cwin17 san francisco-thomas dornis-2017 - data concierge-the foundation of a digital business

Data Concierge –

The Foundation of a Digital Business

Thomas Dornis

San Francisco, CA – December 7th 2017

#CWIN17

CWIN San Francisco 2017 – Data Concierge| December 7th 2017

Copyright © 2017 Capgemini. All rights reserved. 2

Table of Contents

Introduction/ Insights Drive Organization

Data Concierge Approach

Balancing Business Value with Industrialized

Capabilities



Becoming an Insight Driven Organization – Intelligent Enterprise

A fact-driven, highly industrialized quick-scan of your insights & data portfolio, giving you all you need to

make informed, value-driven decisions about the next steps of your insight-driven journey.

A high-speed, metrics-driven 6-12 weeks intervention that delivers

a business case, to-be design and roadmap for key portfolio

areas:

BI Modernization

Business Insights Service Center/ Data Concierge

Big Data/ Data Science

Cognitive & AI

Sector and Domain Analytics

What We Do

The Value

Client sees many opportunities to leverage insights & data, for

example around Big Data, Modernizing the BI Landscape, creating

advanced analytics and exploring Cognitive & AI

Client has trouble in making decisions, being hesitant about the

financial impact, the technology choices, the to-be design and

most feasible next steps to make

Challenges & Opportunities

Multiple years of tried & tested approach, delivering solid results in

a short timeframe, facilitating decision-making

Dedicated and specialized Center of Excellence in Bangalore

Collaborative process, involving all key stakeholders from day 1

Tools-supported, compelling visual report outs



Key transformation challenges for large scale data & analytics platforms

End user profiles/ requirements vary widely across the organization

Demand for new data assets become intolerable in a classic governance set up

User stories backlogs are getting longer and longer even when employing Agile methods

Deploying new services is taking too long

New tools & analytical techniques proliferate

DATA CONCIERGE

Industrialize and automatize data provisioning processes as much as possible

Provide a simple, business-oriented information catalog of all data assets available

Provide a simple and managed way for business users to go “self service” where possible

Use intelligent processes for proactive optimization & recommendations



Compressing the time to value, standardizing the cost to insight

Business Information Catalog Services

Repository, search and recommendation services for business meta-data

Data Operations Services

On-going management and support of the data assets including optimization, quality and governance

Ingestion Services

Loading data in appropriate perimeter with corresponding SLA and on-demand / self-service features for the business

Distillation Services

Structuring and providing the business with the information they need in the right view

Data Science and Analytics Services

A bespoke service for data science & analytics with multiple insights delivery models

Use Case Catalog Services

Repository of solutions/ use cases that have been implemented with business value and impact to bottlers

“Art of the Possible”

Industrialized

Automatized

Intelligent

Data Concierge Framework



Data Operations

Services

Data Concierge: Business Information Catalog Services

Business description of datasets, structures & services

Through a web-based portal, users can search for data assets within the lake, and use

recommendations provided by the tool; shopping cart approach for data assets

Communicate data assets characteristics

Ownership – IT & Business champions in charge of the data assets & contact info

Perimeter & SLAs – industrial/certified, experiment, self-service (shareable)

Access – which user population have access to this data asset, type of access

Current status of accessibility/usability within the lake

The data lake governance instances periodically review and curate the additions & modifications

made to the catalog

Curation of data assets

Governance of self service & experiment perimeters – discard data assets when initiatives are finalized

Major communication tool to support user adoption

Ingestion

Services

Distillation

Services

Data Science

& Analytics

Services

Business

Information

Catalog Services

Use Case

Catalog Services



Use Case

Catalog Services

Data Concierge: Data Operations (Governance) Services s

Intelligence behind the Data Concierge, masking complexity to the user

• Logs data assets search requests by user population

• Builds recommendations for data assets searched and used by similar populations of users

• Manage access rules between stakeholders (sharing of information, use etc.)

