In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data. Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques. Key Takeaways: (i) Demystify the myths of analytics (ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars. (iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.

2. Shailendra Kumar, Chief Evangelist, SAP
May 1, 2019
Making Money Out of Data
The art & science of analytics

3. Keynote Speaker, Global Data Science and Analytics Influencer, BestSelling Author &
International article contributor and has experience in Business and Customer Analytics
across Europe, United Kingdom and in Australia, generating incremental value benefits of
over $1 Billion
Industry Experience:
▪ Woolworths, Largest Retailer, Australia: Chief AnalyticsOfficer
▪ Coles, Grocery Retailer, Australia: Senior Business Executive,Analytics
▪ Swisscom, Telecom Operator: Senior Manager
© 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC
Selected Professional Experience
Shailendra Kumar
Chief Evangelist, Analytics & Leonardo
APJ&GC
Based out of Sydney,Australia
Shailendra is the author of the Amazon bestseller
“Making Money Out of Data” and is the
Evangelist for APJ&GC and helps SAP’s
Chief
client
organisations in creating incremental value using data

4. Analytics provides you with the tools to benefit from a series of
business imperative questions
Imagine if you could …
… understand
demand across
the regions
…know which
half of your
marketing dollar
gives returns
… understand
use of network
capacity and the
reasons behind it
… know price
elasticity of
demand in
advance
… know customer
behavior from
social media
originating
information
… know which
part / process is
going to fail,
before it fails
Analytics

5. Analytics is not new
Increasing
Analytic
Sophistication
& Capabilities
Ad Hoc
Query
Scorecards
DW Lifecycle
Mgmt
Collaboration
& Workflow
Batch
Reporting
Online Analytic
Processing Dashboard &
Visualizations
Process
Awareness
ETL /Data
Quality
Data
Models Alerting Cognitive
Analytics
Predictive
Analysis
Data
Warehousing
Static
Reporting
Event
Automation
So what is different now?
Definition: Analytics is the process of using quantitative methods to derive
actionable insights and outcomes from data
1975 1989 1990 2004 2005-2020
Templates
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6. WE STRUCTURE OUR APPROACH TO INTEGRATED ANALYTICS INTO FOUR
MUTUALLY REINFORCING TIERS
VALUE TO THE ENTERPRISE
BUSINESSAGILITY
Product Segmentation, Sales
performance dashboards, Social
MediaAnalytics, Mobile Data & BI
Supply Chain Optimisation,Trigger
Based Modeling,
Social NetworkAnalysis, Next
best offer / action
MANAGING
INFORMATION
Managing information to improve business
process with information strategy and
governance while achieving a ‘single source
of the truth’ for customer information
DESCRIPTIVE
ANALYTICS
Deliver business intelligence and insight
generation capabilities and leverage
timely deployment of actionable
information and industry focused
intelligence
AD-HOC
ANALYTICS
SERVICES
Answer business questions to address
day to day business needs in a timely
manner to service business units.
PREDICTIVE
ANALYTICS
Improconfirm that analytics insights are turned
into both actions and measurable outcomes
proactively, driving high performanceve the
speed and quality of decision making to
What if scenarios, simulations, adhoc
analysis, combatingtactical issues
Manage Customer / Product related data for sales
performance, Integrate data across multiple channels Single
view of customer / product, revenue byregion
Analytical Competitive Strategy

7. Business and Technology changes are opening up new
opportunities for innovating around analytic capabilities
y
Business Context
• Greater volatility
• New waves of growth & innovation
• Governance in a multi-speed recover
• Different questions
Innovation Opportunity
1.Measure what matters, while
what matters is changing
2.Next Practice not Best Practice
3.Innovate through Decision
Process Reengineering
4.Automate the Outcomes
Technology and Data Context
• Technology mega-trends
• Data driven innovations
What has Changed? So What? Now What?
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8. High Performing Organisationsare able to realize outcomes better using analytics
High Performing companies are satisfied with the contribution analytics has
made to financial performance, strategic direction, addressing growth opportunities, informing
critical decisions and managing risk, compared with Low Performers (onaverage)
LOW PERFORMERS HIGH PERFORMERS
THEANALYTICSJOURNEYTOROI
FOCUS ON DATA TO INSIGHTS
Commit to analytics
Manage talent from end-to-end
Use advanced analyticaltechniques
Embed analytics into the decisionprocess
FOCUS ON INSIGHTS TO OUTCOMES
✔Commit to analytics
✔Manage talent fromend-to-end
✔ U s e advanced analytical techniques
✔Embed analytics into the decision process

