Bringing next-generation data, analytics & AI to the ...
Transcript of 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
Deloitte
Partner
Analytics & Cognitive
Chris MeisnerDeloitte
Senior Manager
Analytics & Cognitive
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“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
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“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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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..
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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…
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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
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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.
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“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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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…
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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 .
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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
Master data dimension
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?
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…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
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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
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“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 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 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 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
“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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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?
ⓘ Start presenting to display the poll results on this slide.
How many technology vendors are currently represented within your current data eco-system?
ⓘ Start presenting to display the poll results on this slide.
How do you see the future scenarios for data & analytics usage as part of a digital core enablement?
ⓘ Start presenting to display the poll results on this slide.
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“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 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 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 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
“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…
23 | Copyright © 2021 Deloitte Development LLC. All rights reserved.
• 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
Master data dimension
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.
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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
Master data dimension
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?)
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• 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…
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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]