Big Data Analytics & Modernization with Data WARP and BAMA · generate business valuetand ROI,gbut It is encouraged to create insight from data within business processes Data drives

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Big Data Analytics & Modernization with Data WARP and BAMA


Theo Elzinga

Global Enterprise Architect

Co-author of the Open Business Data Lake Standards

TOGAF certified

Author The CORA model

http://www.coramodel.com


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


…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


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


…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


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


Data WARP is a short assessment project to define a data & analytics platform roadmap well fitted into the overall process & IT architecture….


… allowing IT to build the supporting technical data capabilities…


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


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



Go

vern

an

ce &

Org

an

izti

on

‘Sub-optimal’B

usin

ess In

form

ati

on

A

rch

itectu

re

‘World Class’






foundation view


environment

Extend current

KPI-portfolio


capability

Assess self-service

requirements


finding)



models




use cases




foundation layer

Establish change

management


framework


strategy


stakeholders and ownership


Enable Process Innovation Chain

& Dev Ops



Introduce business

stewardship



topologies


Integrate ML in domain pocesse

s


Create application






Define application

roadmap


technologies


CoolBI

Visualise

Agilize

Cloudify

Democratize

Dataversify

Intelligize

Actionize


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


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?


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



Go

vern

an

ce &

Org

an

izti

on

‘Sub-optimal’

Bu

sin

ess In

form

ati

on

A

rch

itectu

re

‘World Class’






foundation view


environment

Extend current

KPI-portfolio


capability

Assess self-service

requirements


finding)



models




use cases




foundation layer

Establish change

management


framework


strategy


stakeholders and ownership


Enable Process Innovation Chain

& Dev Ops



Introduce business

stewardship



topologies


Integrate ML in domain pocesse

s


Create application






Define application

roadmap


technologies


Data & Analytics

platform

No limit to volume

No limit to structure

No limit to analyzing

No limit to timing


Who to contact ?

Capgemini Balaji Palanidurai

• Global Architect

• [email protected]

Theo Elzinga• Global Architect, EMEA


Monish Suri• Global Alliance Manager


SAS Jeroen Dijkxhorn

• Director Analytics Platform CoE, SWE region


Paul Gittins• Principal Business solutions EMEA


Valerie Vaquerizo • Global Alliance Manager


mailto:[email protected]






Documents

Big Data Analytics & Modernization with Data WARP and BAMA · generate business valuetand ROI,gbut It is encouraged to create insight from data within business processes Data drives