The document discusses business analytics and its importance for businesses. It notes that while analytics was previously seen as only for large businesses, it is now important even for small businesses during the pandemic. The document provides predictions about the growth of machine learning, data management, and the use of prediction markets and data literacy initiatives by organizations. It also discusses trends in analytics like the focus on data strategy and democratizing data access. Finally, it provides a framework called the VIA model for conceptualizing analytics projects and an example of how it can be applied.

1. JUST ANALYTICS CONFIDENTIAL

2. APAC Business Innovation Summit
ASEAN * SAARC

3. There has been a lot of talk about connecting
business with analytics, and the common
perception is that only businesses with big budgets
are able to afford this.
What's your opinion on this?
How important is this for businesses especially in
this pandemic situation that we are currently
facing?

4. Data,
Integration,
and Analytics
Predictions
• By 2024, the productivity gap will double between enterprises that
deploy ML-enabled data management, integration, and analysis to
automate IT and analytics-related tasks and those that do not.
• By 2025, 30% of organizations will be using internal or external
prediction markets to make important business decisions
• By 2022, a third of G2000 companies will have formal data literacy
improvement initiatives in place to drive insights at scale, create
sustainable trusted relationships, and counter misinformation
• By 2023, 70% of G2000 companies will have metrics in place to
evaluate value realized from data, with those at more mature stages
gaining resource allocation agility and efficiency over competitors

7. There has been a lot of talk about connecting
business with analytics, and the common
perception is that only businesses with big budgets
are able to afford this

9. Foundational
Questioning
what AI is and
how to apply it
Wrong expectations
or disappointment
Low digitization
Basic analytical capabilities
Approaching
Hopeful on
AI and its
promise
Digitization under way
Looking to increase or
optimize processes
Cautious about
disruption
Aspirational
Experimented
and applied
AI
Mature
Emerging Data
science and
operational
capabilityHigh digitization
Desires new business
models
Achieved a data culture
Understands model
lifecycle and
management
Building a foundational
data architecture
Mapping Your AI Maturity

10. What opportunities do you think organizations can
benefit from an AI powered business intelligence?
And for businesses looking to apply this in their
organizations how can they solve the technical
challenges of implementing business analytics?

11. Analytics 3.0
• The Bosch Group - intelligent fleet management, intelligent vehicle-
charging infrastructures, intelligent energy management, intelligent
security video analysis, and many more.
• Schneider Electric - Today it focuses primarily on energy
management, including energy optimization, smartgrid management, and
building automation
• General Electric - With sensors streaming data from turbines,
locomotives, jet engines, and medical-imaging devices, GE can determine
the most efficient and effective service intervals for those machines
• UPS - It captures information on the 16.3 million packages, on
average, that it delivers daily, and it receives 39.5 million tracking
requests a day.
Resolve by a company’s management to compete on
analytics not only in the traditional sense (by improving
internal business decisions) but also by creating more-
valuable products and services.

13. Data Strategy Lifecycle
Develop the
strategy
Create the
roadmap
Change
management plan
Analytics lifecycle Measurement plan
Perspectives
• Scope and Purpose – What data will we manage? How much is our data worth? How do we measure
success?
• Data Collection – Archiving, what data where and when, single source of truth (data lake), integrating
data silos
• Architecture – Real time vs Batch, data sharing, data management and security, data modelling,
visualisation
• Insights and Analysis – Data exploration, self-service, collaboration, managing results
• Data Governance – Identify data owners, strategic leadership, data stewardship, data lineage, quality,
and cost
• Access and Security – RBAC, encryption, PII, access processes, audits, regulatory
• Retention and SLAs – Data tiers and retention, SLA’s to the business?

14. BI and Apps
Data-driven
Business
analytics and
reporting
Azure Data Services
SQL Server
Power Platform
(Power BI, PowerApps &
Microsoft Flow)
Saas with AI
Immediate
Actionable
insights
with AI
Dynamics 365 AI for
Sales, Marketing, &
Customer Service
Insights
Workplace Analytics
AI “Accelerators”
Solution-
specific AI
services and
patterns
Custom AI
Data science
and Deep AI
capability
Azure Cognitive Services
Azure Bot Service
Azure Search
Open Frameworks
Azure Databricks
Azure Machine Learning
Azure AI Infrastructre
Azure IoT Edge
The AI Journey – Where to Start
AI Industry Accelerators
AI Solution Templates
AI Analytics Templates
AIMaturity
AI Capability

16. What trends and changes have you noticed in
Business Analytics?
Can share some best practices in implementing
Business Analytics?

