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Business Data Lake best practices
OOP Munich, 2017-01-31
2Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
The speaker – Arne Roßmann
!  Part of Insights & Data team
•  Global team delivering around BI, DWH, Information
Strategy & Big Data
!  Working in Business Intelligence since 2008
!  Delivering as Big Data architect & Project
Manager at our clients
•  Defining processes
•  Creating architectures
•  Leading projects
!  Worked in many industries
•  Retail, Chemical, Financial, Logistics, Automotive, ...
3Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Capgemini’s Insights & Data Global Practice
With 15,000 experts globally, we are a recognized leader in information-led transformation
Capgemini’s Insights & Data Global Practice
Expertise in Big Data & Analytics Capgemini Solutions
!  Over 15,000 consultants
globally
!  Industrialized delivery
framework Next Gen Business
Insights Service Centre
!  CUBE lab on the cloud with
various demonstrations for
BI environments
!  Built-in Tools for interactive
agile BI and Devops
Partner Ecosystem
800+ Big Data & 400+ Data
Science Global Consultants
Customer Analytics
!  Segmentation &
Behavior Profiling
!  Behavior Propensity
scoring
!  Pricing Analytics
Marketing & Campaign
Analytics
!  Campaign
Recommendation
!  Cross Sell/Up Sell
!  Campaign
Measurement
!  Campaign Execution
Management
Operations Analytics
!  Sales/ Demand
Forecasting
!  Activity Based Costing
!  Call Center Analytics
Asset/ Equipment
Analytics
!  Warranty Analytics
!  Asset Performance
Monitoring
!  Predictive Asset
Maintenance
!  Insights from Connected
Equipment
Fraud Analytics
!  Fraud Scoring
!  Collusion Fraud
Identification
!  Fraud Framework for
Public Sector (Trouve)
Content Analytics
!  Text Mining Accelerators
!  Key Opinion Leader
!  Content Analytics for
Fraud Detection
Business Data Lake
offering
Data Warehouse
Optimization Solution
Strategic Alliances and
partnerships with major
vendors
Enabling Co-Innovation with
the CUBE lab
Experience in designing and
deploying big data analytics
solutions in a varied
ecosystems
4Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Table of Contents
!  Why the Business Data Lake works
!  Services your Business Data Lake should provide
!  Standardize, Industrialize and Innovate!
Why the Business Data Lake works
6Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Big Data creates opportunities but poses challenges as well
Where do
I start ?
“We know that Big Data can be
helpful but how do we quantify the
benefits and develop a
Business Case?”
“How do we know which Big Data
technology/platform(s) suits our
architecture and business
requirement? “
“How do I get all the unstructured data
(mainly images) out of my operational
processes, into an analytical
environment that allows me to
experiment with data?”
“Can we easily combine data from
multiple source systems into our Big
Data environment and visa versa?”
“Can I do it myself? What skills do I
need for Big Data? “
“How do I measure the effectiveness
or performance of my Big Data
initiative? How do I measure ROI?”
7Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Businesses are looking to close the gap towards ‘insight driven’
Have not completely
integrated their data sources
across the organization
79%
Scattered data lying
in silos across the
organization
Do not have well-defined criteria
to measure the success of
their own Big Data initiatives
67%
Absence of clear
business case
for funding and
implementation
Dependence on
legacy systems
for data processing
and management
Use cloud based Big Data
and analytics platforms
36%
Have either scattered pockets
of resources or follow a
decentralized model for
analytics initiatives
Ineffective
co-ordination
of Big Data and
analytics teams
47%
8Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
The Business Data Lake delivers what we need for the new data
landscape.
Govern
Where it
matters
Encourage
local
requirements
Distill on
demand
Store
securely
!  Focus on MDM
!  Enforce only when sharing
!  Treat Corporate as
aggregation of Local.
!  Let the business decide
what they need
!  Build from the bottom
!  Enable traceability to
source disposable data
views.
!  Store everything ‘as is’
!  Include structured and
unstructured data
!  Store it cheaply were
possible
!  Select only what you want
!  Business friendly tooling
!  Re-usable information
maps
!  Rapid change cycle.
9Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Business Challenges driving the need for BDL services
Business
Enablement
!  Achieve real-time
optimization of
business processes
through predictive
insights and
performance analytics
!  Enhance new services
and stay competitive in
the market
!  Be agile, get insights
fast
ControlControl
!  Ensure data security
and compliance with
EU data regulations
!  Enable up- and
downscaling
according to business
needs
ControlControl
!  Reduce costs
associated with the
governance and secure
storage of data
!  Control the costs of
running flexible data
services
!  Reduce Capex
Services your Business Data Lake
should provide
11Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Capgemini can help accelerate clients’ journey to Insights..
A cloud powered, big data & insights service; bring all your data in one place,
deliver insights at the point of action and generate differentiated business
value.
‘Software-
Defined’’, full
stack cloud
infrastructure
Flexible
‘Pay-as-you-go’
Commercial
Model
Secure
as a Vault
‘Ready to Harvest’
Sector & Domain
Insights
Modular
Hybrid & Elastic
powered by
‘Intelligent
Automation’
Get started quickly: with our platform , tools and expertise we can support you at any
level to manage your data and harvest insights
Your ‘Lab in the
Cloud’
!  Experiment
!  Hypothesize
!  Simulate
12Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
The BDL architecture we built for our clients
Pla$orm		
as	a	Service	
Insights Platform UX Portal
HTML 5, CSS, Angular JS
Big Data Lab
Dataset Library
Data Science Lab
Models Library
Insights Lab
Ready Insights
Common Services Common Services
Ingest
Algorithm
Library
Sector
Insight Labs
Smart
Insights 360
Catalog &
Provision
Meter&Bill
ResourceMonitor
Provision
ServiceCatalog
IoTFramework
AccessMgmt
KnowledgeBase
Helpdesk
RESTful	Web	Services	
Infrastructure	
as	a	Service	
Hybrid	Cloud	Extensibility	-	(Bosh,	CF)	CG-CSB,	Virtustream	
Storage	and	ParallelizaIon	-	EMC	Isilon		
Compute	&	Memory	-	EMC	VCE	
Big	Data	Suite	–	Pivotal,	Cloudera,	Hortonworks	
VMware,	Cortex	
Data	Management	–	InformaIca,	Talend,	HDF,	Apache	Nify	
AnalyIcs	tools		-	SAS,	Madlib,	RStudio,Spark	
Vmware	
Security	&	Governance	
RSA,	AD,	Knox,	Ranger,	Kerberos,	Atlas,	TDE,	W2W,	Metron,	
Falcon	
ITSM		-	BMC	Remedy	
•  Common Web UI and
UX architecture
•  Fully Virtualized
compute, storage &
Network
•  Intelligent automation
of provisioning,
process, service and
support orchestration
•  Modular Component
Architecture
•  Multiple points of
presence
•  Seamless integration
between on-premise,
private & public cloud
•  Proven reference and
component
architecture for on-
premise builds
•  Professional Services
teams to build full stack
•  Demo of full stack
•  Accelerated Partner
enablement
MD&LM
Environment
Hadoop	DistribuIon	–	Hortonworks,	Cloudera	
RE&D,	Dev	Ops	-	Cloud	Foundry,	Jira,	Jit,		
Application LayerInfra Layer User Access LayerSoftware & Services
VisualisaIon	–	Qlik,	Tableau,	SAS	VA,	D3,	High	Charts		
VisualisationVisualisation Self Service Insights
Capgemini	Private	Cloud	 On	Premise	Cloud
13Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
BDLaaS – illustrative example service Dashboard
14Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Standardize, Industrialize and Innovate!
15Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Big data processing is done in three different stages and we have to
cater to each stage differently
!  Continuously running
analytics processes
!  Trust in data quality
!  Service levels secured
!  Managed by IT
Operationalize
!  Store everything: internal
and external, structured
and unstructured
!  Store granular data
!  Minimal effort on IT
Load “as-is”
!  Agile and explorative way
of work
!  Self service
!  Fail fast
Distill on demand
Time
Stage
Actors
Paradigms
IT implements data
integration process for
production
Data providers and IT
provide and store data
Data scientists and
engineers explore and
analyze data
1 2 3
Allow creativity
Encourage collaborationEnsure Business Meta
Data & Data Catalogue
Enable Data Masking
Industrialize!
