Finding the story in the data - Ray Poynter - 2015

Learn How To Find The Story In The Data Ray Poynter, UK, December 2015

Learn How To Find The Story In The Data

Ray Poynter December 2015

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Why are we interested in storytelling?

Memorable

ABenCon Grabbing

Easier to understand

Gives coherent message

Shows we understand it


The data doesn’t speak for itself


Storytelling – NarraHve Theme Wake Breakfast Travel Work Lunch Work Drinking Travel Sleep

Get changed Warm up Run Warm down Shower Get changed

•  Smallpox emerged about 10,000 years ago

•  300-‐500 million deaths during 20th Century

•  One of the first to be tackled by vaccinaCon

•  Declared exCnct in 1979 •  One of only 2 so far

(Rinderpest) •  Let’s tackle others, e.g. Polio


Frameworks Most of the teams that reliably produce good analysis and useful stories use frameworks –  Individuals are less dependent on frameworks

Elements of frameworks –  How to frame the problem –  Linking the project to a wider context –  A standard method of organising the data (qual and quant) –  SystemaCc methods of analysing data –  A preferred method for extracCng the story and linking it the wider context


Further Reading

Published by Wiley, 2004


From Data to Stories 1.  Define the problem 2.  Establish what is currently known/believed 3.  Check and organise the data 4.  Find the message in the data 5.  Cra` and tell the story

Starts when the request for a study emerges. It does NOT start when the fieldwork finishes.


Define the Problem “A problem defined is a problem half-‐solved” Sources of informaCon: –  The request for a study –  The proposal –  Discussions between sponsor, insight team and supplier

•  What is the background to the project? •  What would success look like? •  What acCons should follow from the research? •  What do people think the results are going to be? (Or, what are the prevalent hypotheses?)

Smith & Fletcher, 2004


Establish What is Already Known?

•  The frameworks approach avoids focusing on just the current research project

•  The analysis, the validity, and the story need to blend research with the wider context

•  The context is a web of exisCng knowledge: – Within your organisaCon – Within the agency/supplier –  In the public realm


Who is the project for? _________________

What is the business issue/problem that is being addressed? __________________________________________________

What does the business want to do, once it has addressed this issue? ______________________________________________________

What do we already know? Item Held by: DescripHon

1  ______ ______ ______________ 2  ______ ______ ______________ 3  ______ ______ ______________

AssumpHons and predicHons Who What

1.  ______ ______ 2.  ______ ______

Simplified


Assembling the Evidence

•  QuanCtaCve – Standardize? Missing Data? Indexing? Re-‐basing?

•  QualitaCve – TranslaCons? Transcripts? Notes?

•  The nature of the sources – Credibility? Bias? InteracCons?





Normalising by ‘Share of’


‘Share of’ is a relaHve measure


Normalizing by Coding

•  SenCment analysis, open-‐ended comments converted to PosiCve, NegaCve and Neutral

•  DigiCzing from analogue to binary

•  AllocaCng to segments

•  Scoring different elements –  (think American Football or Rugby, different points for different events, leading to points in a league)


Normalizing by Growth PaXerns

Forbes: hBp://bit.ly/NewMR_208


Is My Data Right?

We see paBerns, even when they are not there. Image from Viking I, 1976 Mars – led to theories of intelligent life.


Spurious CorrelaHons


HRT and CHD

•  Several studies showed that women taking HRT were less likely to suffer from coronary heart disease

•  Some leading doctors propose that HRT was protecCng women against CHD

•  Randomised Controlled tests showed that HRT created a slight increase in risk of CHD

•  Huh! – Women taking HRT were typically from higher income, healthier groups in society – who have lower rates of CHD


Embedding Frameworks

•  Establish your framework

•  Share it with colleagues •  Share it with suppliers •  New projects can be designed to produce inputs that work well with the framework you are using


Finding the Story 1.  Know what the quesCon is. Have an idea of

what success looks like. 2.  What is the big story? – What do most people do? Why do most people do it?

3.  What are the relevant excepCons? 4.  Determine how the message in the data

answers the business quesCon and cra` that as a story.


Find the Total Picture First Then the relevant detail

Quant •  Look at the Total Column

•  Look for big numbers and big paBerns

•  What is the big picture?

