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1Copyright © Capgemini 2017. All Rights Reserved
Big Data Analytics & Modernization with Data WARP and BAMA
2Copyright © Capgemini 2017. All Rights Reserved
Theo Elzinga
Global Enterprise Architect
Co-author of the Open Business Data Lake Standards
TOGAF certified
Author The CORA model
http://www.coramodel.com
3Copyright © Capgemini 2017. All Rights Reserved
We live in a world of connected people and things, where the data & analytics platform should provide data driven insights in many different ways …
FOUR DOMAINS
in which data-driven insights
are changing businesses
Information System
Understanding how
processes are performing
and where optimisation is
needed, predicting
process failure or leaks
(e.g. fraud detection,
operator room of the
future).
Customer Experience
Understanding and
segmenting customers
based on behavior
(e.g. assets, web
transactions, call centre
logs, social media).
Ecosystem
Explore business areas to
connect with partners
which were unknown by
harnessing new data
sources (e.g. open data,
documents, web logs)
Internet of Things
Monetize on the data
itself, resulting in new lines
of business by connecting,
analyzing and control of
physical assets (core and
operation Technology
systems).
Source: Gartner – Building a Digital Business Technology Platform
Employees Customers Partners Things
4Copyright © Capgemini 2017. All Rights Reserved
…in order to reach potential benefits and creating direct business value.
Cost reductions due to data rationalization (i.e. less duplicate data instances)
Cost reductions due to with less complexity (i.e. smaller server park)
Cost reductions due to better infrastructure (i.e. lower licensing cost)
Reduce the TCO of your data environment
Possibility to leverage structured, semi structured and even unstructured data
Less time needed for gathering information for decision making
Improve your decision support
Improved agility & time to market
Better decisions enabled by better data quality and data harmonization
Increased return on existing investment
5Copyright © Capgemini 2017. All Rights Reserved
However most current data & analytics platforms are slowing down business, financial and architecture effectiveness….
Business perspective
No alignment with information strategy, limited
support of business use cases and lack of agility in
development of new insights
Financial perspective
Escalating Total Cost of Ownership (TCO)
because higher volumes of data necessitate
upgrading existing data warehouse where licenses
are priced according to volume
Architecture perspective
Complexity and immaturity of the current data &
analytics platform to support any data insight
strategy end-to-end
Challenges
Business
perspective
Financial
perspective
Architecture
perspective
6Copyright © Capgemini 2017. All Rights Reserved
…and restricting the combined application of different insight-driven data strategies.
What
happened?
Why did it
happen?
What might
happen?
What will
happen?
Str
ate
gy
De
scriptive
Pre
dic
tive
Pre
scriptive
Prescriptive
Predictive
Simulation/optimization/
cognitive analysis
Machine learning/exploration
causation analysis
DescriptiveReporting (aggregation) analysis
Data
An
aly
tics
Data
Scie
nce
Reporting (root/cause) correlation analysisDiagnostic
Insights
Dia
gn
ostic
Customers
Partners
Employees
Things
7Copyright © Capgemini 2017. All Rights Reserved
In other words, to move towards an optimized data & analytics platform, there is a clear need for a roadmap.
‘Good enough’ ‘Competitive’
Domain Technology Data Foundation Data Analytics & Data Science
Go
vern
an
ce &
Org
an
izati
on
‘Sub-optimal’
Bu
sin
ess In
form
ati
on
A
rch
itectu
re
‘World Class’
Establish BI program
governance framework
Create end-to-end data
foundation view
Create data discovery
environment
Extend current
KPI-portfolio
Assess self-service
requirements
Explore data lake (pattern
finding)
Define metadata/ontologies
Define future application deployment
models
Evaluate employee facing processes
Define Data Reporting & Visualization
Define domain specific business
use cases
Improve collaboration
between Business and IT
Establish data lake as data
foundation layer
Establish change
management
Define/ Tailor agile program delivery
framework
Establish an information
strategy
Define Business priority ,
stakeholders and ownership Enable Process
Innovation Chain & Dev Ops
Integrate data lake with
existing data platforms
Introduce business
stewardship
Define search criteria
Define data set ingestion
topologies
Align data quality with insights requirements
Integrate ML in
domain pocesses
Investigate need for cognitive analysis
Create application
landscape incldata flows
Assess future timeliness of
data (batch vs realtime)
Integrate data lake with existing technology platforms
Define application
roadmap
Implement data quality
technologies
Implement API-repository
Data & Analytics
platform
No limit to volume
No limit to structure
No limit to analyzing
No limit to timing
Design interface upgrade plan
Invest in Data Scientists
Introduce Machine learning
capability
Understand the data needed to provide
domain specific analytics
8Copyright © Capgemini 2017. All Rights Reserved
Data WARP is a short assessment project to define a data & analytics platform roadmap well fitted into the overall process & IT architecture….
