Data: A Guide to Humans
By Phil Harvey and Noelia Jiménez Martínez
Why data is important, how companies and governments are using data, and, crucially, how you can use data to improve your business, your life, or maybe even the world.
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What is the book about?
Data is humanity’s most important new resource. It is not only important to business, it matters to everyone. Data has the capacity to give us new insight into every aspect of our lives, this planet and the universe at large. Exploiting the value of data will change us, as a species, for the better. As much as, if not more than other technological revolutions. Not only does it change what we know, but also how we know it.
The more we understand about data, the more we understand about ourselves, our world, and how to change it. And yet this precious new resource is being underused, misused, or just downright ignored.
Our book, Data: A Guide to Humans, will help you understand why data is so important, how companies and governments are using data, and, crucially, how you can use data to improve your business, your life, or maybe even the world.
But data without empathy is useless: the current problem with data science and data analysis is that we forget about humanity; the people who make up the data, the people who work with the data and those expected to understand the results. In the world of data, empathy is a powerful tool that will unlock and amplify success.
Understanding the technical landscape of data products from formats, standards, quality, landscape to the tools used by data providers and data consumers is vital, but so too is understanding human needs and feelings. It is this crucial last part – the humanity - that elevates our understanding of data from a purely technical implementation to something which can make a lasting and essential contribution to humanity.
Why are we writing this book?
This is a book about data, but it is also a book about people. We are writing this book because the world is at a tipping point and the way we use data defines how we see people. Even in this, the seemingly most technical area, empathy matters.
We must be empathic towards each other locally and globally, especially towards people of different cultures, races and genders from ourselves. And before it’s too late, we must empathise the ecosystem in which we exist.
In this book we want to open the conversation on empathy from all sides. From the technical and non-technical as may arise in business. From human diversity to the biodiversity of the planet. These topics sit next to each other in our book because they sit next to us all in our lives.
Who is this book for?
- If you are deeply scientific and/or technical and ‘don’t care about people’ but care about our impact on the planet
- if you are a manager and wish technical people ‘would just do what they are told’
- if you are trying to bridge conversations between the above two warring factions
- If you are working in data in any form and want to know how to be more effective and successful in your data work.
- If you are purely interested in how people are using data to shape our world.
Where has this book come from?
This book has grown out of our experience of working in data for the last 10 years as well as lecturing and researching data empathy. It includes insight from our work with hundreds of people across data science, data engineering and data philosophy. It will be packed with examples of good and bad behaviours and well as providing a new way to think about and approach data work.
But as long as data and tech teams all over the world are lacking in diversity, that vital contribution of different voices, backgrounds and opinions, we will never be able to accurately assess the valuable potential of data we need to understand humanity on a truly global scale, and so our book argues too for greater inclusion across the world of data science.
The possibilities of data are endless: from simple visualisation showing something new in a well-trodden space to Artificial Intelligence and the full amplification of human potential. Our chapters will cover why empathy leads to success, the technical details of empathy and data quality. But at its heart this is a book not just about data but about empathy, and how one is useless without the other.
Noelia Jiménez Martínez
Phil works as a Cloud Solutions Architect for Data & AI in the Microsoft One Commercial Partner organisation.
He is on the front line with innovative partners looking to make the best of Microsoft Cloud & AI technologies.
Phil is passionate about all things data, especially how it interacts with people. He has a 10 year history with programming and data engineering.
Phil believes that data represents a fundamental shift in the way we can know things in all walks of life. Previously to Microsoft Phil has worked in such diverse industries as Chartered Surveying, Architecture, Advertising and as the CTO of a data technology start-up.
He started his career with a BA in AI from Sussex, he is also an international keynote speaker, model, author and big beardy geek.
Noelia is Head of Data Science and Astrophysics at Unbound. She holds a PhD in Numerical Astrophysics applied to Galaxy Formation and Chemical Evolution. Prior to joining Unbound she worked as a Data Science Consultant in London, and before that, in another life, as an Astrophysics Researcher. Her postdocs gave her the chance to live in different countries across Europe and collaborate with a huge diversity of people from several fields and backgrounds. She enjoyed very much teaching and mentoring students in different fields, such as Machine Learning, Astrophysics and Statistics. If you wonder, she is originally from Argentina and yes, she can dance tango!
Empathy; a core skill in data.
Empathy has a very simple definition: the skill of understanding and or sharing the feelings and needs of other people. While psychologists study the levels of natural empathy a person has in their base character, the empathy we are interested in is that which can be learned . Whatever naturally occurring empathy exists within us, it is possible to learn or increase our empathy as a skill too. Especially when it comes to your work and existing as a productive member of an organisation.
When it comes to data, Empathy is a core soft skill. Being more empathetic makes you more effective when working with data.