•  This will frame the detail

Qual •  Read the transcripts

–  Unless you conducted the fieldwork

•  Create notes and memos

•  What are the main messages

In the context of the Business QuesCon


Where does the best MR come from? Column % Which of the following best describes you? Countries Merged

Total Research or Consultancy Supplier

Supplier to the research industry

Research Buyer/User

Academic + Other English Speaking Non-‐English Speaking

UK 63% 61% 60% 92% 40% 66% 60%

USA 51% 52% 50% 46% 60% 52% 50%

Germany 18% 13% 30% 15% 60% 16% 21%

Australia 15% 14% 15% 15% 20% 16% 12%

Canada 11% 8% 20% 0% 40% 9% 14%

France 7% 7% 10% 8% 0% 7% 7%

Japan 5% 3% 15% 0% 0% 3% 7%

Brazil 3% 3% 5% 0% 0% 3% 2%

China 2% 1% 5% 0% 0% 3% 0%

Italy 2% 1% 5% 0% 0% 0% 5%

Other 8% 10% 10% 0% 0% 9% 7%

None of these 11% 15% 5% 0% 0% 9% 14%

Column n 109 71 20 13 5 67 42

The wrong approach to starCng analysis


Where does the best MR come from? Column % Which of the following best describes you? Countries Merged

Total Research or Consultancy Supplier

Supplier to the research industry

Research Buyer/User

Academic + Other English Speaking Non-‐English Speaking

UK 63% 61% 60% 92% 40% 66% 60%

USA 51% 52% 50% 46% 60% 52% 50%

Germany 18% 13% 30% 15% 60% 16% 21%

Australia 15% 14% 15% 15% 20% 16% 12%

Canada 11% 8% 20% 0% 40% 9% 14%

France 7% 7% 10% 8% 0% 7% 7%

Japan 5% 3% 15% 0% 0% 3% 7%

Brazil 3% 3% 5% 0% 0% 3% 2%

China 2% 1% 5% 0% 0% 3% 0%

Italy 2% 1% 5% 0% 0% 0% 5%

Other 8% 10% 10% 0% 0% 9% 7%

None of these 11% 15% 5% 0% 0% 9% 14%

Column n 109 71 20 13 5 67 42


0% 10% 20% 30% 40% 50% 60% 70%

Which Country Produces the Best MR?

The Big Message

Big story

QuesHons Why are the UK & USA so high/different? Is this true for everybody? What are the implicaCons of this?


The Cartographer and the Journalist


The Lead

Nora Ephron When Harry Met Sally Sleepless in Sea1le

1st Day in Journalism School 5 Ws (Who, What, When, Where & Why?) Asked to write the Lead for the school newspaper “The en3re school faculty will travel to Sacramento next Thursday for a colloquium in new teaching methods. Among the speakers will be anthropologist Margaret Mead, college president Dr. Robert Maynard Hutchins, and California Governor Edmund Brown.” All the students wrote about the 5Ws – good, but not right.

The Lead? No school next Thursday!


Different PerspecHves


The Tenuous Link Between Finding the Story and Telling the Story

•  In finding the story we have mulCple data sources •  We have differing degrees of confidence in those sources –  A conjoint study with consulCng surgeons might be our best source for finding the story

•  The best way to convey the story does not have to rest on the ‘best’ data –  A vox pop video with a paCent might be a poor way to find the story, but it can be a great way to tell the story


What are the key findings?

1.  Link to the project objecCves 2.  ‘Need to know’ not ‘nice to know’ 3.  Supported by paBerns or themes in the data –  Not just a single data point

4.  Clear findings –  e.g. In the chart UK and USA were a long way

ahead in terms of Best Research


Hans Rosling

1. What is his key message? 2. What is the story? 3. What has he le` out? hBps://youtu.be/jbkSRLYSojo


Hans Rosling & NarraHve Theme? Key Message:

–  It is possible to tackle world health problems

Key Story: 1.  200 years ago short-‐life expectancy was the norm, then the West

moved ahead, but over the last 50 years most countries have caught up

2.  There are some countries sCll behind, and some regions of other countries, but since most of the world has been solved, the rest can be

Key narraHve axis: –  200 years from 1810, from bad to good


What Did Hans Rosling Leave Out? Numbers: –  A few dates, 3 life expectancies, 3 income levels –  Based on 200 countries and 120,000 numbers

DefiniHons: – Which 200 countries? –  How did he deal with country amalgamaCon and fragmentaCon?

517 other staHsHcs: –  GapMinder lists 519 key global stats, over Cme


Using an Insight


You’re not you when you are hungry •  People behave differently when they are hungry

•  Snickers is big enough to end the hunger

•  Global campaign –  Local execuCons

•  Sales increase –  e.g. USA sales +8%


Developing your narraHve theme

•  Select your primary axis

•  This is the elevator pitch •  Use a structure that works with the audience •  Typical USA structure –  The main finding was X, so we recommend Y & Z – Now, let’s tells you why it is X, and why are it’s Y & Z –  But it can be different in different places


The Big Picture •  Develop a framework approach •  Define the problem before you try to find the answer to it

•  Put the research project into the context of what is already known

•  What do you want the client to think, feel, do a`er hearing the results? –  The story is a device to deliver that acCon


Thank You!

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Finding the story in the data - Ray Poynter - 2015