9Copyright © Capgemini 2017. All Rights Reserved
… allowing IT to build the supporting technical data capabilities…
10Copyright © Capgemini 2017. All Rights Reserved
The assessment consists of four phases…
Phase 1 − Week 1 Phase 2 − Week 1-2 Phase 3 − Week 3 Phase 4 − Week 4
To
ols
& A
rtifac
tsA
cti
vit
ies
Kick-off meeting to agree on scope(relevant lenses) and
deliverables and identify stakeholders
Gather domain data (Customer, Employee, Partner, Things) with
regards to the current status of the digital
business
Analyze domain data and assess maturity against the data &
analytics platform capabilities
Create data & analytics platform heatmap & optimization roadmap,
review the results and discuss next steps
Scope &
deliverables
Data gathering
- Desk research
Data gathering
- Workshops
Data gathering
– Review
Deliverable
preparation
Review
deliverables -
next steps
Heatmap
Optimization
roadmap
Maturity
assessment
Score
interpretation
ScanPlot Craft Solve
Relevant
lenses
Stakeholder
identification
11Copyright © Capgemini 2017. All Rights Reserved
Non-validated performance as input for the workshop where we define your plateaus
Minimum
Viable
Product
Additional
Sprint(s)
…resulting into a heatmap and a platform optimization roadmap.
Additional sprints might be needed depending on the requested input:
information strategy, business use cases, application landscape, reference
architectures, standards, principles, etc.
Sub-optimal GoodEnough Competitive World-class
Domaintechnology Insightsaredomain-
specificandmainly
createdwithinthe
employeeand
Insightscreationis
extendedtothe
partnerandthings
domain,butmainly
Cross-domaininsights
arecreated,however
limited.
Cross-domaininsights
creationisfullycross-
domain.
Datafoundation Domain-specificand
staticdatamodels,
limitednumberofdata
sources,limiteddata
cleansing,data
integratedintodata
warehouse,datalake
Limitedandstatic
enterprisedatamodels
howeverwith
divergingsemantics,
extendednumberof
datasources,adhoc
datacleansing.No
Focusonmeta-data
anddatasecurity.Data
cleansingwithlimited
automation.Evolving
enterprisedatamodel
basedupon
ontologies.Limited
Datalakeasasecure
foundationforstoring
andintegratingdata.
Datawarehouseispart
ofthedatalake.
Automationwith
regardstodata
DataAnalytics&Science Insightsarefocusedon
describingwhat
happened
Datawarehouse
implemented,broad
usageoftools,limited
analyticaldatamarts
Insightsareusedto
predictthelikelihood
ofwhatwillhappen
withregardstocurrent
Insightsofanykindare
usedtohelpdecision
makingandmaximise
businessvalue
BusinessInformationArchitecture Insightsaremainly
focussingonfinancial
andregulatory
reporting
Itisrecognizedthat
datacanbeusedto
generatebusiness
valueandROI,but
Itisencouragedto
createinsightfrom
datawithinbusiness
processes
Datadrivescontinuous
businessmodel
innovation
DataGovernance&Organization Datagovernanceis
largelymanualand
barelysufficientto
standuptolegal,audit
andotherregulatory
scrutiny.
Expertiseinanalyticsis
limitedtofunctional
Understandingofdata
anditsownershipare
definedandmanaged
butinalimitedway.
Organizationis
focussedon
performance,
Policiesand
procedurestomanage
andprotectdatais
implemented,
howeverautomationis
limited.
Advanceddata
Policiesand
procedurestomanage
andprotectdataare
fullyautomated.
Organizationconsists
ofabalancedskillset
ofbusinessanalysts
‘Good enough’ ‘Competitive’
Domain Technology Data Foundation Data Analytics & Data Science
Go
vern
an
ce &
Org
an
izti
on
‘Sub-optimal’B
usin
ess In
form
ati
on
A
rch
itectu
re
‘World Class’
Understand the data needed to provide
domain specific analytics
Establish BI program
governance framework
Create end-to-end data
foundation view
Create data discovery
environment
Extend current
KPI-portfolio
Introduce Machine learning
capability
Assess self-service
requirements
Explore data lake (pattern
finding)
Define metadata/ontologies
Define future application deployment
models
Evaluate employee facing processes
Define Data Reporting & Visualization
Define domain specific business
use cases
Improve collaboration
between Business and IT
Establish data lake as data
foundation layer
Establish change
management
Define/ Tailor agile program delivery
framework
Establish an information
strategy
Define Business priority ,
stakeholders and ownership
Invest in Data Scientists
Enable Process Innovation Chain
& Dev Ops
Integrate data lake with
existing data platforms
Introduce business
stewardship
Define search criteria
Define data set ingestion
topologies
Align data quality with insights requirements
Integrate ML in domain pocesse
s
Investigate need for cognitive analysis
Create application
landscape incldata flows
Assess future timeliness of
data (batch vs realtime)
Design interface upgrade plan
Integrate data lake with existing technology platforms
Define application
roadmap
Implement data quality
technologies
Implement API-repository
CoolBI
Visualise
Agilize
Cloudify
Democratize
Dataversify
Intelligize
Actionize
12Copyright © Capgemini 2017. All Rights Reserved
Data WARP for SAS Analytics: integrating with SAS BAMA
Prescriptive
Predictive
Simulation/optimization/
cognitive analysis
Machine learning/exploration
causation analysis
Descriptive Reporting (aggregation) analysis
Data
An
aly
tics
Data
Scie
nce
Reporting (root/cause) correlation analysis
Diagnostic
BAMA Data WARP
Ove
rall P
roc
es
s &
IT
Arc
hit
ec
ture
SA
S T
ec
hn
olo
gy c
ap
ab
ilit
ies
13Copyright © Capgemini 2017. All Rights Reserved
Why SAS? Why Capgemini?
