Dr Harvey Lewis - Trends in Big Data, Key Challenges for Skills
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Transcript of Dr Harvey Lewis - Trends in Big Data, Key Challenges for Skills
Deloitte UK screen 4:3 (19.05 cm x 25.40 cm)
© 2014 Deloitte LLP. Private and confidential.
Trends in big data Key challenges for skills
© 2014 Deloitte LLP. Private & Confidential
Dr Harvey Lewis, Deloitte Analytics
Big Data for Security and Resilience: Challenges and Opportunities, 7 March 2014
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Deloitte and big data
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Deloitte and big data
• 7,000 people worldwide in the
Deloitte Analytics community
• 700 people in the UK
• Gartner ‘magic quadrant’ for
Global BI and Performance
Management
• Dedicated analytics facilities,
including data ‘laboratories’,
visualisation environments and
training suites
• Predictive Analytics Centre of
Excellence
• Technology-independent
− IBM, Microsoft, Oracle, SAS, Tibco,
Tableau, QlikTech, SAP, R, etc
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© 2014 Deloitte LLP. Private and confidential. 4
Deloitte UK screen 4:3 (19.05 cm x 25.40 cm)
© 2014 Deloitte LLP. Private and confidential. White markers indicate position
of Deloitte Drawing Guides
7.40 cm
2.91 Inches
6.00 cm
2.36 Inches
8.00 cm
3.15 Inches
11.70 cm
4.61 inches
0.50 cm
0.2 inches
0.50 cm
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To view Deloitte drawing guides:
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on screen’
3. Select ’OK’ Big data definitions
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Big data
Small data
Any data
Real-time
Velocity Volume Variety
Large data
“Data that is too rich or complex to analyse
well in a spreadsheet and without concepts
from university-level statistics”
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Retailing in a hurricane
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Crowd monitoring at a major event
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0
100000
200000
300000
400000
500000
600000
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93
Ob
serv
ed
Vo
lum
es
Time
Observed crowd estimate along river zones
Royal NavalCollegeZone 10
Zone 9
Zone 8
Zone 7
Zone 6
WestminsterBridgeVauxhall Bridge
Lambeth Bridge
ODA Zone 4
ODA Zone 3
ChelseaEmbankment
Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 Zone 9 Zone 10 Zone 11
Zone 1 40% 44% 29% 1% 1%
Zone 2 65% 27% 27% 2% 1%
Zone 3 30% 19% 40% 25% 4% 2% 1%
Zone 4 1% 1% 26% 55% 19% 5% 1%
Zone 5 5% 20% 37% 43% 6% 1%
Zone 6 1% 3% 20% 35% 44% 3% 1%
Zone 7 3% 51% 45% 5% 2%
Zone 8 9% 15% 63% 22%
Zone 9 2% 3% 12% 57% 29%
Zone 10 33% 66%
Zone 11 2% 1% 2% 1% 1% 1% 94%
Zone 1 Zone 3
Zone 4
Zone 5
Zone 6
Zone 7 Zone 8 Zone 9
Zone 10
Zone 2
Zone 11
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Identifying sentiment, emotion, indicators and warnings
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0 1 2 3 4 5 6 7 8 9 10 11 12
Social Media CDC Timeline News Media
Maturity / Investigation Complete
Pre-conditions for Takeoff
Timeline in Weeks
Dev
elop
men
tal
Stag
es
Takeoff
Infancy Stage
What How
Source Where
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Finding influencers in organisational networks
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(Skills) challenges for security and resilience
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Over-reliance on technology
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Proprietary big data
technologies
Open source big data
technologies
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Leaving no stone unturned
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Selecting the wrong data
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Believing more data = more information
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Mistaking correlation for causality
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Number crunching not data-backed insight
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𝑦 𝑥 =𝑒 𝑏0+𝑏1𝑥
1 + 𝑒 𝑏0+𝑏1𝑥
𝑏0 = 2.298057
𝑏1 = 30.023823
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Not recognising the creepy line
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Trends
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Big data return-on-investment
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‘Bigness’ of data
Marginal Value
Positive ROI
Source: Deloitte LLP
Marginal ROI Negative ROI
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Seven big data trends
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Right talent doing
the right things
A picture is worth a
thousand numbers
Machine learning
finds its niche
Data discovery
platforms
Doing good with
data
The rise of
predictive data
The death of the
data warehouse
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Dealing with big data (1)
21 Source: Deloitte LLP
Right talent
Machine learning
‘Bigness’ of data
Marginal Value
Death of the data
warehouse
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Dealing with big data (2)
22 Source: Deloitte LLP
Data discovery
‘Bigness’ of data
Marginal Value
Visualisation
Doing good with
data
Predictive data
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Image source: NASA/courtesy of nasaimages.org.
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About Deloitte
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© 2014 Deloitte LLP. All rights reserved.
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