• Logs access to datasets, structures & services, distillation & transformation processes

• Detects anomalous search & access behaviors

• Apply quality & governance rules for data assets, structures & services

• Publish data assets and structures to perimeter, deploys services

• Enable & publish data lineage for each service

• Propose schema/structure optimization within a Data Hub/ Business Data Lake structure

• Cluster performance monitoring and optimization recommendations

Ingestion

Services

Distillation

Services

Data Science

& Analytics

Services

Business

Information

Catalog Services

Data Operations/

Services



Use Case

Catalog Services

Business

Information

Catalog Services

Data Operations

Services

Data Concierge: Ingestion Services

“Industrialized” Ingestion Services

Support multiple modes of ingestion:

• Real-time streaming

• Micro-batch

• Batch

• Replication

Govern choice of software vendors: Data Integration Platform, API

Services, Open Source tools

Service Ticket driven approach

Users request a system and data asset that they didn’t find in the catalog

They receive a defined response time for loading

Stage Key Data Sources

80/20 Rule: 80% of analytics requirements are driven by 20% of data

Stage common/ most used data sources to “seed” the data lake

Ingestion

Services

Distillation

Services

Data Science

& Analytics

Services

Ing

es

tion

Se

rvic

es

Events

Web API

File

App API

Adaptor

RDBMS

Stre

am

ing

Ba

tch

SO

A/E

AI

CD

C

Te

ch

nic

al M

eta

Data

Au

dit

Da

ta L

ak

e



Use Case

Catalog Services

Ingestion

Services

Business

Information

Catalog Services

Data Operations

Services

Data Concierge: Distillation Services

Conversion of RAW data into usable data

Users request data assets to be converted into SQL data

stores with the right view for their needs

Users can also request Sandboxes for investigation and

analytics

Users can also request Excel and interactive reports

dashboards

Users receive a defined response time for deployment

Incorporate MDM and X-Ref data to create single

views of given domains (re-usable components)

Aggregate massive data sets down to manageable

results volumes

Includes the provisioning, and de-provisioning of

distillations on an automatic or scheduled basis

Distillation

Services

Data Science

& Analytics

Services

Master Data &

X-refTransformation

Aggregation

Data Lake

Distillation Layer

Usage Layer

Extraction

SQL SandboxSQL Excel

Provisioning



Ingestion

Services

Distillation

Services

Business

Information

Catalog Services

Data Operations

Services

Data Concierge: Data Science & Analytics Services

Question-based Data Science

“Can we improve the forecast for next years sales? (12+ month view not in SAP/ APO?)”

“What are the primary drivers for missed deliveries?”

“Can we predict factory downtime?”

Multiple delivery modes available depending on complexity and business users’ autonomy

Users can request to be fully autonomous, or work in integrated team, or request a fully delivered service

For an integrated team or fully delivered service, a CoE Data Science team can work collaboratively to define the problem space, data requirements and definition of the outcome required

A fixed price is then provided for the ‘proof of value’

Once the model has been proven it can then be industrialized via the Ingestion and Distillation Services

The different delivery modes can be used as a framework to progressively ramp up the end users on the new system

Enabling new data usages and new tools:

• Initial use cases are delivered in integrated mode; formal delivery by CoE or similar structure

• Over time, stakeholder may deliver their own use cases

Use of data lab approach and exploration to help users get familiar with the data assets available and the functions of the new system vs. legacy

Data Science

& Analytics

Services

Use Case

Catalog Services



Data Concierge – Use Case Catalog

Ingestion

Services

Distillation

Services

Business

Information

Catalog Services

Data Operations

Services

Data Science

& Analytics

Services

Use Case

Catalog Services

Existing Use Cases New Use Cases+

Improve

Insights Catalog

Industrialize

PoC

Filter and Eliminate

Scale & Communicate



Data Concierge – Use Case Catalog

Advanced Analytics/ Data Science Example (CPG)

