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SAP Dreamzone - Data Platform Implementation

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

Published in: Technology
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SAP Dreamzone - Data Platform Implementation

  1. 1. OCTO Part of Accenture Digital © 2019 - All rights reserved SAP DREAMZONE Data Platform Implementation
  2. 2. T H E R E I S A B E T T E R W A Y Project Overview 01
  3. 3. OCTO Part of Accenture Digital © 2019 - All rights reserved 3 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. 4. T H E R E I S A B E T T E R W A Y Core reporting and self service 02
  5. 5. OCTO Part of Accenture Digital © 2019 - All rights reserved 5 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.
  6. 6. OCTO Part of Accenture Digital © 2019 - All rights reserved 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
  7. 7. OCTO Part of Accenture Digital © 2019 - All rights reserved 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 Copyright © 2019 Accenture. All rights reserved. 7
  8. 8. T H E R E I S A B E T T E R W A Y BW/4Hana and greenfield approach 03
  9. 9. OCTO Part of Accenture Digital © 2019 - All rights reserved 9 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
  10. 10. OCTO Part of Accenture Digital © 2019 - All rights reserved 10 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. 11. T H E R E I S A B E T T E R W A Y Digital Marketing platform on Hana 04
  12. 12. OCTO Part of Accenture Digital © 2019 - All rights reserved  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
  13. 13. OCTO Part of Accenture Digital © 2019 - All rights reserved 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
  14. 14. OCTO Part of Accenture Digital © 2019 - All rights reserved 14 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

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