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Painting pictures with data: Emma’s year of adventure.

How’s this for a year’s project?

THE RULES:

Do something new each day
Do it with someone else
Document it

Pretty daunting, huh?

That’s why I’m not doing it. But Emma Lawton is. That’s incredible. What’s more incredible is that in 2013 at the age of 29, Emma was diagnosed with Parkinson’s. And, like so many people with Parkinson’s, she radiates a determination to focus on the things she can do more than the things she can’t.

Late last year Emma sent a call out at the Parkinson’s UK office to anyone who wanted to spend a lunchtime with her teaching her something. I wrote back and said she should come and geek out with me and Myuran over some analytics. She said yes. Hooray! A win for spreadsheets!

In prepping for Emma’s session, it was tricky to decide how to approach ‘analytics’ as a subject for an hour’s chat in a way that would do anything other than scratch the surface. It’s a vast topic that can easily get quite boring about stats if that’s not what a person is interested in. Instead of that, we did what we often do with clients and chose to focus on the person rather than the ‘data’. We took inspiration from Emma’s up front questions to us which were all about how important we regarded learning new things. Combining that idea with the knowledge that Emma’s own professional background includes design, we decided to play around with dataviz.

It turned out to be a fruitful starting point to ask Emma about her own project, regarding the year she was planning out and acting on as a potential dataset. She told us she had a planning spreadsheet that she was very proud of and would we like to see it (would we ever – hooray for spreadsheets!) Then we started asking questions. What did she really want to achieve by doing this? What kinds of stories would she want to be telling people? What pictures might she paint to illustrate those stories? What information might she collect that could form the material for those pictures if we thought of them as graphs or infographics?

Analysis can be defined as the summarising and visualising of information for the purpose of gaining insight.

Whilst Emma is writing a blog post for every activity filled with rich qualitative information about her experiences and thoughts on each, summarising and visualising the project as a whole after a year is more challenging based on reminiscence and journalling alone. That’s where data capture comes into play. By noting down a few pointers in a set format for every activity in a spreadsheet (hooray!), she can build material she can later mine for patterns, patterns she could draw. She could capture almost anything about her activities, but based around the ideas she gave us that the project was about people and about how doing this stuff made her feel, we kept focus there. The data capture includes names and dates, information on the activity, how the person she meets is connected to her and various measures of how she feels about the activity including how new, challenging and satisfying it was plus the brainwave of a dropdown list of emojis for overall feel.

Is this reductive? Yes.
Can we capture the richness of Emma’s emotional experiences with dropdown lists of ratings and emojis? No.
Might it allow the viewing of wood rather than trees? Yes.
Might it reveal something of her experience that might otherwise remain hidden? Yes.

Meeting, speaking and sharing ideas with Emma was a joy and I felt that in that hour we explored in microcosm what we do on any project with any client. We meet people where they are and focus on what they tell us is important to them. We then work with them to think of how data can be useful as a lens through which to look, not to the exclusion of other lenses, but in addition for the provision of a different angle.

I can’t wait to see what Emma’s year ends up looking like to her.

Catch Emma’s analytics blog post at the fuck it list. And do explore the rest of her adventures. Perhaps there’s even something she could come and learn with you?

Why ‘I don’t have time’ is a lie to yourself and others

At work, time poverty is a lie, an illusion. In a company where everyone works the same hours a week, time is cancelled out as a factor in the productivity equation. Time doesn’t exist.

So what truths does that leave?

‘I don’t know what I’m trying to achieve’

Sounds simple, but your goals and objectives need articulating on paper, out loud and revisiting often.

Why are you at work? What are you doing there? What will be produced or delivered to prove you did something? What changes will have been made, how will things look, what will things feel like when you’ve done what you’re doing?

‘I take on more than I can do’

Workload is a real issue. A volume of work that is unrealistically high will hamper productivity. Knowing your objectives will make this one easier. Making conscious choices about what you say yes and no to is then possible.

‘I’m not clear on my priorities’

So you have said no to some things but still feel time poor for what you have on your list and you’re stuck on how to prioritise. Knowing your objectives also makes this one easier. When you know what you’re focusing on, you can rank different tasks, choose to work on the important ones. Urgent is not the same as important and you can choose to work through that issue too.