Throughout this book we are going to explore different scenarios where empathy in data applies and ways you can practice and develop this skill. There are things you can do to be more effective across the full lifecycle of a project. From its inception and development, through early stage prototyping all the way through to full operationalisation and retirement.
Empathy is not a new consideration when it comes to technology. With each new development it has been considered and discussed under many different guises. With data, I have decided to tackle this head on and call it what it is. Data Empathy.
Empathy is often discarded and seen as innate or ignorable. You either have it or you don’t. If you don’t, it’s not your problem. This is profoundly untrue. Even the least empathic individuals can learn the skill of empathy to be wielded to huge impact. Dark as it may be, serial killers, learn how to understand and manipulate human emptions to entrap their victims. Like a spider and its web. You can learn this too, and use the powers for good!
To put Data Empathy in context, it’s worth looking at how other areas of technology have handled this discussion.
When you consider the intersection between people and technology you find a field called ‘Human Computer Interaction’. It is from this field that two areas of work have emerged under the mantle of software engineering. User Experience (UX) and User Interface (UI).
UI is the discipline focused on the ‘what’ and ‘how’ of creating interfaces for a computer. Whether this is a mouse and keyboard or the mechanics of getting you something to click on or a box to type in.
UX is the discipline focused on the ‘why’ and ‘how’ of creating interfaces for a computer. You can see that UX/UI cross over in the how. The difference, and the importance of UX is beautifully demonstrated in the real world.
When people are involved, every system has a UI and a UX. Data systems have them. More importantly, data itself has a UX. That user experience is often hidden behind the UI/UX of the system or software used to access the data. (For simplicity I will use “system” to mean software or system from now on).
Historically, the system has been the only interface to the data and as such the user experience of the system has been the user experience of the data. Users experienced data through an application built to handle that data. Power Users or Data Base Administrators (DBAs) experienced that data through code (Most usually Structured Query Language - SQL).
The emergence of Big Data as an industry (from about 2010) broke data free of a discussion of application and SQL and allowed us to think about data differently. This is not a book about Big Data, too many of those have been written already. We now can look at data as it really is and not bound to any single application.
Your popularity awaits.
Stripping back the layers of history and application and looking at the data, naked, uncovers a complex mess. It’s this mess that inspires me to roll up my sleeves and get my hands dirty.
In every project, in every piece of work and organisation you will find all kinds of bad behaviour – usually unintentional – that need to be carefully unpicked and fixed. Committing to this uncovers the value in the data asset, no matter how big or small.
Engaging your new data empathy skills, caring about and liberating the users of data from experiences where they weren’t considered will surprise and delight and make you both successful and popular.
Empathy and success.
It is straight forward to see why caring about the needs and feelings of other people will make you more popular – it is an integral part of positive social interactions.
In a purely social context it is easy to see why caring about the needs and feelings of other people will make a person more popular. We are social animals after all.
However how will empathy make you more successful in a technical context?
Firstly, we need to define what kind of success we are talking about. I am defining success here as the delivery of the result of a piece of work that is accepted by the intended audience on their own terms to either provide value in the form of demonstrable desired improvement and/or a clearly defined set of new understanding that redefines the scope of action.
Let me break that down a little further.
1. ‘The delivery of the result of a piece of work’ – the assumption here is that you have been doing work to seek some form of outcome. The effort you have put in has not only been for personal satisfaction.
2. ‘accepted by the intended audience on their own terms’ – the group of people to whom the work is relevant are able to understand and validate the result of the work against a set of measurements that they have defined themselves.
3. ‘provide value in the form of demonstrable desired improvement’ – that the work results in a state better than that when it started. For example, improved profit, customer retention, donations or brand perception.
4. ‘clearly defined set of new understanding that redefines the scope of understanding’ – this is a fancy way of saying ‘learning from failure’. Any piece of work can teach you something even if the desired value didn’t occur. Working towards an end with data means that you are learning and evolving your understanding. For your intended audience to accept and grow with you is success.
Secondly, how does empathy help with this success?
Empathy means that you are able to think about each of the points 1 through 4 from the perspective of the needs and feelings of your intended audience and any other groups impacted by the work.
From the perspective of the direct intended audience any presentation that speaks directly to their needs and feelings will be more understandable. To include your considerations about other groups impacted will allow your audience to see other angles of the work done. This will put your work in a greater context, and more context makes things easier to understand.
An audience who is asked to accept ‘your terms’ as opposed to their own will need to be ‘convinced’ that your perspective, needs and feelings take precedence over their own. You are asking them to ‘give up’ their position and this is a lot of emotional work for them and for you.
Results provided without empathy can ‘fall flat’ and it can be hard to understand for the presenter when they have poured time, energy and effort into producing these results. This can lead to frustration and a greater divide between audience and presenter.
These people are helping to fund Data: A Guide to Humans.
Fawzia Zehra Kara-Isitt