OCTO répond aux différentes étapes de transformation vers une entreprise data-driven reposant sur des solutions autour de l’analytics SAP.

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SAP DREAMZONE
Data Platform Implementation

2. T H E R E I S A B E T T E R
W A Y
Project Overview
01

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Project Overview
Key Initiatives :
• Core reporting
• Self service
• Digital marketing platform (Customer engagement platform that ensures
we deliver universal, simple and consistent experiences across channels
based on retail industry models
• Core meter-to-cash platform that evolves with the market
CUSTOMER
ENGAGEMENT
METER-TO-CASH
DATA & ANALYTICS
Solution
Components
DATA & ANALYTICS
CUSTOMER ENGAGEMENT
METER-TO-CASH
2022202120202019201
8
Release 1
Analytics
Release 2 & 3
Customer Engagement
Release 4
Universal Bill
Release 5-8
Core Solution

4. T H E R E I S A B E T T E R
W A Y
Core reporting and self service
02

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CORE Reporting Approach
Disposition current state: Users will need to maintain current reporting needs for business critical
processes (such as Month-End closing, SOX or controls). Focus on understanding business priority
and simplifying the report inventory through rationalization and/or Best Practice Reports
disposition approach.
Tool definition: Select the right tools by defining intended user interaction.
Change management: Business readiness and training to transition to Self-Service operating model which
are critical components for self-service adoption
Harness the power of user self-service: Users will have the ability to pull data and build their own reports.
Align operating model & process to the right balance of self-service vs. delivered reporting capabilities
to ensure consistency and data integrity.

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Future BI
Self-Service
Solution
6
Approach for deriving the right Recommendation
• Identify the right criteria for Tool Analysis / Comparison and understand current Reporting
landscape
Understand current Self-Service Practice (Tools)
Analyzing Dreams and Pain Points regarding Self-
Service
• Provide a recommendation regarding the usage of Self-Service BI to allow ad-hoc Analysis
driven from the Business
Comparison of Self-Service Tools to highlight the key differentations
and highlight aspects for the Governance strategy
• Identify areas for optimization
Analyzing Dreams and Pain Points regarding Self-Service

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A selection tool launched for a visualization tool, main objectives that the tool had to answer was
• Be future driven
• Be pragmatic
• Be user centric
The evaluation was one on :
• functional requirements
• accessibility requirements
• technical requirements
SAC was selected at the end of this study to meet visualization and self-service capacity needs.
(major reasons: integration with SAP, authorization, hierarchies)
SAP analytics Cloud selected
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8. T H E R E I S A B E T T E R
W A Y
BW/4Hana and greenfield approach
03

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Option selected to deliver in BW4Hana
• BW System is used for more than 1000 Reports for consolidation
• Data Lineage Analysis revealed several years of project
evolution leading to layers of logic and redundancy leading
to sub optimal design
• Remote Conversion PoC highlighted the expectation of a ‘clean’ source. Significant effort to cleanse.
Analysis of Reporting Landscape
Greenfield Installation
“If the design of your current SAP landscape is over five years old, it is time to consider a fresh start”.
OPTION SELECTED

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MAJOR Reasons for the Greenfield Approach
• Data lineage has highlighted several issues. Some are mentioned below.
> Transformation with non optimized or redundant logic
> Multiple layers of objects that will be useless in an LSA++ scenario
> Overlapping and redundant dataflows, that means some dataflow can
be merged, resulting in a leaner, easier to manage BW landscape.
> Several objects (DTP, Cubes, DSO, transformations, etc.) that are not used
at all and need to be deleted
> Multiple queries – and related dataflows – to be moved to S/4 Hana
> Extensive usage of 3.X objects that – in case of brownfield - will eventually
need 7.x conversion before they can be moved to BW/4 Hana
All the above would imply, in case of brownfield, a potential long and
expensive housekeeping activity. This is one of the reason why greenfield
should be preferred to other potential solutions.

11. T H E R E I S A B E T T E R
W A Y
Digital Marketing platform on Hana
04

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 Different uses cases for digital marketing platform on Hana
> Use case: Track the interactions customers are having across multiple channels - web, IVR, live voice calls, social media, etc. and
build a holistic customer profile.
> Use case: Build on the holistic customer profile to begin to engage with customers in new ways. Customer Care specialists will have
insight into interaction data providing them context into why the customer may be calling.
 Based on technical context,
> Hybris on Hana implemented,
> Hana used for Core reporting solution.
 It was decided to implement a digital marketing platform on Hana Standalone. (Hana 2.0) based on the experience already done with
previous project.
12
Hana for a digital marketing platform

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SPECIALIST
13
Advanced Analytics Overview
CUSTOMER
IVR* CDP (Web)
Mobile
App
Service
Front End
INBOUND
CHANNELS
Request
Account, Channel
Response
Log Recommendation &
Result
MACHINE LEARNING
Recommended Actions
Result
Presented? Selected?
SAP HANA EE
Predictive Analytics Library
(PAL)
PERSONALIZATION ANALYTICS
& PREDICTION ENGINES
Manage Recommendation to
configure products, actions,
activate, deactivate, RankProvide Recommendation
(PAL)
Hybris suite
OUTBOUND
CHANNELS

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Advanced Analytics Overview
• Algorithms recommend appropriate actions,
such as:
> Update phone number
> Promote My Account
> Promote mobile
> Update email
> Inform recent work order
• Algorithms predict the intent of call as soon
as it is received by call center specialist to
enable more effective and personalized
response:
1. Predict outage calls
2. Predict repeat caller
Outage | Billing l SpeedPay | Start service
Disconnected – Non payment (DNP)
• Algorithms predict the best offer (regulated
and non-regulated products and services)
for customer based on buying propensity.
Offer Recommendations Intent prediction Recommended Actions