Making a decision changes a state of possibility into a state of intention. Like turning batter into cake. Intention followed by action followed by reflection is the only route to learning. Like eating the cake and seeing how it tastes.
The making of a decision is the point at which multiple possibilities resolve to the choice of a path forward. Ideas become plans, intuitions become direction and opinions give way to responsibilities. There is a transition from one state to another through discussion and consideration of relevant information. At that point, action is needed.
Once a decision is made, that cake cannot be unbaked, you need to try it to know what you think of it and how to improve the next.
What’s your style for making decisions? And what does that mean about how you use data?If you’re an analyst then what decision-making styles do your stakeholders have? And how do they vary? What does that variation mean for how you talk to them and deliver to them?
We all have different personalities which bring different preferences for how we do things. Just as there are various leadership or management styles, there are different decision-making styles.
Here are three models for how to think about decision-making styles. Whichever model resonates for you the most, it’s interesting to reflect that some personal styles are going to lean more naturally towards using data than others. Something to consider for data literacy, data strategy and data culture initiatives as well as your day-to-day analytics delivery.
Top of this morning’s google search of ‘decision making’ brings up the totally sensible, totally logical 7 step model: 1) Identify the decision 2) Gather information 3) Identify alternatives 4) Weigh the evidence 5) Choose among alternatives 6) Take action 7) Review the decision
As if. Let’s be honest, this is not how humans work. And even if they did, real business contexts often do not allow for this type of process to take place.
Real decision-making can often be a fuzzy, ambiguous, fluid, organic and emotional activity. When we make decisions we will all bring our own unique combination of head, heart and gut. Heads bring knowledge, opinions, questions and logic. Heart brings conscience, ethics, sympathy and personal incentives. Gut brings instincts, experience and an eye for risk.
Bringing Data to the table can provide information and frameworks that allow for the best of what our heads, hearts, and guts can provide with curiosity, structure, measurement, assessment, critical thinking and insight. An insightful approach brings the art and science of decision-making together.
How can we make really good decisions? With our heads, hearts, guts and data.
In briefing for delivery of data, analytics and insight I’ve asked the following questions:
Who is it for?
What is their objective/what are they trying to achieve?
What is the issue/pain point/business problem they are dealing with?
What decisions are they looking to make?
What kind of action and change do they hope will result from those decisions?
Once you know these answers you can start to ask:
What do they want to know?
What questions do they have?
What data are available to investigate?
The question that I most want to find out about from the decision-maker I’m working with is: How will you be making your decisions on this matter?
For data to truly inform or drive decisions, for data to truly help improve the quality of decisions, discussing how decisions are made is vital. Knowing what decisions, made by whom, when and how are key pieces of information that help data, analytics and insight delivery provide the support needed for good decisions to be made.
It’s hard to ask about the how of decision-making, and it takes self-awareness to start to answer it. Better decisions result when you do though.
In a self-driving car, inputs from the surrounding environment are processed, and outputs of speed, steering and navigation of the vehicle are modeled and executed. The assessments of risk, the prediction of outcomes and the ensuing actions happen with varying levels of automation depending on the level of automation.
The car model
Level 0 – no automation; the driver drives.
Level 1 – hands on; there is shared control of speed, steering and braking.
Level 2 – hands off; driver is still alert and ready to intervene immediately.
Level 3 – eyes off; the automated system is in control but can alert the driver to intervene in certain circumstances.
Level 4 – mind off; the driver can go to sleep but only in certain areas.
Level 5 – steering wheel optional; full control is with the system and there is no driver intervention.
The phrase data-driven decision-making is common. Yet, in the age of self-driving cars it is not a binary choice between whether a human or a system is driving the decisions.
The decision model
Level 0 – no automation. The decision-maker drives. They will use inputs of knowledge, emotions and incentives, plus their instincts and experiences alongside any objective data they have available (which they are also free to ignore).
Level 1 – hands on. Where there is strong trust in the information then aspects of decision-making can be strongly informed by insights and associated recommendations.