18. Products and Services Organization Size Industry Country
Shell invests in safety with
Azure, AI, and machine vision
to better protect customers
and service champions
In the energy industry, Shell manages everything from wells to
retail gas stations—44,000 of them. The company works hard to
ensure the safety of service champions and customers at its retail
sites. Shell is piloting a new cloud-based, deep learning solution
built on Microsoft Azure. The solution uses closed-circuit camera
footage and Internet of Things technology to automatically
identify safety hazards and alert service champions so they can
quickly respond and eliminate potential problems.
The NetherlandsMining, Oil and Gas86,000 employeesMicrosoft Azure
Azure Databricks
Azure IoT Edge
Azure IoT Hub

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Data Driven People & Culture
• Critical Thinking – Step outside internal biases to examine facts you
think are true
• Data-Centrism – Making data the center of decisions
• Democratize Data – Assume data is for everyone, not just privileged
few in the organization
• Continuous Learning – Move away from “know it all” to “learn it all”.
Do not punish failure
Business Intelligence Data Engineers Data Scientists

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DemocratizeData

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• Embarked on iterative approach to
become data driven
• Between 2018 Q4 and 2020 Q1,
transformed Sales, Inventory, Supply
Chain, Finance, Production, Raw
Material, Marketing
• Reduced operational and
management reporting timelines by
80% and FTE by 50%
• Piloting AI based sales forecast,
balanced with human inputs
Data-Centrism

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• Data Driven Culture innovation
– Critical & Design Thinking training for
business users
– IT responsible for data availability only
– Centralized CoE to provide guidance,
support and best practice
– Experimentation encouraged
• Individual business units and business
users driving modern consumption of
data
• Decision making times reduced by ~
65%. Top 5 business units seen KPIs
increase by ~ 35%.
CriticalThinking&ContinuousLearning
Leading global semiconductor
process manufacturer

23. How is Business intelligence important for Security
and Compliance of Business?

24. HOW TO ENABLE A SECURE SOLUTION

25. How can businesses start? How simple or complex?
How can they likewise measure ROI?

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THE VIA MODEL
V
I
Any Use
Case
A
Value
What is the question, business
problem or target outcome?
How is value realized?
Information
What data or data
sources are involved?
Analytics
What analytical or data science
methods are applied to the data?
Fostering Data Literacy and Information as a Second Language, Gartner 2018

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THE VIA MODEL: A Real-Life Example
V
I
Any Use
Case
A
Value
Business Value = Maximize customer revenue/share of
wallet. Cross-sell and upsell. Profitability.
Customer Value = enhanced personalization & experience
with advice on selection and recommendations
Information
Shopping history, browsing history,
shopping cart items, ratings and
reviews, comments, demographics,
social media history
Analytics
Base: Recommendation engine based on affinity
analysis & market basket analysis
Stretch: Pricing algorithm, campaign analytics,
customer profitability optimization models
Fostering Data Literacy and Information as a Second Language, Gartner 2018

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Recommended Approach
Discovery
•Pre-Requisite
•Defined business question
•Activity
•½ day workshop to understand
•Data Landscape
•Current Architecture
•Pain Points & Business Areas
•Outcome: Defined problem statement
•Value: Defined completely
•Information: Exploratory
•Analytics: Undefined
Accelerate
•Pre-requisite
•Defined problem statement with data domain
identified
•Activity
•2-3-week activity focused on Tech only
•AI focused:
•Build an AutoML model to validate potential and
deploy it
•BI Focused:
•Building a sample data model with real data to
showcase consumption
•Outcome: Project viability and business case support
•Value: Defined completely
•Information: Defined completely
•Analytics: Established
Deep Dive
•Pre-requisite
•Viability established with a prototype
•Activity
•1-3-month activity covering Tech, People & Process
•AI focused:
•Increase the performance using multiple algorithms
and deep learning
•MLOps: Automated drift detection and retraining
•ML Interpretability
•BI Focused:
•Operationalizing single subject area with automated
pipeline and data quality and self-service model
•Outcome: Productionized solution
•Value: Defined completely
•Information: Defined completely
•Analytics: Defined completely

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