Bringing next-generation data, analytics & AI to the

Bringing next-generation data, analytics & AI to the digital core Enabling the next steps towards a modern SAP-based data eco-system

Grab’n’Go November 2nd 2021

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Welcome to todays session…

Mads Frank

[email protected]

Deloitte

Partner

Analytics & Cognitive

[email protected]

Chris MeisnerDeloitte

Senior Manager

Analytics & Cognitive

mailto:[email protected]



“What value can a dedicated master data initiative provide ?”A perspective on the value of having a solid masterdata strategy as part of a digital core journey

“We have S/4 coming in shortly - why should be continue to look towards external data & analytics platforms ?”A perspective on how to setup the right balance between data availability in the digital core vs. flexible data distribution

“We experience an increased self-service interest and analytics maturity from the business – how do we address that ?”How to leverage multiple analytics platforms and the link towards the digital core

“We have put significant investments into our current SAP data & analytics solutions –should we continue to leverage those going forward ?”Going from a singled siloed data approach to data in the hands of many

“There continues to be a need for data centric initiatives, both why should we bother aligning them around the digital core ?”… Sharing our perspective on trending use-case and why data eco-systems matters

Common industry questions which we are going to address today…

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Data, Analytics & AI









Imagine a truly data-driven growth agenda…

BEYOND 2022

"We see data, analytics & AI playing a key role in reaching and interacting with people”

Data, analytics & AI provides a personal experience - to customers in all touch points enabled by segmentation, behaviour, interest, omnichannel insights etc.

Data, analytics & AI enables corporations to reach individuals - by tailoring messages and targeting customer segments based on eg. real-time analytics of social media transaction etc.

Data, analytics & AI guarantees availability - through omni-channel fulfilment enabled by customer-, installer-, inventory-assortment localization etc..


BEYOND 2022

“We see data, analytics & AI playing a key role in creating a positive impact for people, society and the planet.”

Corporations can use data, analytics & AI to run a clean and efficient value chain from sourcing raw materials to product usage and replacement enabled by waste-, customer-, market analytics.

Enables corporations to drive environmental responsibility by providing a new generation of active or interactive products enabled by customer-and behavioural analytics.

Bringing people together in the social- and professional ecosystems based on employee-, market-, customer- and supplier analytics

Imagine a data-driven sustainability agenda…


BEYOND 2022

“We see data & analytics playing a key role in maintaining quality and to drive efficient manufacturing”

Enables corporations to put machine learning and intelligent data processing into play as an integrated part of manufacturing and quality inspection.

Harvest data from low-cost and power efficient sensor technology tracks inventory and products around the globe to optimize logistics and supply chain.

Imagine data-driven manufacturing and intelligent quality inspection…

Tap into manufacturing processes in real-time and utilize simulation, digital twins and forecast to optimize production output


Next generation data &

analytics is focusing on

releasing data and bringing

analytics and advanced

insights into the hands of the

many

Next generation data & analytics is

about bringing best of breed

capabilities into a flexible operating

model and to align on an integrated

operating model by building on

existing investments

Next generation generation

data & analytics is about

releasing the full potential

of data and analytics

without spending the same

money twice

BEYOND 2022

"We see the concept of data, analytics and AI continuing to play a crucial role in building next generation data driven organizations –but the demand for range and adoption are changing”

Bottom line…data are going into the hands of the many…

Have you identified potential next generation data, analytics, and AI use cases? Which ones?

ⓘ Start presenting to display the poll results on this slide.









For most corporations the next generation data eco-system is very much within reach as a result of ongoing investments and corporate maturity. However to succeed in taking the next step a common set of design criteria’s should be considered

12345

Enable data democratization, local flexibility and innovation

Enable global and “purple” collaboration from ideation to industrialization and throughout the data value chain

Leverage and augment existing platform and investments

Strike the right balance between autonomy and necessary governance

Ensure privacy and security by design

So what to think of before taking the data eco-system to the next level…


To establish a modern data eco-system which supports wide use of high-quality data, the following logical components are required

Analytical dimension Operational/transactional dimension

Golden recordsDigital core - ERP + MarTech + CRM etc.

Master data dimension

Advanced data model, Analytics,

Insights & AI

What makes up a modern data eco-system ?

• Core logical component in the wide data eco-system.

• Handles data ingestion and integration.• Collects, transforms and distributes data.• Storage, modelling and transformation of

“golden record”• Identity management.

• Storage and model component for analytical processing and insights

• Data model component, data lake & golden record storage and AI platform for further processing.

• Compute platform for advanced algorithms and machine learning.

• Visualization platform for analytics and reporting purposes.

• Marketing automation, CRM, ERP etc.• Operational and transactional in nature.• Both consumes and distributes data

across the data eco-system.• Dependent on a stable master data

dimension.• Are typically regarded as critical core

systems and therefore normally only used for transactional processing .


Linking the three key data eco-system components enable a so-called close feedback loop binding data, analytics and operational execution together – outlined below as part of a customer data scenario. This is a typical approach for most companies however we also see quite many examples of decoupled components and parallel execution

Core customer data profile and related transactional data combined for advanced insights. Design of new campaigns, retrieve details of executed campaigns, data mash-up etc.

Core customer data profile data used to support campaign execution, sales insights and to harvest updates to golden record from executed campaigns and marketing.