Level 2 – hands off. Decision-making can be automated from the data but decision-makers still check the information, insights and recommendations. Decision-makers confirm actions.
Level 3 – eyes off. Decision-maker relies on automated systems for decisions in a particular area, with the exception of times when an alert triggers them into corrective action.
Level 4 – mind off. Fully automated prediction and decisions running in a system – e.g. automatic marketing journeys where messages and offers are automated based on existing customer preferences and purchases. This will still be limited to certain activities and these parameters can change.
Level 5 – steering wheel optional. Full control is with the system and there is no decision-maker intervention. Complete trust in the predictive outputs of the automation and full autonomy with those building the system.
How well do you think the self-driving car metaphor stacks up as a way of looking at this spectrum of control between decision-maker driven and data-driven actions?
Data Culture fits into Organisational Culture via its Decision Culture.
Every organisation has a Decision Culture which influences how decisions are made. This will be determined by stated values, beliefs and behaviours – statements that appear in an organisational strategy, governance documents and written policies and processes. The Decision Culture will also be influenced by unstated norms of behaviour – people’s real incentives and how decisions actually get made everyday.
Decision Culture is made up of: decision awareness (the awareness that you are making a decision and have choices about how decisions are made), accountability (somebody somewhere owns every decision and its follow-through), and the mix of emotionality and rationality that goes into the decision-making process (we all use our heads, our hearts and our guts when making decisions).
Working on Data Culture is the work of seeking to improve the use of data for the rational part of the decision-making mix. It involves sparking curiosity, building data literacy (knowledge about data, analysis and insight) and confidence. Together, curiosity, literacy and confidence enable evaluation thinking (how good is this?) and critical thinking (is that right?). More evaluation and critical thinking sharpens the rational part of the decision-making process.
Those working on Data Culture can easily get pulled into other aspects of Decision Culture and the overall Organisational Culture because elements of culture overlap and dynamically influences each other constantly. Whilst working on Data Culture can easily balloon into broader Organisational Culture change, it can help you retain your sanity if you remember where it fits into the bigger picture.
You’re a wood carver. You do a great job. Your work is valuable. You’ve built a reputation. People like working with you. They have more requests for you. New people want to work with you now they’ve seen your work. The list of requests is growing. The seeds you planted to grow your own trees have become a forest to manage. As well as carving the wood. It’s a good problem to have. You have support to grow what you do. You have plans. Not just to deliver more of the same, but to change how you deliver your work. Different trees, different carvings. You have budget to bring in a bigger team. The requests keep coming. It’s a good problem to have. You’re a victim of your own success, right?! Just need some headspace to make a plan. Just need more people to make the headspace. Just need time to find the people to take the load off. You were tending your forest. Now you’re fighting forest fires that keep growing. If this is a good problem why does it feel out of control? Just need some more firefighters in here with you. Yes? No.
You make a firebreak. You control the current fire. You stop it growing any further. You breathe. You sit in a clearning to remember why you do what you do and where you want to go. You don’t have to do it alone. Maybe there’s someone who can help with this. Someone who’s been there. You lean on them now. You make a plan of what to do and how. It’s not perfect but it’s good enough. You build your new team to tend the forest, not fight the fires. You realise that tending the forest is a different job from leading the team of foresters. You create boundaries in and around the forest that will stop future fires spreading. You plant new trees. You give it time and care. It will grow as never before.
The documentary series Inside Pixar, includes the brilliant story of Jessica Heidt, one of the animation studio’s script supervisors.
Jessica’s story got me very excited because it is a story about research, curiosity, data and about how evidence alongside advocacy can make change.
Jessica works on scripts at Pixar, she keeps track of all script changes through the multitude of versions that any production goes through, making sure all the right characters are saying the right lines in the right order to tell the story to the audience.
