DATA - THE SOURCE OF CURIOUS INSPIRATION

(AND PROGRESS)

Slipstream

15 Apri l 2015

HERE’S AN IDEA: DATA IS THE CREATIVE MEDIUM OF THE

FUTURE! NOW WHERE ARE THE ARTISTS AND WHAT DO

THEY LOOK LIKE?

PURPOSE and MEANING

ROCKSTARDATA

Professor Hans Rosling

Stats that reshape your world view

Western World

Third World

Swedish Students - Karolinska Institutet 1

We

Them

Long life in small family

Short life in large family

1 Hans Rosling - TED Talk 2006

LIBERATE DATA, INSPIRE PEOPLE

Our souls are fed by Curiosity and

Creativity. Data provides a Medium for

achieving this and the tools and methods

already exist for anybody to become the

Artist.

This is BOB

?

Take a

Number!

IT

Speed to Opinion(Weeks/Months)

Opinion-Based Data-Driven

Conventional Self-Reliant

Want

Can

8 %

People

Experience

Time

Want Can

80 %

Speed to Insight(Seconds/Minutes)

Top performing companies:‐ 5x more likely to be data driven‐ Improve productivity by 5 % and profitability by 6 %

Tell a Story.

Start a

conversation.

INSIGHTS - ITS EVERYWHERE AND ITS EASY

BUT UNDERSTAND WHAT THE CREATIVE LANDSCAPE LOOKS LIKE!

DATA IS EVERYWHERE

Social Media

Device Generated

Machine Generated

BIG DATA

UnstructuredData Lakes Internal Systems

Data

Warehouse

Connect

CreateShare

Informal Sources

• http://public.tableau.com/profile/sadethesage#!/vizho

me/CashonHand1/CashonHand

• http://public.tableau.com/s/gallery

LIBERATE DATA , INSPIRE PEOPLE

Imagine if every person had the freedom to transform

data into knowledge easily and accurately.

People need to easily explore, understand, create and

act.

Accessible, data-based insight are the lifeblood of

tomorrow’s organisations and Societies – making them

responsive and innovative.

SPATIALLOCATION PLANNING

8 km

8 km

Aged 18 - 35Income R6.4k to R25.6k

UrbanEmployed

SHOPPING CENTRE

THE ZONE @ ROSEBANK

TARGET DEMOGRAPHIC

YOUNG & ASPIRATIONAL

Data: RSA individual demographics

Data: Shopping Centre (SC) locations

Convert X-Y coordinates to Spatial objects

Find closest SC to each individual, calc proximity

Filter out individuals not within trade area

Filter (split) targeted individuals and others

Count targeted individuals and others in each SC

Ratio of targeted individuals : all

Sort by Ratio, descending

TARGET DEMOGRAPHIC

Young and aspirational: Aged 18 - 35, Income R6.4k to R25.6k, Urban, Employed

Shopping Centre Num. Targeted Individuals Num. All Individuals Percent TargetedBel Air Shopping Mall 2616 14872 17.59Rivonia Crossing 6792 43379 15.66Clearwater Mall 10171 70772 14.37Fourways Crossing 2001 14753 13.56North Riding Square 4051 32173 12.59Monte Casino 2103 17154 12.26Brightwater Commons Mall 4799 40169 11.95Cresta Shopping Centre 5627 56748 9.92Rivonia Square 3329 34543 9.64Cedar Square 3117 35595 8.76Hyde Park Corner 1439 17918 8.03China Mall 8137 101541 8.01The Glen Shopping Centre 10911 142742 7.64Northgate Shopping Centre 5345 71139 7.51Nelson Mandela Square 1035 14022 7.38Greenstone Shopping Centre 10353 143666 7.21Fourways Mall 32 464 6.9Lakeside Mall 9017 140616 6.41Southgate Mall 6530 102756 6.35Westgate Shopping Centre 7034 114029 6.17East Rand Mall 9076 147378 6.16The Zone @ Rosebank 5643 100035 5.64Eastgate Shopping Centre 8440 154396 5.47Key West Shopping Centre 5671 116502 4.87China Mall West 5465 116370 4.7Sandton City 1309 29601 4.42Melrose Arch 3203 82871 3.87Trade Route Mall 4385 131962 3.32Jabulani Mall 11382 353851 3.22Maponya Mall 5556 250246 2.22Dobsonville Shopping Centre 4107 227348 1.81

PREDICTIVELOGISTIC REGRESSION

• Age

• Job

• Contact method

• Furniture / Clothing accounts

• Previous marketing campaigns

• Current campaign

PREDICTOR VARIABLES

WHAT WE KNOW ABOUT THE LEADS

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Model ‘Score’

Succ

ess

Rat

e (R

ealit

y)MODEL PREDICTION PERFORMANCE

Identify data that is relevant to

the business goal

Integrate and enrich the data

into an analytical data set

Run predictive algorithms

to find the model

Test the model to make

sure it will work

Use the model

in applications

Measure the effectiveness of

the model in the real world

Predictiveanalytics

Understand data

Monitor

Deploy

Evaluate

Prepare data

Model

FEEDBACK CYCLEIn Theory

Understand data

Monitor

Deploy

Evaluate

Prepare data

Model

FEEDBACK CYCLEIn Practice

Survey

Model

Score (Evaluate)

THANK YOU.