The document discusses best practices for business data lakes. It describes how business data lakes can help organizations address big data challenges by storing all data securely in its native format and enabling local business units to access and analyze the data. It recommends standardizing processes, industrializing data management, and innovating through a self-service approach to distilling insights on demand. Key services a business data lake should provide include governance, cost control, business enablement through predictive analytics, and agility.

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..
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Your ‘Lab in the
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! Experiment
! Hypothesize
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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
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Models Library
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ServiceCatalog
IoTFramework
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Storage and ParallelizaIon - EMC Isilon
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Data Management – InformaIca, Talend, HDF, Apache Nify
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• 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
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" Abstraction layer to
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" Abstraction layer to
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" 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
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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
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Collaborative Business Experience™, and draws on Rightshore®,
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Learn more about us at www.capgemini.com.