Admit and accept that when you say
‘I didn’t have time for that’,
you actually mean
‘That is not my priority right now, I chose this other thing instead’.

‘This is taking longer than I thought. I don’t actually know how to do this task’

Trying to complete tasks that you do not possess the skills, knowledge or experience to successfully execute is time consuming. And unnecessary.

Identify what’s needed that you don’t have.
Who can help you?
Swallow your pride and ignore any inner voice telling you not to ask for help.
Reach out and ask questions.
Accept help offered, learn what you need to, or share the task with others.

‘This is taking longer than I thought. There’s likely to be a more efficient way to do this that I haven’t explored’

You do possess the skills, knowledge or experience to successfully execute your task. But the way that you’re doing it is time consuming. Open yourself up to new methods, or solutions for automating parts of the task you’re trying to do.

Creating efficiencies or automation itself takes an investment of time and effort. But that up front investment is repaid on every occasion you repeat the task and reap the reward of the time saved then.

Creating more time is an impossible problem that nobody can solve.
Good news is that you can solve any and all of these other problems.

Who, what, why and Venn

Vacation is a good time to contemplate life and career, fulfillment and frustration. It’s a good time to assess if you have the right balance in your life, and, if not, what might be missing.

And what better way to make that assessment that using the favourite of data visualisers everywhere – the venn diagram.

ikigai

I enjoyed examining this and relating it to my own situation. I even found myself unpicking some of it: aren’t mission and vocation the wrong way round? Depends on your understanding of the semantics I guess. Also, what does it mean to be excited at the same time as complacent and uncertain? What kind of people actually find themselves occupying this space? It reminded me just how powerful venns can be for exploring concepts.

What I loved the most was trying to decide the tweaks I can make to draw me closer to the middle. For me, it’s allowing myself to ensure that I continue doing the things I love, and not be drawn too strongly out of the LOVE circle and into that space between profession and vocation. I think this happens to me sometimes. In future I’d like to remember to make choices that are enriching for me too, rather than always fall into the ‘pleaser’ habit of prioritising what others are asking me for.

Data vs Insight skillsets

Growing any analytics or insight function requires hiring the right skills for the jobs. Those jobs include data jobs, analysis jobs, and various shades of soft skill jobs like stakeholder development, project management, communication, influencing, storytelling and the list goes on.

Whilst there are some unicorns out there whose talents straddle all things techy data as well as business knowledge, insightful thinking, and fabulous communication skills, these people tend to be rare and expensive. We’re all in search of the ‘geek who can speak’!

Teams need the right mix and any strategy for growth must assess what, and therefore who, is needed.

In thinking through this recently for our own planning I found myself drawing up this skillset comparison. Before anyone gets insulted, I’m not saying people on one side can’t do the things on the other. But it is my opinion that most people will fall more strongly one side or the other. I myself am firmly on the insight side of the equation. I do have strong technical skills and could even code up a model if I had to (probably!) But the work overall benefits from me delegating tech tasks to someone with a strong data skillset and concentrating my efforts on the insight tasks.

Skillsets

It’s tough to know how best to develop yourself in this sphere. It’s hard to keep developing in depth in all areas. I often feel I want to increase my techy skills but it’s equally important that I develop in line with my strengths which are for planning, project management and people stuff.

Quantifying personal transformation: the altMBA

I’ve just finished my run at the altMBA. I recommend it!

I had high expectations going in. In fact, nothing sort of transformational would have met those expectations – especially for the money! (Although I was very fortunate to get a contribution from my organisation – thank you Parkinson’s UK.)

What happened?

The stats
4 weeks
~111 students in my full international cohort
~25 students/coaches in my timezone cohort
~ 4-5 students per learning group – assigned to a learning group per week for discussion.
17 channels on slack where we discussed topics and supported each other
13 learning group meetings
13 projects shipped (more if you were mad enough to do extra!)
13 reflections on what was learned from those projects
~70 comments made by fellow students on my work to help me learn
~100 comments made by me on the work of fellow students
5 1/2 books read (7 were sent so I’ve still got some work to do there)
~1000* online articles read and videos watched, countless more on my follow up list
3 timezone cohort meetings online
1 special online workshop
1 meet up in person with a fellow student
1 coaching session 121 where I cried because things were so challenging to manage
2 eyes opened to my own ideas, motivations, strengths, weaknesses and blind spots
(*I’m not going back to count them, so this might well be an exaggeration…)