9. Moving to predictive analytics and insights
* Percentage of time
9
Advanced
analytics
Analysis
and
insights
Reporting and
data collection
Analysis
and
insights
Reporting and
data collection
• Report
rationalization and
automation
• KPI alignment
• Issue-driven insights
and measures
• Data virtualization/
centralized
data access layer
• Business intelligence
and data
visualization toolkit
• Advanced Analytics tools and solutions
• Information and analytics organization
design and operating model
• Talent sourcing and delivery model
Advanced
analytics

10. 9
9
Data Collection & Preparation
Clean, fully mapped and validateddataset
delivered to modelling team
Modelling
Preliminary results delivered tofor
use in Business workshops.
Business Discussions
Final modelling results
incorporating business
recommendation
Insights & Recommendations
Results & insights presentationsprepared
and socialised after incorporating the
business PoV
Execution
Incorporate insightsinto
Business Processes to
get results
Tracking
Track the actuals against
the recommendedinsights
Objective
Outcome
Data Science process currently followed is linear and is designed to
work in Silos

11. Challenges in Execution of Analytics Insights
Your Analytics Model
doesn’t make sense and
I don’t think it will work!
I know more about the
business than your
analytics can tell me!
I am told to align the
data but I don’t know
why?
Analytics is just yet
another jargon… and
does nothing!!
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Business Business Team
Finally

12. Illustrative Benefit Areas
12
Business
Value
Income
Growth
Risk
Management
Operational
Efficiency
12. Reduced staff costs
13. Optimized sales & marketingspend
14. Reduced operational and administrativecosts
3. Increased cross-sale & share of wallet
5. More effective salesforce
7. Improved campaign and offer effectiveness
6. Optimised leadsmanagement
8. Improved risk management
9. Improved fraud identification & mitigation
10. Improved staffproductivity
Analytics Benefits Driver Tree
2. Improved customer retention and loyalty
1. Improved customerexperience
11. Improved employeeengagement
4. Improved customeracquisition
↓ Staff costs pertransaction
↓ campaign cost per campaignsale
↓ Operational cost by totalbook
↑ Products per customer
↑ Sales per staffmember
↑ Offer to saleconversion
↑ Lead to sale conversion
↓ Breaches
↓ Fraud cost
↑ Transactions per staff member
↓ Account closures
↑ NPS ↓ Complaints ↑ Satisfaction
↓ Attrition ↑ Engagementsurveys
↑ New customers
Outcome Measure

13. There are huge benefits when we bring it all together
Challenge
A large retailer in the US wanted to know whatis
the profile of the employee that are likely to
leave the organisation in less than a year
A large CPG had thousands of KPIsacross
functions, geographies and categories
There was difficulty in understanding gaps in the
performance
Approach
Brought together : Social Media data, Internal
employee data, Performance Data
Built a predictive model and then scored each
employee with an attrition score
Collected data from Financial Systems,
Operational Systems, BI Systems, etc.
Created Early Warning System and a
visualisation of the most impactful KPIs
Outcome
The retailer created a profile of an employee who
is likely to leave in less than year, which helped
them introduce additional questions totheir
interview process. It significantly reduced attrition
rate.
Alerts are generated about an issue before it
happens and furthermore, the board focusses on
the KPIs that matter the most towards the
growth of the organisation
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Human Resources IT Operations

14. Important characteristics of a successful analytics environment
Live &
Connected
14
Story-
Telling
Realtime
Updates
Powerful
Visuals
Integrated
Predictive
Collaborative

15. Embed Analytics in Processes:
From “Craft” Analytics to “Industrial”
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CRAFT INDUSTRIAL
Pattern Ad hoc, project-oriented Embedded
Purpose One-time decision or event
support
Ongoing process performance
Benefit One-time Recurring
Investment Higher, recurring Lower, One-time
Speed of
Analysis
Same as time to implement Fast or instantaneous
Staff Labor-intensive Informed or automated
Memory of
analysis
Saved for reuse or lost Maintained and improved

16. Critical Success Factor: Start from the End
Identify the
Opportunity Define the
Outcome &
the Execution
Quantify the
Value
$
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17. Analytics driving innovation in China
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18. We have a huge opportunity to shape
the future together
Contact information:
Shailendra Kumar
Chief Evangelist Analytics | Leonardo
@meisShaily
linkedin.com/in/shaily
CognitiveToday.com
shaily.kumar@sap.com
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