Enable customers to maximize the
value of SAS products
Introduce new technology & tools
which allows for new use cases, less infra
structure costs and increased investment
in new enabling technologies & tools
Opportunity for optimization of software
and resell for SAS on new products
Enable customers to maximize value of
Insights & Data capabilities
Help customers towards a new data
landscape, combining different vendor
solutions in an optimized way
Full alignment with the Enterprise level
process and IT architecture
Combining deep knowledge about SAS products with Capgemini consulting
expertise will bring more value to (shared) customers
Why DATA WARP & BAMA?
14Copyright © Capgemini 2017. All Rights Reserved
Summary: challenges to be addressed
Challenges to be addressed
“Which data should we
retain and/or which
data could we
archive?”
“To what extent can I
monetize the data I’m
collecting through
IoT?”
“Can I decrease costs
by moving my data (&
analytics) to the cloud
or As-A-Service”
“How mature is my data
& anaytics platform in
comparison to the best
industrial trends?”
“I have been told “to
do something” about big
data analytics but don’t
know where to start”
“How can my Business
Intelligence landscape be
optimized to derive the
maximum value out of it?”
“Our data landscape
is scattered, complex
and very expensive,
can we fix it?”
Value created
An optimized Data & Analytics Platform
will enable:
Reduced complexity: Rationalizing the
data landscape to meet demand
Lower cost: Reduce the operating cost
of your data landscape
Increased agility and better time to
market: More speed in the development
of new information applications
Better insights and return on
intelligence: Ease to derive meaningful
insights and enable business
transformation
Less risk: Reduce complexity of the
data landscape
Data security & privacy: Make your
data landscape compliant with rules
and regulations
‘Good enough’ ‘Competitive’
Domain Technology Data Foundation Data Analytics & Data Science
Go
vern
an
ce &
Org
an
izti
on
‘Sub-optimal’
Bu
sin
ess In
form
ati
on
A
rch
itectu
re
‘World Class’
Understand the data needed to provide
domain specific analytics
Establish BI program
governance framework
Create end-to-end data
foundation view
Create data discovery
environment
Extend current
KPI-portfolio
Introduce Machine learning
capability
Assess self-service
requirements
Explore data lake (pattern
finding)
Define metadata/ontologies
Define future application deployment
models
Evaluate employee facing processes
Define Data Reporting & Visualization
Define domain specific business
use cases
Improve collaboration
between Business and IT
Establish data lake as data
foundation layer
Establish change
management
Define/ Tailor agile program delivery
framework
Establish an information
strategy
Define Business priority ,
stakeholders and ownership
Invest in Data Scientists
Enable Process Innovation Chain
& Dev Ops
Integrate data lake with
existing data platforms
Introduce business
stewardship
Define search criteria
Define data set ingestion
topologies
Align data quality with insights requirements
Integrate ML in domain pocesse
s
Investigate need for cognitive analysis
Create application
landscape incldata flows
Assess future timeliness of
data (batch vs realtime)
Design interface upgrade plan
Integrate data lake with existing technology platforms
Define application
roadmap
Implement data quality
technologies
Implement API-repository
Data & Analytics
platform
No limit to volume
No limit to structure
No limit to analyzing
No limit to timing
15Copyright © Capgemini 2017. All Rights Reserved
Who to contact ?
Capgemini Balaji Palanidurai
• Global Architect
• Balaji.Palanidurai@Capgemini.com
Theo Elzinga• Global Architect, EMEA
• Theo.Elzinga@Capgemini.com
Monish Suri• Global Alliance Manager
• Monish.suri@Capgemini.com
SAS Jeroen Dijkxhorn
• Director Analytics Platform CoE, SWE region
• Jeroen.dijkxorn@sas.com
Paul Gittins• Principal Business solutions EMEA
• Paul.Gittins@sas.com
Valerie Vaquerizo • Global Alliance Manager
• Valerie.Vaquerizo@sas.com
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