Items I can’t count
New connections made, friendships forged, messages sent
Ideas, experiences, vulnerabilities, stories, troubles and successes shared
Questions asked, challenges made, different angles explored, ‘aha’s and fist pumps

Areas covered
Thinking frameworks flexible enough for work and life
Exercising the creativity muscle – in business and life
The power of empathy and sonder – in marketing and life
Embracing vulnerability
Getting stuff done to deadline, no excuses
Working with others generously, but not letting them off the hook
Giving and receiving feedback well
Thinking big
Identifying the barriers and laying plans to do the hard parts first
Much, much more – it would take a book to flesh out the things I learned

Value gained
Immeasurable and keeps growing every day with practice of the learnings

What now?

I wanted to feel changed by this experience, and changed I feel. As well as tired, stunned, nervous for the future and excited for the future. A little time is needed to digest everything that has just happened. But then it will be back to work with new energy, new optimism, new ideas and a new approach.

Tell your story! Easy to say, hard to do. Here are the tips that help.

Storytelling is vital. Whether for making data accessible to an audience or for any other reason.

We all know this, we’re told it all the time, mostly by Seth Godin! We’re surrounded by stories and we all realize that to create connection, to persuade, to make change, we have to tell our own. And to get anywhere, we better get good at it. So why is it still so damn hard to do it well? How come I forget how to do it every single time I come to write?

Fundamentally, it’s easier to start with the internal than the external.
‘What do I want to say?’, ‘what product do I want to tell people about?’, ‘what’s my opinion on this?’
Working at a charity, we fall into this internal thinking trap all the time. We have so many messages we want people to hear, so many important pieces of news to impart, so many actions to invite people to take. And for communicating insight we get caught up asking ‘what do my findings say?’, ‘what my recommendation?’, ‘how can I make this graph just a little bit prettier?’

But to start with the external means to think of the reader first. Who are they? What might they be interested in? Why should they care about what I’ve got to say?

Every story needs to start from the outside and go in. Every story is a fruit that the reader peels back the layers of to get to the juicy, tasty bit in the middle. If you’re like me and need a helping hand offering up the best fruit then look no further than Bobette Buster’s handy how to:

It’s all gold but here’s the best bits that are easily actioned every day:

Tell your story as if to a friend, no matter who you’re talking to.
Choose a ‘gleaming detail’, the one ordinary moment, object or metaphor that embodies the heart of what you’re telling a story about. Hook onto the senses: what does that detail look, sound, smell, taste or feel like?
Be vulnerable. Dare to share the doubt, the anger, the surprise the joy.
Bring yourself. It doesn’t have to be about you to include you or your opinion.
Let go. Less is more.

Insight to Outcomes

This is a new outline that I and my analyst colleague Myuran have been working on as our department goes about their yearly planning cycle.

In setting up our insight function from scratch, one of the challenges I’ve found is how to properly evaluate the work and its impact. Often we have to chase our clients for feedback on what we delivered and ask them what they used the information for. It often takes time for insight to reap benefits, so it can be hard to measure tangible value early on.

However, this year we’ve drawn up this mapping to help us demonstrate what we achieve via different pieces of work and what the results feed into to. We aim to use this dataviz to help as context in future briefing and get clients focused on the decisions they want to make. Also, our client teams can use this themselves to map out the data-driven decision-making that they are taking care of themselves without coming through the insight team. That way we can demonstrate the ways that the department is becoming more insight-driven in its decision-making.

We’ll start using this more pretty soon and will no doubt find out if we’ve missed something!

Insight to Outcomes

(dotted lines around audience attitudes indicate that we don’t really do a lot of qualitative research ourselves at the moment, our insight team is quant based but we know that qual is important for the fuller picture on audiences).

Critical thinking – question your analyst!

If you’re the client for analysis, insight or research, keep these questions in mind when you get results presented back to you.

Analysts and researchers try to tease out useful information from data, whatever their methods. But their interpretation is in the end just one interpretation of what they have looked at.

