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Conclusion

  • Big Data jeopardizes century-old models (e.g. risk mutualization in insurance sector) : for the Greater Good or for profit seeking purposes ?
  • Algorithms are tools like any other in the Human species' evolution : we need to harness it.
  • Currently cowboys' behaviors are common : not sustainable
  • "Black Box" algorithms are highly dangerous : more transparency (and Ethics) needed
  • The challenge of the coming years is not Big Data anymore ... it's privacy

A business perspective on Big Data trends

Happy to answer questions and welcome inquiries to speak about Big Data

pn@intotheminds.com

+32 486 42 79 42

  • Big Data gives the impression to make any marketer's dream come true: one-to-one personalized relationship
  • Data collection has become easier and cheaper (IoT)
  • More data from the same sources is not always meaningful
  • Real-time processing is an important evolution made possible by mutualization (AWS, Azure, ...)
  • Sociological approaches to behaviors worryingly disappearing : Big Data doesn't explain causation ! Qualitative approach still needed.

Big Data : business Trends and Challenges

  • New data sources are key for better predictions but may be more intrusive
  • Firms will increasingly look to enrich their data with other businesses (e.g. banks + retailers, banks and social networks, ... )

Big Data in a nutshell

2. The future of insurance

  • Some industries more advanced than others
  • Retailers, banks, insurances, telecom are heavy data producers and users
  • Data often used for profits
  • 2 types of (big) data usages : keep costs down vs. increase revenues
  • Adaptive pricing ("Pay for your own risk")
  • insurance companies need more "user-centric" data
  • Example 1 (real) : car insurance
  • Example 2 (fictitious ?) : health insurance

Increase revenues = sell more

Keep costs down = control risks

Profile risk evaluation (insurances)

Mortgage risk evaluation

Selfscan control

forecasting

Churn prediction

Cross- and upselling

Behavior-based pricing

Couponning

PREDICTIONS

  • Insurance companies will investigate new (intrusive) forms of data collection
  • In Belgium, smart boxes will not be adopted
  • Data currently collected does NOT improve significantly predictions
  • market structure impedes adoption (4m units in Italy vs. 30k in Belgium)
  • New variables need to be included in model (e.g. health-related with wearables)

1. The future of retail

  • Parallels between retail evolutions & Big Data
  • 3 major evolutions in retail landscape

bypassing retailers

Retail evolutions

data capture of in-house consumption: replenishment and forecasting

in-store distribution

manufacturing

Consumption at home

Retailer 1

supply-chain

Client A

Brand

supply-chain

Client B

Retailer 2

Retailer 1

supply-chain

Brand A

Getting customers away from competitors

understanding total purchases to make consumer switch (coupons)

order automation to get rid of temptations (Nespresso)

Client

supply-chain

Brand B

Retailer 2

PREDICTIONS

Dr. Pierre-Nicolas Schwab

Big Data manager RTBF

Founder IntoTheMinds

Researcher ULB

  • Data inflation seems to be the only way to go
  • New variables increasingly captured (time of consumption for instance) giving new possibilities (forecasting) but also increasing firms' bargaining power over consumers
  • New data collection methods threaten established business models
  • Real-time processing will enable new customer experiences (personalized coupons when you enter the store ... not after you have shopped)
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