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



Deloitte and big data

2



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. 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

0.2 inches

11.70 cm

4.61 inches

To view Deloitte drawing guides:

1. Right-click on slide and select

’Grid and Guides...’

2. Check ’Display drawing guides

on screen’

3. Select ’OK’ Big data definitions

5

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”



Retailing in a hurricane

6



Crowd monitoring at a major event

7

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



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



Finding influencers in organisational networks



(Skills) challenges for security and resilience

10



Over-reliance on technology

11

Proprietary big data

technologies

Open source big data

technologies



Leaving no stone unturned

12



Selecting the wrong data

13



Believing more data = more information

14



Mistaking correlation for causality

15



Number crunching not data-backed insight

16

𝑦 𝑥 =𝑒 𝑏0+𝑏1𝑥

1 + 𝑒 𝑏0+𝑏1𝑥

𝑏0 = 2.298057

𝑏1 = 30.023823



Not recognising the creepy line

17



Trends

18



Big data return-on-investment

19

‘Bigness’ of data

Marginal Value

Positive ROI

Source: Deloitte LLP

Marginal ROI Negative ROI



Seven big data trends

20

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



Dealing with big data (1)

21 Source: Deloitte LLP

Right talent

Machine learning


Marginal Value

Death of the data

warehouse



Dealing with big data (2)

22 Source: Deloitte LLP

Data discovery


Marginal Value

Visualisation

Doing good with

data

Predictive data



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dr harvey lewis - trends in big data, key challenges for skills

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