Analytics Time to Grow Up

The Data Party

2

2012

http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

The Data Party is Over

3

2012

http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ https://gigaom.com/2014/03/19/the-sexy-era-of-big-data-is-over-as-vcs-turn-to-fund-unexpected-industries/

2014

Insight

What Does Good Look Like?

4

Data

01

Action

02 03

Turning data into

insight and

action?

Transforming

data into

actionable

insight?

The Reality: Lots of Moving Parts

5

One size does not fit all

Pricing

Customers

Competitors

Business Plan

People

Skills

Tools

Platforms

KPIs

Data

Structure

Maturity Is Not One-Dimensional

6

Data

Reporting

Analysis

Optimisation

Personalisation

Risk of Box Ticking

7

Data

Reporting

Analysis

Optimisation

Personalisation

Got some data?

Is the data in a report?

Hired an

analyst?

Done a test?

Switched on personalisation?

Risk of Oversimplification

8

Data

Reporting

Analysis

Optimisation

Personalisation Reporting requires high-level data

Optimisation requires granular data

Visualisation

MVT

Analytics Maturity

9

Time to Grow Up

Modelling

Big Data

Predictive

Business

Intelligence

Test & Learn

DMP

Streaming

AVOIDING THE HYPE

SPEED, SCALE & “SHINY”

ACHIEVING BALANCE

DATA VERSUS DECISIONS

01

02

03 PRIORITISING

SKILLS, TOOLS AND PROCESSES

Visualisation

MVT

Analytics Maturity

10

Time to Grow Up

Modelling

Big Data

Predictive

Business

Intelligence

Test & Learn

DMP

Streaming

AVOIDING THE HYPE

SPEED, SCALE & “SHINY”

ACHIEVING BALANCE

DATA VERSUS DECISIONS

01

02

03 PRIORITISING

SKILLS, TOOLS AND PROCESSES

Avoid The Hype 1: Speed

11

Speed is not in itself an outcome.

Humans rarely make (good) decisions in real-time.

Speed often arises only from

automation, which comes after

analysis.

The need for speed becomes a

barrier to investing in longer term

analytics with much higher ROI.

Avoid The Hype 2: Scale

12

Rubbish In = Rubbish Out

Applies at every size of data

Critical success factors are the

same regardless of size:

Relevancy

Accuracy

Coverage

Structure Rubbish

Massive data

Small Data

Medium data

Big

Data

Avoid The Hype 3: The Shiny

13

Useful Shiny

Visualisation

MVT

Analytics Maturity

14

Time to Grow Up

Modelling

Big Data

Predictive

Business

Intelligence

Test & Learn

DMP

Streaming

AVOIDING THE HYPE

SPEED, SCALE & “SHINY”

ACHIEVING BALANCE

DATA VERSUS DECISIONS

01

02

03 PRIORITISING

SKILLS, TOOLS AND PROCESSES

Balance is Key

Analytics maturity is two-dimensional

Constant trade-off between:

Sophistication and availability of data

Capacity to make effective decisions

15

Data

Decisions

Maturity

Data Famine

Trying to run sophisticated models and make extremely granular automated decisions…

… without investing in getting decent data to feed the process.

16

Data

Decisions

Maturity

Drowning in Data

Overinvestment in data capture and systems integration…

…lack of capacity or understanding to respond to the data flows

17

Data

Decisions

Maturity

Maturity Model

18

Educated Guesswork

Benchmarking

Understanding

Optimisation

Competitive

Advantage

Data

Dec

isio

ns

Maturity Model

19

Educated Guesswork

Benchmarking

Understanding

Optimisation

Competitive

Advantage

Ad Hoc Capture Measurement Design Real-Time Flows

Data

Dec

isio

ns

Maturity Model

20

Educated Guesswork

Benchmarking

Understanding

Optimisation

Competitive

Advantage

Ad Hoc Capture Measurement Design Real-Time Flows

Automation

Test & Learn

Statistical Analysis

Business Intelligence

Basic Reporting

Data

Dec

isio

ns

Maturity Model Example

21 Measurement Design

Statistical Analysis

Data

Dec

isio

ns

Understanding early

behavioural triggers to

purchase

e.g. a key signal of being

in the market for a

holiday is performing

multiple searches with

different dates on travel

sites

Maturity Model Example

22

Optimisation with critical link between analytics

(segmentation) and testing (variant) data e.g. quickly understanding how a test performs for

different audience/customer segments or varies across devices before it is too late

Real-Time Flows

Test & Learn

Data

Dec

isio

ns

Visualisation

MVT

Analytics Maturity

23

Time to Grow Up

Modelling

Big Data

Predictive

Business

Intelligence

Test & Learn

DMP

Streaming

AVOIDING THE HYPE

SPEED, SCALE & “SHINY”

ACHIEVING BALANCE

DATA VERSUS DECISIONS

01

02

03 PRIORITISING

SKILLS, TOOLS AND PROCESSES

People

Process

Technology

0

5

10

I q II q III q IV q

Resourcing Debate

24

Analytics Nirvana?

What do you think is the optimal balance between technology, people

and process to achieve analytics nirvana?

2014 Econsultancy/Lynchpin Measurement & Analytics Survey

25

Refocusing the Debate

26

35%

? 45%

20%

What are the best

processes to invest in?

What are the right

technologies to buy?

Who are the right people

to hire?

Technology

Digital is increasingly Just Another Data

Source for visualisation and modelling tools

Success not always about having the best

tool

More often about using the right tool for the

right job

27

Technology

0

5

10

I q II q III q IV q

Investing in Process?

28

PROCESS

=

ROI

Process Determines ROI on Analytics

What are the objectives?

What is the most appropriate methodology?

Is there a genuine balance between:

Improving data quality/relevancy/accuracy

The capacity to respond to that data?

29

ROI Comparison

30

Digital Analytics Tool

Re-platform

return

0%

Rare to break-even in first

year once cost of change is

taken into account

Customer Retention

Model

return

+25-30%

Average 25% - 35%

performance improvement

from targeting customers

that are likely to lapse

Attribution Project

return

3-6%

Average 3 - 6%

performance improvement

from evidence based

reporting and some budget

reallocation

People: Critical Skills

• Strategy

– Objective setting is 20% of the time but 80% of the importance of

most analytics projects.

31

• Engineering

– Measurement, data flows and databases needs to be

designed, not hacked together.

• Analysis

– Understanding the business model is as important

as understanding a statistical model.

Critical Skills

32 Ad Hoc Capture Measurement Design Real-Time Flows

Automation

Test & Learn

Statistical Analysis

Business Intelligence

Basic Reporting Engineering

Analysis

Strategy

Analysis (is not) Magic

33

Experience plays a massive part in the process

Finding new

avenues of

exploration

Knowing how far to

go down an avenue

before pulling back

Fitting data to

purpose and

questions

Preparing and

manipulating data

so it’s in the right

shape

Beware false

dawns of speed,

scale and

shininess

5 Take Home Points

34

Analytics Maturity

01

Process

determines ROI:

choose the right

analytics

projects wisely

02 03 04 05

Maturity is not

one-

dimensional:

one size does

not fit all

Balance

between data

and decisions is

vital for analytics

success

Invest in the

critical

engineering,

analysis and

consulting skills

35

Educated Guesswork

Benchmarking

Understanding

Optimisation

Competitive

Advantage

Ad Hoc Capture Measurement Design Real-Time Flows

Automation

Test & Learn

Statistical Analysis

Business Intelligence

Basic Reporting

Data

Dec

isio

ns