Examples of technical metadata
!  Path (folder location)
!  Filename
!  File type
!  File size
!  Date of ingestion
!  Technical Owner / Group
!  For HIVE:
!  Nr of records / lines
!  Column number
!  Column names if available
!  Column data types
!  Value distribution
!  Min/Max
Examples of business metadata
!  Project (possibly automatic)
!  Data set name
!  Logical description of dataset
!  Data owner/data stewart
!  Confidentiality classification
!  Line of business
16Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
Start using ELT tools now!
Need for more platform updates
Need for more denormalization
Need for more specialized
Know-How
" Abstraction layer to
Hadoop processing
engines
" Abstraction layer to
NoSQL & SQL
databases
" Standardized control
flows
" Availability of
developers
ELT Tools offer:
17Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
17Copyright © Capgemini 2016. All Rights Reserved
Insights as a Service – Analytics Cloud for Oil & Gas major
Well Health
Dashboards
Equipment Performance
Disaster Management
Supply Chain Analytics
Predictive Maintenancezz
z
Device Data
Driving behavior, GPS, diagnostics, etc.
Real Time DataSystem Data
Environment DataProject Data
• 10 data points per
sec
• 40 GB per field
• 5-6 GB per day
per well,
• 80TB Well data
year
• 24x7x365 monitoring
usage
• Real time charts of
streaming data
• Real time alerts
• Thermal
Visualizations
18Copyright © Capgemini 2015. All Rights Reserved
OOP MUC 2017 - Business Data Lake best practices
We helped customers getting to real value within 12 weeks from idea to
production.
1 3
a
5 6 7 9 11
Business
Insights Need
Integrate
DataSet
Model Build and
Training
Iterate and
Tune
Data
Exploration
Test Data
Science Model
Apply Data
Science
12
Business
Validation
Publish
Insights
Weeks
Business Problem
Identified
Business Value
Delivered
The information contained in this presentation is proprietary.
Copyright © 2015 Capgemini. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.
www.capgemini.com
About Capgemini
With more than 145,000 people in over 40 countries, Capgemini
is one of the world's foremost providers of consulting, technology
and outsourcing services. The Group reported 2014 global
revenues of EUR 10.573 billion.
Together with its clients, Capgemini creates and delivers
business and technology solutions that fit their needs and drive
the results they want. A deeply multicultural organization,
Capgemini has developed its own way of working, the
Collaborative Business Experience™, and draws on Rightshore®,
its worldwide delivery model
Learn more about us at www.capgemini.com.

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Business Data Lake Best Practices

  • 1. Business Data Lake best practices OOP Munich, 2017-01-31
  • 2. 2Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices The speaker – Arne Roßmann !  Part of Insights & Data team •  Global team delivering around BI, DWH, Information Strategy & Big Data !  Working in Business Intelligence since 2008 !  Delivering as Big Data architect & Project Manager at our clients •  Defining processes •  Creating architectures •  Leading projects !  Worked in many industries •  Retail, Chemical, Financial, Logistics, Automotive, ...
  • 3. 3Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Capgemini’s Insights & Data Global Practice With 15,000 experts globally, we are a recognized leader in information-led transformation Capgemini’s Insights & Data Global Practice Expertise in Big Data & Analytics Capgemini Solutions !  Over 15,000 consultants globally !  Industrialized delivery framework Next Gen Business Insights Service Centre !  CUBE lab on the cloud with various demonstrations for BI environments !  Built-in Tools for interactive agile BI and Devops Partner Ecosystem 800+ Big Data & 400+ Data Science Global Consultants Customer Analytics !  Segmentation & Behavior Profiling !  Behavior Propensity scoring !  Pricing Analytics Marketing & Campaign Analytics !  Campaign Recommendation !  Cross Sell/Up Sell !  Campaign Measurement !  Campaign Execution Management Operations Analytics !  Sales/ Demand Forecasting !  Activity Based Costing !  Call Center Analytics Asset/ Equipment Analytics !  Warranty Analytics !  Asset Performance Monitoring !  Predictive Asset Maintenance !  Insights from Connected Equipment Fraud Analytics !  Fraud Scoring !  Collusion Fraud Identification !  Fraud Framework for Public Sector (Trouve) Content Analytics !  Text Mining Accelerators !  Key Opinion Leader !  Content Analytics for Fraud Detection Business Data Lake offering Data Warehouse Optimization Solution Strategic Alliances and partnerships with major vendors Enabling Co-Innovation with the CUBE lab Experience in designing and deploying big data analytics solutions in a varied ecosystems
  • 4. 4Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Table of Contents !  Why the Business Data Lake works !  Services your Business Data Lake should provide !  Standardize, Industrialize and Innovate!
