Find and Communicate the Story - Ray Poynter - Lesson 1

Finding and Communica-ng the Story – Lesson 1 of 6 Ray Poynter, 2016

Finding and Communica-ng the Story

Lesson 1 of 6

Overview and Introduc-on

Ray Poynter

February 2016


Introduc-on

•  An Introduc-on and Overview -‐ Feb 23 •  Working with Qualita9ve Informa9on – Apr 5

•  Working with Quan9ta9ve Informa9on -‐ May 24

•  Working with mul9ple streams & big data -‐ July 5

•  U9lizing visualiza9on – Sep 13 •  Presen9ng the story -‐ Nov 8


Why?


Movie Popcorn Center for Science in the Public Interest (CSPI) In 1994 it revealed Medium bag of popcorn = 37 grams saturated fat USDA (United Stated Department for Agriculture) Recommended maximum = 20 grams

Made to S9ck – Chip and Dan Heath

20 37

0

20

40

Grams Fat

USDA

Popcorn


Robust Simplifica-on

Movie Popcorn Data About double your daily fat allowance

Visual Story A day’s worth of unhealthy food


Movie Popcorn What was leN out?

•  Other things in the popcorn, e.g. salt & sugar •  Large bag of popcorn •  Other foods eaten in movie theatres

•  Other comparators, e.g. normal daily meal


Movie Popcorn Think, Feel, Do

•  Think, the ‘facts’: –  Popcorn has saturated fat, saturated fat is not good, there are limits

•  Feel –  3 junk meals in one day is dumb/gross

•  Do – When you are about to buy popcorn, think about all that fat, feel a bit sick/scared, do trade down or out



3 Steps

1.  Find the story 2.  Create the story 3.  Communicate the story


Created by Dudolf hdps://goo.gl/uwB4Ru

Where is the Panda?


Finding the Story

Intui-ve Genius – Great at finding the message in the data, just seems to play around with the numbers and out pops the story

– Usually bad at training others and ohen poor at working in teams

Systema-c approach – Frameworks


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) –  Systema9c methods of analysing data –  A preferred method for extrac9ng the story and linking it to the wider context


Define the Problem “A problem defined is a problem half-‐solved” Sources of informa9on: –  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 ac9ons 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 exis9ng knowledge: – Within your organisa9on – 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: Descrip-on

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

Assump-ons and predic-ons Who What

1.  ______ ______ 2.  ______ ______

Simplified


Assembling the Evidence

•  Quan9ta9ve – Standardize? Missing Data? Indexing? Re-‐basing?

•  Qualita9ve – Transla9ons? Transcripts? Notes?

•  The nature of the sources – Credibility? Bias? Interac9ons?


Is My Data Right?

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


Spurious Correla-ons


Normalizing by Growth Pa\erns

Forbes: hdp://bit.ly/NewMR_208


Different Perspec-ves


Findings Need a Comparator

RFID


Finding and communica-ng the story in health data


Bad news for men in Eastern Europe

Eurostat -‐ hdp://goo.gl/r2q526

Amenable Deaths Per 100000 of popula9on -‐ 2012


Over 100,000 Avoidable Deaths in England and Wales

114,740

393,050

2013, England and Wales 506,790 Deaths

Avoidable

Other

UK Office for Na9onal Sta9s9cs: hdp://goo.gl/oJYMgo


USA and Smoking Leading cause preventable deaths

hdp://www.cdc.gov/healthreport/publica9ons/compendium.pdf


Embedding Frameworks

•  Establish your framework

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


Finding the Story 1.  Know what the ques9on 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 excep9ons? 4.  Determine how the message in the data

answers the business ques9on and crah 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 paderns

•  What is the big picture?

•  This will frame the detail

Qual •  Read the transcripts •  Create notes and memos

•  How does it compare with what is already known

•  What are the main messages

In the context of the Business Ques9on


The Tenuous Link Between Finding the Story and Telling the Story

•  In finding the story we have mul9ple data sources •  We have differing degrees of confidence in those sources –  A conjoint study with consul9ng 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 customer might be a poor way to find the story, but it can be a great way to tell the story


The Lead

Nora Ephron When Harry Met Sally Sleepless in Sea8le

1st Day in Journalism School 5 Ws (Who, What, When, Where & Why?) Asked to write the Lead for the school newspaper “The en9re 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!


The Cartographer and the Journalist


What are the key findings?

1.  Link to the project objec9ves 2.  ‘Need to know’ not ‘nice to know’ 3.  Supported by paderns or themes in the data –  Not just a single data point

4.  Clear findings –  Re-‐work the data groupings to make the findings

clearer


Developing your narra-ve 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


Good news? Bad news? Hypotheses and beliefs crucial

•  Good news •  Good news with warnings •  Bad news, but it can be ameliorated

•  Bad news “It’s good and bad” means you don’t understand the problem, the data, or both


Hans Rosling The impact of religion on the number of babies per woman hdps://goo.gl/nwZq29


Some people say Popula9on will keep growing Because some religions stop women from having few babies Is it true?


Had to crate their own map. Size of bubbles = popula9on


In 1960 you had to be rich and Chris9an to have few babies per woman & be rich – or be Japan

But in La9n America and Africa, many countries were Chris9an, poor, and had many babies per woman


Uses 9me as narra9ve axis. Showing number of babies falling, across religions, irrespec9ve of wealth.


By 2010 about 80% of people live in countries with 2 babies per woman.

All Eastern religion countries about 2 babies per woman. Propor9on of Chris9an and Islam majority countries similar. You can be poor and have few babies, but not VERY poor.


Hans Rosling hdps://goo.gl/nwZq29

Watch the whole video Plenty more points made What Rosling did to get to his story? How does the story work? What did he leave out and why?


The Big Picture •  Frameworks for reliable / effec9ve stories •  Define the problem •  Find the main message •  Remove anything that is not the story •  Is it good or bad news, confirming or challenging expecta9ons/beliefs

•  Engaging, memorable, simple story


Schedule

•  An Introduc-on and Overview -‐ Feb 23 •  Working with Qualita9ve Informa9on – Apr 5

•  Working with Quan9ta9ve Informa9on -‐ May 24

•  Working with mul9ple streams & big data -‐ July 5

•  U9lizing visualiza9on – Sep 13 •  Presen9ng the story -‐ Nov 8


Further Reading

Published by Wiley, 2004


Video Gegng to Great Research Stories

hdp://newmr.org/play-‐again/fes9val-‐of-‐newmr-‐atlan9c-‐monday/


Thank You!

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