She read some research about the gender imbalance in film scripts and it got her thinking about how she had a unique perspective and opportunity to look at this issue at Pixar. Starting manually with a spreadsheet, Jessica started counting the number of lines of dialogue and the number of characters in a script. She then compared the number of male and female characters as well as the number of lines that male and female characters had. Working on Cars 3 at the time, she found that the male to female script ratio was around 9:1. Jessica started conversations on this topic with her colleagues and began regularly showing her script stats to senior staff at key production milestones. People got interested and changes would be made as the production went along – both the creation of female characters to add to the story, and the reallocation of lines of dialogue from male to female characters to balance out the general texture of the script.
Jessica then worked internally with a colleague to create a tool that would do the counting in the script software automatically – no more spreadsheets. In the documentary, Jessica describes her pride at Pixar’s latest release Soul achieving near gender parity. And it’s amazing to hear that it’s now part of Pixar’s vision to continue to measure and ensure balance in the future – not by imposing a 50:50 split in every film necessarily, but by aiming for a cumulative balance over the output of 5 years.
This story exemplifies how insight can drive change and is a fantastic case study for any insight analyst. Jessica cared about the issue of gender equality in movies and that led to her asking the question about Pixar’s own productions – how balanced were they? She found a way to produce clear and unbiased statistics to show the situation and she communicated that information to colleagues. Using the evidence in combination with personal persuasion, Jessica inspired changes to be made. She then worked on automating the process of data collection so that those stats could be quickly and easily reported at every key decision-making moment of the production process, embedding the insight and ongoing changes into the normal day-to-day activity of the company. Jessica Heidt is a worthy winner of Pixar’s Unsung Hero Award and an inspiration to insight analysts everywhere.
Whether building a new team or function from scratch (maybe it’s just you!) or scaling up a team, intentionally defining your purpose is a great way to get clarity on what’s important, which will help enormously with future prioritising.
Crafting a purpose statement is well worth working in a group to do, perhaps even with an outside facilitator. For an analytics team, your purpose covers why you are here, what you do towards the wider vision of the organisation, who you deliver to and how. Others have written better about how to do this.
Vision – Roadmap – Plan
Vision is the description of where you want to go – Liz Ryan from humanworkplace.com talks about long-term vision being the view from the clouds. Objectives and goals are the hilltop view, the big picture of what’s going on and the ability to see if you’re headed in the direction you wanted to go. Initiatives, projects and day-to-day tasks are down in the weeds at ground level. Additional to all of this, in analytics, there’s always the rabbit holes you go down from there into the actual data – so there’s many levels to our work!
My passion is for creating the staircases between cloud and hilltop, and then again between hilltop and ground level. My experience is that where there is too much separation between these viewpoints, alignment slips and work becomes disconnected from vision. As a result, people can feel isolated in their jobs without a sense of contributing to the wider purpose of whatever organisation they work for.
What creates the staircase between cloud and hilltop? The act of translating a long term vision into reality comes in two parts:
Getting really specific about what the vision means in reality. Define those terms and say what it would look, feel, sound and smell like in that future reality. As an example, many analytics teams set a vision of being a ‘sector leader’. The next step is to articulate in specific terms what it means to be a sector leader.
Roadmap from where you are now to where you want to be in a timeframe. An outline plan, a roadmap, sets out where you are now, what needs to be achieved to reach your vision point, the path forward and key milestones along the way. It is not a detailed plan, it must be flexible to change, it can’t be set in stone. However, it must give a sense of the order that change needs to come in. Some change is dependent on other change, certain things must therefore come first. A roadmap allows you to define what elements of your change are foundational:
what must be in place for you to succeed in the future?
what changes are needed to enable other changes?
what is most important and will create most value?
what order do you need to do things in? What is for now, what’s next, what’s for the future?
What creates the staircase between hilltop and ground? The act of translating your roadmap into a plan involves:
Planning out what you need to do now
Setting objectives with short timeframes and specific deliverables
Measurement and Pride
The integrity of the staircases from ground to hilltop to cloud viewpoint is strengthened by both measurement and pride. It is vital to put in place some form of measurement of how you are doing, to enable you to adapt, improve and change direction as needed.