  • 5. Why the Business Data Lake works
  • 6. 6Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Big Data creates opportunities but poses challenges as well Where do I start ? “We know that Big Data can be helpful but how do we quantify the benefits and develop a Business Case?” “How do we know which Big Data technology/platform(s) suits our architecture and business requirement? “ “How do I get all the unstructured data (mainly images) out of my operational processes, into an analytical environment that allows me to experiment with data?” “Can we easily combine data from multiple source systems into our Big Data environment and visa versa?” “Can I do it myself? What skills do I need for Big Data? “ “How do I measure the effectiveness or performance of my Big Data initiative? How do I measure ROI?”
  • 7. 7Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Businesses are looking to close the gap towards ‘insight driven’ Have not completely integrated their data sources across the organization 79% Scattered data lying in silos across the organization Do not have well-defined criteria to measure the success of their own Big Data initiatives 67% Absence of clear business case for funding and implementation Dependence on legacy systems for data processing and management Use cloud based Big Data and analytics platforms 36% Have either scattered pockets of resources or follow a decentralized model for analytics initiatives Ineffective co-ordination of Big Data and analytics teams 47%
  • 8. 8Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices The Business Data Lake delivers what we need for the new data landscape. Govern Where it matters Encourage local requirements Distill on demand Store securely !  Focus on MDM !  Enforce only when sharing !  Treat Corporate as aggregation of Local. !  Let the business decide what they need !  Build from the bottom !  Enable traceability to source disposable data views. !  Store everything ‘as is’ !  Include structured and unstructured data !  Store it cheaply were possible !  Select only what you want !  Business friendly tooling !  Re-usable information maps !  Rapid change cycle.
  • 9. 9Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Business Challenges driving the need for BDL services Business Enablement !  Achieve real-time optimization of business processes through predictive insights and performance analytics !  Enhance new services and stay competitive in the market !  Be agile, get insights fast ControlControl !  Ensure data security and compliance with EU data regulations !  Enable up- and downscaling according to business needs ControlControl !  Reduce costs associated with the governance and secure storage of data !  Control the costs of running flexible data services !  Reduce Capex
  • 10. Services your Business Data Lake should provide
  • 11. 11Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Capgemini can help accelerate clients’ journey to Insights.. A cloud powered, big data & insights service; bring all your data in one place, deliver insights at the point of action and generate differentiated business value. ‘Software- Defined’’, full stack cloud infrastructure Flexible ‘Pay-as-you-go’ Commercial Model Secure as a Vault ‘Ready to Harvest’ Sector & Domain Insights Modular Hybrid & Elastic powered by ‘Intelligent Automation’ Get started quickly: with our platform , tools and expertise we can support you at any level to manage your data and harvest insights Your ‘Lab in the Cloud’ !  Experiment !  Hypothesize !  Simulate
  • 12. 12Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices The BDL architecture we built for our clients Pla$orm as a Service Insights Platform UX Portal HTML 5, CSS, Angular JS Big Data Lab Dataset Library Data Science Lab Models Library Insights Lab Ready Insights Common Services Common Services Ingest Algorithm Library Sector Insight Labs Smart Insights 360 Catalog & Provision Meter&Bill ResourceMonitor Provision ServiceCatalog IoTFramework AccessMgmt KnowledgeBase Helpdesk RESTful Web Services Infrastructure as a Service Hybrid Cloud Extensibility - (Bosh, CF) CG-CSB, Virtustream Storage and ParallelizaIon - EMC Isilon Compute & Memory - EMC VCE Big Data Suite – Pivotal, Cloudera, Hortonworks VMware, Cortex Data Management – InformaIca, Talend, HDF, Apache Nify AnalyIcs tools - SAS, Madlib, RStudio,Spark Vmware Security & Governance RSA, AD, Knox, Ranger, Kerberos, Atlas, TDE, W2W, Metron, Falcon ITSM - BMC Remedy •  Common Web UI and UX architecture •  Fully Virtualized compute, storage & Network •  Intelligent automation of provisioning, process, service and support orchestration •  Modular Component Architecture •  Multiple points of presence •  Seamless integration between on-premise, private & public cloud •  Proven reference and component architecture for on- premise builds •  Professional Services teams to build full stack •  Demo of full stack •  Accelerated Partner enablement MD&LM Environment Hadoop DistribuIon – Hortonworks, Cloudera RE&D, Dev Ops - Cloud Foundry, Jira, Jit, Application LayerInfra Layer User Access LayerSoftware & Services VisualisaIon – Qlik, Tableau, SAS VA, D3, High Charts VisualisationVisualisation Self Service Insights Capgemini Private Cloud On Premise Cloud
  • 13. 13Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices BDLaaS – illustrative example service Dashboard
  • 14. 14Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Standardize, Industrialize and Innovate!