Connected Assets & Service

Recommendation

• Analyze customer usage

pattern

• Recommend optimal services

offer

• Real-time Asset control

Product Design Analytics

• System Reliability Modeling

• Intelligent target setting &

allocation

• Cost & Weight Analytics

• Approximate models using

CAE/Test Data

• Product BenchmarkingEn

gin

eeri

ng

Dir

ecto

r

Supplier Risk Analytics

• Supplier quality analysis & Risk

Driver Identification

• Quality & Risk Scoring

• Rationalization & optimal

selection

En

gin

eeri

ng

Dir

ecto

r

Factory Analytics

• Machine Performance

& Control

• Energy Consumption Analysis

• Predictive Machine

Maintenance

• Stochastic demand & supply

planning

Head

-M

an

ufa

ctu

rin

g

Asset Performance & Control

• Performance Analysis

• Segmentation

• Adaptive Control Limits

• Real-time monitoring

Head

-O

pera

tio

ns

Predictive Maintenance

• Correlation of events/usage

to failures

• Root cause analysis & driver

identification

• Failure Prediction &

Recommendation Explore

failure anomalies

Head

-O

pera

tio

ns

CM

O

Advanced Planning &

Scheduling

• Analyze disruptions &

operational impact

• Stochastic demand & supply

planning of resources (e.g.

assets, manpower, services,

etc.)

Head

-O

pera

tio

ns

Service/ Issue Analytics

• Financial budgeting & reserving

• Claims & supply side

optimization

• Recall & product improvement

modeling

• Coverage & pricing strategy

formulation

CM

O &

CF

O

Service Optimization

• Dealer/service provider

performance analysis

• Predict the profitable customers

who may sign/renew services

contracts

• Service price optimizationC

MO

• Data Science

“Applications” to

address specific use

cases

• Contributions by entire

system:

• Business Units

• Corporate

• CoE

• Vendors/ Integrators

• Shared Service manages

the industrialization/

scaling

• Apply when ready –

driven by stakeholder

maturity

Development Approach:



Balancing Business Value & Industrialized Capabilities

End State

Quick Wins

Ideal

Architecture

Corridor

of balance

Bu

sin

es

s S

erv

ice

Va

lue

Capability

Value

Score-

carding Catalog

of Services

Training

Organisation

and Roles

rou

te r

eq

uest

Customer

Business Partner

Engagement Support

Engagement

Core

Advanced

Custom

Interpretation

Internal

delivery

Walk up or Ad-

Hoc

Engagement

Walk up

Template Capability Centre

Person/screen

Capability Centre or Cluster

Capability Centre or Cluster

Brief

Demand Management Supply Management

Brief

EngagementEngagement

Projects EngagementProject Plan

Data &

Technology

Allocation

Resource

Allocation

Governance

Delivery

Prioritisation

En

ter

into

dem

an

d b

acklo

g

Service

SelectionBrief Type Delivered By

Business Question

DefinedDelivery Plan

Delivery Processes

SUBSCRIPTION

PAY-AS-YOU-GO

PAY FOR FLEX

RESOURCES

Funding



Thank You!

Phone: +1 (520) 661-7333

[email protected]

Thomas Dornis

NA Leader – Information Strategy

Insights & Data

Speaker 1

Photo



Appendix



Data Concierge: Services Mapped to the Data Hub/ Business Data Lake

Architecture

Data Lake

Distillation Layer

Usage Layer

ODS

Applications

Analytics & Data Science

Industrial, certified

Perimeter

Experiment

Perimeter

Self service

Perimeter

Business Information Catalog

Op

era

tion

s

MDM Transformation Aggregation Transformation

Aggregation

Transformation

Aggregation

Governance

Go

ve

rna

nce

Co

rpo

rate

vie

w

Lo

ca

l

vie

w

.. Sa

nd

box

sp

ace

N

Sa

nd

bo

x

sp

ace

1

.. Sa

nd

box

sp

ace

N

Sa

nd

bo

x

sp

ace

1

Sources

Ingestion Services

Distillation Services

Data Science

& Analytics Services

Business Information

Catalog Services

Data Operations

Services

Data domainsData domains

Data domainsData domains

Data domainsData domainsData domains

Use Case

Catalog Services

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