A N A L Y T I C S A S T H E D R I V E R O F V A L U E

P E T E R L A F L I N

P E T E R . L A F L I N @ B L O O M A G E N C Y . C O . U K

@ S Y S T E M S P E T E R

The importance of language

Courgette or Zucchini?

The importance of language

Chips?

We’re falling into the trap of talking about a number of different

disciplines and roles with the same term, whilst failing to recognise

other things as being data science.

We talk about the same things in different ways, and different things

in the same way.

The importance of language

W E S H O U L D L O O K T O

M A T H E M A T I C S T O P R O V I D E

A C O M M O N L A N G U A G E

The importance of language

The importance of language

The importance of language

The importance of language

The importance of language

D A T A S C I E N C E

V S

D A T A A N A L Y S I S

The Process?

Am I a scientist if I:

• Make random observations about the

weather?

• Collect some weather data?

• Use an IoT device to collect detailed

weather data?

• Show someone a graph of the weather

data I’ve collected?

• Use my data to produce a basic model of

how weather changes near my house?

• Combine my data to provide a robust

abstraction of the laws of “weather”?

• Mathematical Proof

• Mathematical Modeling

• Statistical Interference

• Heuristics

The Scientific Process

With science, we start with a hypothesis and set up experiments to generate

data to test our hypothesis.

With data science, we could take “all of the data” and find reasons to prove or

disprove anything.

W H E R E D O E S D A T A

S C I E N C E F I T ?

If we gave an infinite number of monkeys an infinite number of typewriters,

we’d surely end up with the script to Hamlet.

Emile Borel - 1913

Correlation does not imply causation

Everyone in data science – from 2000 onwards

I S V A L U E O U R G O A L ?

SHOULD WE CARE?

IS GETTING THERE MORE IMPORTANT?

Different Sectors – Different Goals

Public Sector / Government

Transparency, Efficiency, Accountability, Linkage

Value for Citizens from Government

Commerce

Shifting from being service led to being led by the user

Balancing Value for Users & Shareholders

Academia

Looking for global truths, rather than local results

Providing long term foundations to sustain value

Even before academic mathematicians became aware of [the need for data

sciences] the genie was already out of the bottle.

Many companies and institutions simply could not wait for the mathematical

research community to catch up with the applications.

The solutions to disruptive challenges and the novel opportunities

so created were simply too valuable. P. Grindrod – The Mathematical Underpinnings of Analytics 2015

C O N T E X T I S E S S E N T I A L

SCIENCE

Data Science process

Facts – which

everyone should agree

on, including the

assumptions on which

they are based.

Conclusions –

which may differ

depending on the

context of the

analysis being

performed.

Strategy – where

the insight is mixed

with domain info to

decide the next

steps.

B U T I S T H I S A L I N E A R

P R O C E S S ?

We need a consistent, robust, formal system that allows us to combine facts

in logically consistent ways across domains, businesses and problem sets.

How do we ensure that facts from different problems, subject to different

assumptions, approximations, models and veracity models, can be combined

effectively in a way which minimises error?

How do we build a data science proof?

I S S E L F S E R V I C E

A N A L Y T I C S P O S S I B L E ?