  • 15. 15Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Big data processing is done in three different stages and we have to cater to each stage differently !  Continuously running analytics processes !  Trust in data quality !  Service levels secured !  Managed by IT Operationalize !  Store everything: internal and external, structured and unstructured !  Store granular data !  Minimal effort on IT Load “as-is” !  Agile and explorative way of work !  Self service !  Fail fast Distill on demand Time Stage Actors Paradigms IT implements data integration process for production Data providers and IT provide and store data Data scientists and engineers explore and analyze data 1 2 3 Allow creativity Encourage collaborationEnsure Business Meta Data & Data Catalogue Enable Data Masking Industrialize! Examples of technical metadata !  Path (folder location) !  Filename !  File type !  File size !  Date of ingestion !  Technical Owner / Group !  For HIVE: !  Nr of records / lines !  Column number !  Column names if available !  Column data types !  Value distribution !  Min/Max Examples of business metadata !  Project (possibly automatic) !  Data set name !  Logical description of dataset !  Data owner/data stewart !  Confidentiality classification !  Line of business
  • 16. 16Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices Start using ELT tools now! Need for more platform updates Need for more denormalization Need for more specialized Know-How " Abstraction layer to Hadoop processing engines " Abstraction layer to NoSQL & SQL databases " Standardized control flows " Availability of developers ELT Tools offer:
  • 17. 17Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices 17Copyright © Capgemini 2016. All Rights Reserved Insights as a Service – Analytics Cloud for Oil & Gas major Well Health Dashboards Equipment Performance Disaster Management Supply Chain Analytics Predictive Maintenancezz z Device Data Driving behavior, GPS, diagnostics, etc. Real Time DataSystem Data Environment DataProject Data • 10 data points per sec • 40 GB per field • 5-6 GB per day per well, • 80TB Well data year • 24x7x365 monitoring usage • Real time charts of streaming data • Real time alerts • Thermal Visualizations
  • 18. 18Copyright © Capgemini 2015. All Rights Reserved OOP MUC 2017 - Business Data Lake best practices We helped customers getting to real value within 12 weeks from idea to production. 1 3 a 5 6 7 9 11 Business Insights Need Integrate DataSet Model Build and Training Iterate and Tune Data Exploration Test Data Science Model Apply Data Science 12 Business Validation Publish Insights Weeks Business Problem Identified Business Value Delivered
  • 19. The information contained in this presentation is proprietary. Copyright © 2015 Capgemini. All rights reserved. Rightshore® is a trademark belonging to Capgemini. www.capgemini.com About Capgemini With more than 145,000 people in over 40 countries, Capgemini is one of the world's foremost providers of consulting, technology and outsourcing services. The Group reported 2014 global revenues of EUR 10.573 billion. Together with its clients, Capgemini creates and delivers business and technology solutions that fit their needs and drive the results they want. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business Experience™, and draws on Rightshore®, its worldwide delivery model Learn more about us at www.capgemini.com.