data analytics time to grow up
TRANSCRIPT
AnalyticsTime 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?
Transformingdata 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
PersonalisationReporting 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
03PRIORITISING
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
03PRIORITISING
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
03PRIORITISING
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
21Measurement 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
03PRIORITISING
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
32Ad 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