From ground to hilltop, the measurements can focus on whether tasks are being completed and how well.
What outputs have you delivered?
Are you doing what you said you were going to do?
How well are you doing what you said you were going to do?
From hilltop to cloud, the measurements can focus on whether the work you are doing is having the kind of outcome and impact that you wanted – are you moving closer to your vision?
What are the outcomes of what you have delivered? What has happened as a result of your work?
How well do these outcomes show an improvement on the past?
How much closer are you to your vision?
What about Pride?
Perhaps this is just another corny quote but I like it (and have no idea who said it):
Don’t wait until you’ve reached your goal to be proud of yourself. Be proud of every step you take toward reaching that goal.
This is incredibly important as an ingredient in effective measurement. Any evaluation, KPIs, monitoring, tracking and reporting could be improved by thinking about what there is to be proud of from the work done. Learning lessons about what to improve is, of course, essential too. But without pride in what has gone well with acknowledgment and celebration of that, learnings will feel weighty and burdensome. You can be proud of successes, and proud to have been bold enough to fail, knowing now something that you did not know before.
The Pride Spectrum
As an individual or as a team, where do you fall on the pride spectrum? How might you aim more for the middle? What are you proudest of? How do you celebrate success?
Here we continue the series exploring what’s needed to scale analytics and insight teams (parts one and two, and three here).
What type of flow are we looking at? All of them…
Flows from stakeholders to the team
Flows from the team to stakeholders
Flows between multiple stakeholders
Flows between analysts within the team
Flows between analysts and other collaborators outside the team
Flows between team leadership and analysts to support and guide
When a team is small and the team members can keep most of the information they need for day to day business in their heads, a neatly managed document system is nice to have, but you can definitely get by with free and loose as long as people get what they need when they need it.
To scale to a medium or large team, you need to decide how things are done and where documents are held. If you don’t, people produce inconsistent documentation (inefficient and can harm reputation), and people don’t know where to find docs (inefficient and plain frustrating). Additionally, by making these decisions, you set in place important parts of the collaboration workflow which enables people to work together easily. For this last part, there are many advantages to opting for cloud-based documentation by default such as Google Drive or OneDrive.
Document Management model
Advantages of Cloud solutions
Remote workflow – it’s easier to work directly in a browser than through a networked connection such as VPN.
Single document collaboration – comments, suggestions and edits can be incorporated on a single shared collaborative document. No more emailing of documents, no more multiple versions, no more collating changes between multiple versions edited by different people on different drafts. And people get to see each other’s comments which is more transparent and enables fuller reflection.
More chat, less email – when you don’t need to email docs, you can opt to share docs and chat back and forth via a chat platform such as Google chat, Teams or Slack. This saves you from long email chains and enables more informal and quickfire conversations to evolve as you work with others. And if things are sensitive or confidential, then the chat can be private too.
Functionality of online docs – browser based versions of Word/Docs, Excel/Sheets, Powerpoint/Slides or whatever it is you use are still not as good as their desktop siblings. Sometimes they don’t do what you want or need them to and you have to revert to desktop, or cry, or both.
Reliance on the internet – unavoidable with reliance on the cloud. Fairly unavoidable in any remote working situation since your VPN connection also won’t work without internet. It’s not really feasible to keep an up-to-date copy of everything on your local computer. Mitigate by investing in good internet connectivity.
Reliability of the system – issues like storage space, back-up, network speed do arise. This is only a disadvantage if your set up is a bad one and these issues are not dealt with. Mitigate by investing well and maintaining your set up, you would have to do this for a non-cloud solution too.
If you don’t like change, you won’t like adapting to new ways of working – I can’t help you here.
I think the advantages of a well thought through cloud-based document management workflow vastly outweigh the disadvantages (most of which are simply bad management). I’m biased on this but all I can say on it is that done right, it works very well and you can just get on with it and not worry about filing.