I’m sure you’ve run into this situation yourself. You’re at a family gathering, and someone at the table asks you exactly what you do for a living. Maybe it’s your uncle, a grandparent, or a child. You try to describe in simple terms what you do, but they get a mystified expression on their face. You keep trying a bit, but it becomes clear you aren’t really getting through to them. Eventually, the conversation turns to another topic.
Your inability to explain what you do satisfactorily can even be the case when talking with someone who’s got a technical background themselves. They’re just not familiar with your field and focus, and can’t understand why you’d be excited by that field to begin with.
Bridging the Understanding Gap–Even with Yourself
Trying to bridge this gap of understanding can actually be a fun challenge. A co-worker of mine back during my PwC days called it “writing for Aunt Doris.” Consultancies like PwC staff over 6,000 roles for client projects, so there are lots of SMEs who have specialties most people know very little about.
But businesspeople have to work together, and consultants have to join teams on an ad-hoc basis, so there’s considerable information sharing within teams, not to mention across firms and of course between consultants and clients.
If you write successfully with a hypothetical relative who doesn’t work in your field in mind, you know you’re getting your message across to a general audience. And if you write in that same manner quite a bit, you come to realize that doing so helps bolster your own understanding of a topic.
Another way to look at this ability is to think in terms of the Feynman Technique, named for the Caltech physics professor who was famous for explaining difficult topics in a clear and simple way. The Feynman Technique includes four steps:
- Pick a topic to study.
- Explain the topic to someone unfamiliar with it, such as a child.
- Note the gaps in your own understanding.
- Simplify and illustrate with analogies and examples.
Using the Feynman Technique allows us to create, learn and share information by studying, writing, and teaching. You start with fundamentals, using examples and analogies to get your points across. The more you write for a general audience is, the more shareable the content.
Next I’ll try to explain to our hypothetical Aunt Doris what FAIR data is and why it’s important.
What is FAIR data?
When it comes to data, FAIR stands for findable, accessible, interoperable, and reusable.
FAIR data is data users can have confidence in more than once, for more than one purpose.
Why is FAIR data important?
Good healthcare is an example that everyone can identify with. Hospitals and clinics need to share lots of accurate and relevant data to help patients. Machines can help by giving healthcare professionals the data they need, when they need it, in the form they need it, directly to the point of care.
Ideally, machines retrieve helpful, accurate FAIR data in response to professionals’ questions or demands, data that they can trust then and there to guide them in making treatment decisions. The right kind of FAIR data delivered at the right time, in the right place, to the right people in the right form can help save or improve the lives of many patients.
If you think about it, any individual or organization needs FAIR data:
- Manufacturers need it to build trustworthy, useful products efficiently.
- Retailers need it to ensure products they’re stocking from a large number of suppliers are genuine and meet their requirements.
- Law enforcement needs it to apprehend valid suspects and to find and prove who’s responsible for crimes in a just and timely way.
- Individuals need it to live and thrive in today’s world. Unfortunately, we’re being forced to trust machines that aren’t always giving us the right information. That’s because the data isn’t suited to the task.
The Problem with Today’s Data
The more we depend on data to help us, the more we’re relying on systems designed by others we don’t know a lot about. Those systems are using and sharing data in ways that aren’t always trustworthy and reliable.
Take chatbots, for instance. Chatbots are software programs designed to help users by engaging those users in a simple form of conversation. They are the next step in customer service evolution. Users encounter them on a call to an 800 number, or when trying to use a website or an app. But often, the answers they give us after we ask a question aren’t useful answers. That’s because the data isn’t being delivered in the context the user is concerned about. Or maybe, the user gets an answer that’s mostly right but is still misleading.
FAIR data will help because it describes multiple contexts in a way that’s machine-readable. The more information is connected and made more precise and relevant with the help of context, the more machines can retrieve reliable answers.
How FAIR Data Will Help
If we create contexts for data so it becomes FAIR data and is connected to other FAIR data, chatbots will give us more reliable answers. And that’s just one way FAIR data can help.
When connected and machine-readable, FAIR data is the kind of data machines will need to make sense of the world. This way, machines and humans together can help users in lots of ways we haven’t even begun to explore yet.
