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Digicloud 2017 First Mover or Fast Follower? Big Data for Everyone

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Presentation on theme: "Digicloud 2017 First Mover or Fast Follower? Big Data for Everyone"— Presentation transcript:

1 Digicloud 2017 First Mover or Fast Follower? Big Data for Everyone
22/03/2017 Alexandre Akrour CEO

2 The Bottom line is what matters for businesses

3 Benefits are driven by Revenue and Costs…

4 …But Revenue is driven by Customer Satisfaction…

5 “ ” If I had asked my customers what they wanted they
would have said a faster horse Henry Ford

6 …And costs by efficient business operations!

7 7billion 90% 50 billion 500 billion People “Big” Data Things
Pillars of Digital Transformation People 7billion 7 billion people will have access to internet by 2020 “Big” Data 90% 90% of the data created in the last two years. 40 zettabytes by 2020 Things 50 billion 50 billion devices connected to the internet by 2020. Applications 500 billion New generation of applications and services API Economy Online sales to cross $500 billion in 2020

8 Artificial Intelligence Revenue By Region, World Markets: 2015-2024

9 Ok, Is it too late now?

10 “ ” First mover advantage doesn't go to the company that
starts up, it goes to the company that scales up Reid Hoffman, Linkedin

11 Big Data Maturity Index

12 Capitalizing on Big Data
Source: Analytics: A blueprint for value – Converting big data and analytics insights into results IBM Institute for Business Value

13 Ease to capture Potential Value

14 Recommendation engines
Financial Services Fraud Detection Credit card fraud contributes to 40% of banking fraud Total amount of credit card fraud worldwide added up to $7.5 billion in 2015 Risk analysis Risk analysis based on deep analytics Portfolio analysis 360 degree view of all portfolios Recommendation engines Customized recommendations based advanced analytics Special services for premium customers

15 Major pharmaceutical and clinical Research
Healthcare Healthcare spending US: Expected to increase 5 to.8% annually until 2024 • US: 17% of GDP, UK: 8.8% (2014) Fraud-Prevention The National Health Car Anti-Fraud Associa tion (NHCAA) estimates that between $68 and $226 billion is lost annually to fraud System-of-Records Reduce the amount of time providers spend doing paperwork, reduce medical errors Improved patient health/quality of care through better disease management and patient education Major pharmaceutical and clinical Research Big Data analytics could save $100B in annually across US Healthcare system (McKinsey) Personalized medicine

16 Recommendation engine
Retail Recommendation engine Recondition Engine are transcribing the in-store and on-line shopping experience by providing ratings and reviews, filtering and price optimization all these are provided by modern big data platforms. Customer relationship management Big Data Insights enables improved product sand services, self- service, personalization and better customer service Supply chain optimization Optimizes supply-side of business activities such as new product development, product and product distribution to maximize revenue, profits and customer value. Store location and layout Big data solutions is transforming customer in-store experience by providing personalized product catalogs and location services

17 Workforce and asset management
Utilities Smart Grid Broad range of intelligent capabilities across customers, operations, and services Avoiding manual collection reduces outages and downtime, detect energy loss, theft and fraud Capacity management Improve load, forecasting and demand accuracy Improve targeted offerings Workforce and asset management Optimize maintenance, repair and field service. Regulatory and compliance Predictive maintenance Exploration Newer modeling techniques 23 16

18 Performance improvements
Manufacturing Performance improvements Continuous improvement through integrating Big Data analytics across the Six Sigma DMAIC- Define, Measure, Analyze, Improve and Control Better forecasts Forecast product demand and production Greater visibility into supplier quality level Asset optimization Monitor, maintain and optimize for better availability, utilization and performance Predictive maintenance Conditional monitoring Predict asset failure to optimize quality and processes 17

19 Big Data Engagement Lifecycle
The Approach

20 3 months Test, find potential, identify limits Plan the future
Proof of Concept: T+3 Months 3 months Test, find potential, identify limits Plan the future

21 Data, business and technology in collaboration
Analytics T+5months Get a tool immediately Data, business and technology in collaboration

22 Building, installing, breaking, fixing
Customer Facing Products : T+9 Months The showcase Building, installing, breaking, fixing

23 Early adopters Feedback and improve Data operating model
Broadening: T+12 Months Early adopters Feedback and improve Data operating model

24 Expansion of projects: 12X Expansion of business users: 30X
Scale up: T+24 Months Expansion of data: 10X Expansion of projects: 12X Expansion of business users: 30X Automate and reuse

25 Guided self service Data is a public good Love your data
Democratization: T+24 Months Guided self service Data is a public good Love your data Care for your data

26 Scientific Embedded Real time Cultural change
The Data Driven Organisation: T+??? Scientific Embedded Real time Cultural change

27 Sample: Agriculture ?

28 It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change. Leon C. Megginson, attributed to Charles Darwin

29


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