Customer data feed for campaign execution and CRM

Analytics data feed for campaign execution

Any

Campaign results for Analytics & AI

Marketing Automation(e.g. Salesforce)

ERP (e.g. SAP)

Single Customer View


Feedback loop - enrich customer profile from

campaign execution and CRM

Analytical dimension

Advanced data model, Analytics, Insights & AI

Operational/transactional dimension

Campaign management and execution + CRM

Customer data feed for analytics insights

and AI

…and how to link these together?


…growing into the need for new capabilities of a moderndata eco-system

Multiple analytics platforms to service

individual preferences

Multiple data storage components to meet

business agility

Multiple complex sources – high velocity

Advanced analytical applications

AI and Machine Learning requirements - MLOps

Advanced modelling capabilities

Dedicated masterdata


Finding the right balance between the digital core and a surrounding data eco-system

• Data modelling across data domains

• Historic data needed• High level og detail• Global definitions and

governance • Historic data needed• Cross functional

processes• Data modelling required

• Process close reporting • Limited requirement for

historical data• Limited requirement for data

modelling



“We have S/4 coming in shortly, why should be continue to look towards BW/BPC or similar”A perspective on how to setup the right balance between data availability in the digital core vs. flexible data distribution


“We have put significant investments into our current BW/BPC solutions – should we continue to leverage those going forward ?”Going from a singled siloed data approach to data in the hands of many




Finding the right balance between multiple analytics and reporting platforms…

Cross platform integration!

Security!

Visualization!

Planning!

Advanced analytics!

Based on the insights just provided, how complex and/or mature do you see your current data eco-system being?


How many technology vendors are currently represented within your current data eco-system?


How do you see the future scenarios for data & analytics usage as part of a digital core enablement?




“We have S/4 coming in shortly, why should be continue to look towards BW/BPC or similar”A perspective on how to setup the right balance between data availability in the digital core vs. flexible data distribution


“We have put significant investments into our current BW/BPC solutions – should we continue to leverage those going forward ?”Going from a singled siloed data approach to data in the hands of many




• The role of a central enterprise master data component is fairly complex and goes far beyond basic data storage and integration scenarios.

• The core functionality of the central master data component is to ensure that relevant master data is at a constant high quality and ready to use across the data eco-system.

• As such, a central master data component handles several complex data operations as outlined in the scenario to the right illustrating a centralized customer data operation.

• Previously, there were attempts to let operational/transactional systems (e.g. ERP systems) and/or analytics components, such as data warehouses, handle the central master data operations. However, due to issues with complexity, high maintenance costs and performance and stability of core systems this approach has been abandoned by many companies.

Collect data from all customer related

data sources

Duplicate handling

Data quality issue handling – blanks,

mismatch values, etc.

Match, merge and consolidate customer

data records and attributes

Deploy unique customer identifier

Store and enrich “golden customer

record”

Distribute “golden customer record”

Design, build and run segmentation and/or

target list

Orchestrate segmentation/target

list to marketing automation

Update data feedback from

marketing automation (return

loop)

Golden Record


Bringing everything together….the importance of high quality and available master dataDigital transformations and the data driven organizations are built upon a strong data foundation. Well defined and managed master data is one of the corner stones of the data asset. It provides the ability to automate business execution and to facilitate effective and efficient decision making with confidence.


Linking the three key data eco-system components enable a so-called close feedback loop binding data, analytics and operational execution together – outlined below as part of a customer data scenario. This is a typical approach for most companies however we also see quite many examples of decoupled components and parallel execution

Core customer data profile and related transactional data combined for advanced insights. Design of new campaigns, retrieve details of executed campaigns, data mash-up etc.

Core customer data profile data used to support campaign execution, sales insights and to harvest updates to golden record from executed campaigns and marketing.

Customer data feed for campaign execution and CRM

Analytics data feed for campaign execution

Any

Campaign results for Analytics & AI

Marketing Automation(e.g. Salesforce)

ERP (e.g. SAP)

Single Customer View


Feedback loop - enrich customer profile from

campaign execution and CRM

Analytical dimension

Advanced data model, Analytics, Insights & AI

Operational/transactional dimension

Campaign management and execution + CRM

Customer data feed for analytics insights

and AI

Recap (and how to link these together?)


• Example on case where considerations was made to implement a master data repository (MDR) to ensure one single source of truth for master data and to facilitate master data alignment and distribution with focus on preserving local keys in local systems.

• The MDR will also support data cleansing and harmonization during system migration & consolidation.

• Together with SAP and other ERP systems, Salesforce and other CRM systems and a shared analytics platform, the shared master data repository provides the needed capabilities.

SAP

Local ERP to be migrated to SAP

Data platform

Local ERP that will continue outside SAP

Salesforce

Other CRM systems

Other systems

Master Data Repository

Master data is synchronized from MDR to the analytics platform, and enriched customer master data is sent back.

Master data is synchronized between local systems and the MDR

Transaction data is copied to the analytics platform and local systems leverage analytics insights via API’s

Putting master data into play…

Audience Q&A Session

ⓘ Start presenting to display the audience questions on this slide.


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Copyright © 2021 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu Limited

Mads FrankDeloittePartner | Analytics & CognitiveContact: [email protected]

Chris MeisnerDeloitteSenior Manager | Analytics & CognitiveContact: [email protected]



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