Being critical doesn’t mean you’re saying ‘this is garbage, you’re wrong about everything!’ Being critical means being curious and having a healthy skepticism when information is presented to you as fact. In science, you learn to have your work ruthlessly pulled apart in order to have your ideas tested so you can improve what you’re doing. In non-profits people are often unsure of their data knowledge and that makes them too timid and too damn nice! Here are some questions to get your critical juices flowing. You don’t have to understand the data, you understand your work and you’re an intelligent adult who can form valid opinions based on information your are shown.

Being curious about alternative stories
Is there another explanation for that?

Debating findings
That’s the opposite of my hunch! That’s really not what I would expect, is it right?

Limits and assumptions of the analysis
How robust is this analysis?
How reliable and consistent was the data?
How did you get to your findings?

Insisting on clarity
What exactly does this show?
What does this mean for us? What can we do with this?
I didn’t get what you said – can you explain that again in English? (see There are no stupid questions)

Insight Analysis methodology – part 2

Part 1 explored the overall insight analysis cycle. This post unpacks the ‘Analysis’ part.

Often after briefing, analysts go off to crunch the data and it can seem like your project goes into a black box and you don’t know what’s going on until at some point, a powerpoint with pretty graphs appears with some results.

We find that different projects unfold in different ways, but we’re nearly always going through this same process to get from questions to answers.

Analysis steps

The first step of sorting out the data can be the hardest and most time consuming.
Working on the data definitions helps get everyone on the same page of what your story will be about (what do you mean by ‘active donor’? Someone giving a gift in the last 2 years, OK, anything else? What about if they engaged in some other way instead, does that change how we consider them?)

Clean datasets are hard to come by, but trash in means trash out so it’s worth at least deciding how good you think the data is and laying out the caveats and knowing your margins of error.

Everyone approaches analysis in their own style. For me there is no abc of data exploration, just like there’s no abc of how to be a detective or write a novel, people doing those things work at their craft and find their own ways and means.

Interpretation is the step many people will miss out, because it’s easy to present observations as though they are answers. Just as when you thrash out questions and hunches with clients you can tap into their gut feel about what’s going on (they do know their activity), when interpreting, check in with your clients for their opinions, I bet they will know something that will help you interpret a trend or other interesting finding.

Telling the story is also an underrated part of the process. You often only get one shot to put your findings across and influence what your clients take forward. Don’t be boring! Don’t hide behind your caveats! Tell the story. Make recommendations backed up by your findings and invite discussion and questioning of what you’ve found. The outcome should be some solid insight to take forward into action.

Insight Analysis methodology – part 1

I came to insight analysis with a mixed background. I had been a fundraiser, and because I’d worked in small organisations I’d tried my hand at many types of fundraising. The other side of my experience was from science where I studied psychology and neuroscience and worked on research on those topics. From that I learned scientific method, stats and data.

This is the general purpose methodology that I drew up as I was finding my feet in my non-profit Fundraising Analysis role, a role that was new in my organisation as well. I knew fundraising and fundraisers and I knew data and analysis but this brought those things together in a way that allowed me to explain the work to others.

Insight Analysis cycle

Insight cycle

What to bear in mind during the process

Objectives:
What do we want to achieve? (overall Goals – increase income/improve donor care)
What do we want to do? (plan for Activities – design a new product, improve a process)

Questions and Hunches:
Questions are often the place people start. There’s nothing wrong with that for brainstorming a starting point, but the sooner it links back to your objectives, the sooner you focus in on what you really need.
Bearing our objectives in mind, what do we already know?
What do we want to know – what will help towards our objectives?
Less is more! The fewer the questions you go into the analysis with, the more focused it will be and the easier to action or iterate onwards from.

Hunches (or hypotheses) about what you think the answers to the questions will be are really important. The move towards ‘insight driven decision making’ is often tagged as the move away from gut feel towards data. I find that using client intuition and experience to form hypotheses and to sense check findings as you go along keeps you on track to useful findings. Hunches are where you start to combine gut feel with the data.

Analysis: this is where you crunch the data. I unpack this in part 2.

Insight:
We have our answers, and a story that we can understand. What do we do now?

Action:
Information from insight going into planning actions.

Evaluation:
What does success look like?
How are we going to measure that?
Do we need any different/additional data capture?
Evaluation is analysis too, checking what happened, iterating back through insight, action and evaluation while you tweak or optimise whatever you were working towards.

And when you’re done – on to New Objectives!

In part 2 we unpack what happens under the hood of analysis.