and

make it accessible « as a service »

Master

Data

Management,

Meta Data

Management

Data Quality

Information

Governance

Enterprise

Information

Integration,

Info Lifecycle

Management,

Data Loss

Prevention

Data

Services

SOA, enter-

prise search,

BI,

Enterprise

portals

Transfer information,

secure it, trace it

Customer_Id First name Name Product Supplier Date Amount

92584789 Ann B. TXF98 Dell 24/12/2013 650 $

92584789 Ann B. AXC54 Maped 24/12/2013 2,44 $

92584789 Ann B. TRE56 Playmobil 24/12/2013 129,36 $

….

What are the data types to consider ?

Transactional data

Analytical data

use

Master and reference data

Meta Data

Describe

Transactional data

Master Data

Meta-data

Data types :

Example:

Analytical data CLV RFM scoring

From data integration to information governance : Where to start?

Design the platform

Define the roles Engage the programs, domains per domain

Product

Customer

Organization

Sites

Platform design

-> Needs assessment workshop

-> Proof of concept -> Roadmaps and budgets

Orga. blueprint

-> Information Governance competency center

-> Data Stewardships -> Service Center

Roll out « Fast delivery » -> Iterative modeling -> Data mappings -> Data quality maps

Big Data : définition

Big Data is high volume, high-velocity and high-variety information assets that demand cost-effective innovative forms of information processing for enhanced insight and decision making

From “the 451 Group” et Gartner Source : Wall Street Journal

Inspired by Wikipedia

“The challenges include capture, curation, storage, search, sharing, analysis and visualization..” (wikipedia)

Big Data is the long tail of information management Po

pu

lari

ty

Available information

“Today, we sold more books that didn’t sell at all yesterday than we sold today of all the books that did sell yesterday”

(amazon.com, via Josh Petersen & Wikipedia)

BI as we know it - Information sourced from internal IT Systems - information provisioned in batch mode - Static information modeling

BI as we’d like it to be BI as we know

+ externally sourced data + « just in time » data + un-structured, semi-structured data + information as it comes (schema less)

Data Warehousing on steroids for better pricing, planning and customer facing policies

Retailers pioneered Enterprise Data Warehouses, especially for market basket analysis.

But retailers are now pressured to get more value from their data assets, to deepen and sharpen analytical capabilities and make them « actionable » .

Dynamic and micro-segmented pricing policies

Personalization of the offers for loyalty programs

Adjustment of offers to demand by locations

Consistency across channels (e-commerce, stores , drive)

Transparency for supply chain efficiencies and superior customer services

Real time information on water flows and quality

A value added service for consumers and institutions

Detection of leaks as they occur along the network and at the end of the chain

A common engagement between supplier and customers in terms of sustainability

Automation of the collection process for billing

(*) Source : SIA conseil

In France, 25% of the water that flows into the distribution networks is lost due to leaks or frauds ; This accounts for 2,4 billions Euros per year. (*)

Digital channels and internet of objects open new opportunities to bring transparency into the supply chain, and deliver superior customer service

Innovation in insurance industry and agritech Innovate with data centric new offers

A start-up to manage risks and insure farmers through online services that predicts how climate affects crop yield and personalized insurance offers.

A predictive platform that combines hyper local weather data with agronomic yield data down to the field level, all undergirded by weather simulations.

Trusted advizorship through online personalized services to help farmers better predict manage the climate conditions

Services can be deployed globally without limits, allowing to tackle new markets

Claims management processes fully automated from observation to payment

Huge opportunities to transform best practices in agriculture and climate management

* acquired by Monsanto on October 2013 for 950 millions $

Innovating in the insurance industry : Fraud Management

Apply the principle of Credit Scoring for claims management.

Integrate unstructured and semi-structured data to highlight inconsistencies in claims declarations.

Push the analytics on the field, close to the customer and when an where the claim is declared .

Success rate of investigations : from 50 to 85%

25% of claims are closed immediately at the first step, against 4% before -> better service for honest customers

Scoring drives the claims process and improves its efficiency (Actionable analytics)

Capture and share unstructured data

Documents, rich content… Public data sourced from

social networks and internet

Create new source insights through new data flows

Sensors, Internet of things External data, shared

data, open data

Extend the founding principles of Data Warehousing and Information

Management for more:

Immediacy Precision Agility

Data Warehouse

On line transac-tional processing

Business Intelligence a

analytics

Big Data

Elevating the good old Data Warehouse with Big Data : Searching for your « long tail »

Big Data by industry and by activity

IBM : the real world use of Big Data

What do you need to manage your Big Data?

MDM and Big Data : The wining association

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Why Big Data needs MDM?

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Entity extraction

Data Quality management

Reconciliation with master data

Data enrichment

Customer_Id

First name Name Product Supplier Date Amount

92584789 Ann B. TXF98 Dell 24/12/2013 650 $

92584789 Ann B. AXC54 Maped 24/12/2013 2,44 $

92584789 Ann B. TRE56 Playmobil 24/12/2013 129,36 $

….

Example : Digitalizing the ordering process to Santa Claus

Why MDM needs Big Data ? Ex.: From Customer Data

Integration to an active and real time 360° Customer View

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Master Data

Transactional Data

Analytical data

Customer Journey Data

Interactions

Customer Data

Platform

Contact Center

Face to face (stores, agencies…)

SMS/Mail/Chat…

Mobile Applications

Web Site

RO

I

3rd

par

ty

Dat

a P

latf

orm

*Source : H Sorensen

Innovation in the hospitality industry: real time recommendations

Improve click rate (+43%) and transformation rate

Ability to test new offers, and to stop or improve them as soon as needed

Ability to listen and react to promoters and detractors in social networks

Personalized offers and personalized interactions

Federation of customer knowledge across brands to adapt to organizational changes

• From attention to intention economy

• Test offers and challenge their efficiency on an ongoing basis

• Provide a consistent quality of service across channels

• Better manage recommendations across the brands, together with interactions with customers, promoters, detractors…

• Acquire/Enrich Customer knowledge

• Recommend the next best offer according to the context

• Manage end to end purchasing journey from intent to payment

• Monitor real time the relevance and success of offers

Innovation in the banking industry: Multi channel customer journeys

Personalized interactions with unknown internauts based on their click stream

Personalized interactions with known customers based on their profile and current /past on line and offline journey

Ability to track the timeline of customer interactions both offline and online

Definition of new customer segments based on analytics around customer journeys

Next step: Towards « predictive/prescriptive apps » : Next generation of apps that can anticipate user need and recommend

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