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BigDataParis2015_VUCANOLOGY

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by

Hugues Le Bars

on 12 March 2015

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Transcript of BigDataParis2015_VUCANOLOGY

Send.Receive.Connect
Send.Receive.Connect
REV :
1113 M€
EMP :
6000
CUST :
800 000
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SHIPPING

DIGITAL

GRAPHICS
ISIN :
FR0000120560
BigData Adoption - VUCAnology rudiments
5Vs
Descriptive, Predictive, Prescriptive
Governance
Security
Data lake
Data visualization
BigData Challenges are understood
So How ?
Observe, Understand, Transform
"Organizations which design systems
are constrained to produce designs which are copies of the communication structures of these organizations."
CONTEXT - VUCA
"Give me a turbulent world as opposed to a quiet world, and I'll take the turbulent one"

V
olatility
U
ncertainty
C
omplexity
A
mbiguity
V
ision
U
nderstanding
C
larity
A
gility
ORGANIZATION - 3 HORIZONS
CULTURE - HORIZON 3
DATA REFINERY
DATA LAKE
Method - Lean Startup
BigData Maturity
C.R.U.De Oil Digital Energy
Hugues Le Bars <h.lebars@neopost.com>
Chief Data Officer

@hugues_le_bars

http://linkedin.com/in/hugueslebars
Horizon 1
Focus on profitability
KPI on :
Efficiency
Customer retention
Incremental innovation
Metrics :
Profit
Margin
Costs
Horizon 3
Focus on discovery
KPI on :
Learnings
Risks
New Business Models
Metrics :
Time
Minimum Viable Products
Horizon 2
Focus on expansion
KPI on :
Performance
Customer Acquisition
Speed
Metrics :
Growth
Market Share
Execution
Growth
Experiment
"Build your sanctuary of velocity"
"Mistakes are
the portal
of discovery"
James Joyce
"Ever tried. Ever failed.
No matter.
Try again. Fail again.
Fail better."
Samuel Beckett
"Validate, invalidate your assumptions quickly"
Make your assumptions :
Customer
Customer problem
Solution
Define your riskiest assumptions
Validate your MVP and persevere
Or invalidate and Pivot
"No Business
Plan
survives the first contact with customers"

Ask Webvan, Iridium, Zynga,RIM...
bigdatamaturity.knowledgent.com
Adopt
Operate
Collect everything
Dive in anywhere
Flexible access
Conversion = Desire - Friction
Target a maximum scale and insight with the lowest possible friction and cost.
Public Authorities
Customer : Toronto District School Board
Problem : School closures. Which Schools ?
Business Intelligence
Manufacturing - Supply chain
ACTION
Complications :
Irreversible decision
Social impact
Human biased analysis and decisions
1600 parameters, 1 millon rows
Complex,huge, long calculations
Solution :
Unsupervised Machine Learning
Attribute Reduction
Demographics, social data, location data
Results :
Enables evidence based decisioning
Cost reduction
Initial human biases removed
Social Insights
Internal adoption : 3 months
Results :
Single source of truth
Insights on Customer churn
Internal adoption : 2 months
Customer : NEOPOST Business Analysis
Problem : CRM, Machine Data Correlations
Complications :
Siloed operational data sources
Archives
2 years of historical data
Structured/Heterogeneous data
Solution :
Hadoop + Cloud MVP
Real Time Datavisualization
Customer : Supply Chain Core Business
Problem : Connect altogether
R.E.A.C.H constraints
Supplier monitoring
Manufacturing Quality
Complications :
Siloed distant data sources
Regulations moving constantly
Manufacturing Quality is accurate
Purchasing consolidations
Worldwide coverage
Any failure is not acceptable
Solution :
On premise Hadoop
Results :
Cost reduction on storage and processing
Consistent insights
Information retrieval
Instant compliancy checking
Melvin Conway, 1968
Andy Grove
Steve Blank
THANK YOU
BigDataParis 2015 - @hugues_le_bars
Since :
1924
"Scan with Ubleam app"
@

BigDataParis 2015 - h.lebars@neopost.com
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
BigDataParis 2015 - @hugues_le_bars
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Full transcript