I have been discussing on the board of a company that I represent as a Non-Executive Director at a great level of detail the subject of Meaningful Data and the value of Meaningful Data vs Data and Information, in making informed decisions across the business. As the subject seems to becoming a business imperative, I thought a great opportunity for my next blog discussion.
It is very clear in today’s world that most organisations recognise that being a successful, data-driven company requires skilled developers and analysts. Fewer grasp how to use data to tell a meaningful story that resonates both intellectually and emotionally with an audience.
Joseph Rudyard Kipling was an English journalist, short-story writer, poet, and novelist who once wrote, “If history were taught in the form of stories, it would never be forgotten.” The same applies to data. Companies must understand that data will be remembered only if presented in the right way. And often a slide, spreadsheet or graph is not the right way; a story is.
Boards of Executives and managers are being bombarded with dashboards brimming with analytics. They struggle with data-driven decision making because they do not know the story behind the data.
Sometimes the right data is big. Sometimes the right data is small. But for innovators the key is figuring out what those critical pieces of data are that drive competitive position. Those will be the pieces of right data that you should seek out fervently. To get there, I would strongly suggest asking the following three questions as a process for drilling down to the right data.
- What decisions drive waste in your business?
- Which decisions could you automate to reduce waste?
- What data would you need to do so?
Information systems might differ wildly in form and application but essentially they serve a common purpose which is to convert data into meaningful information which in turn enables the organisation to build knowledge:
Data is unprocessed facts and figures without any added interpretation or analysis. “The price of crude oil is £50 per barrel.”
Information is data that has been interpreted so that it has meaning for the user. “The price of crude oil has risen from £30 to £50 per barrel” gives meaning to the data and so is said to be information to someone who tracks oil prices.
Knowledge is a combination of information, experience and insight that may benefit the individual or the organisation. “When crude oil prices go up by £10 per barrel, it’s likely that petrol prices will rise by 2p per litre” is knowledge.
The boundaries between the three terms are not always clear. What is data to one person is information to someone else. To a commodities trader for example, slight changes in the sea of numbers on a computer screen convey messages which act as information that enables a trader to take action. To almost anyone else they would look like raw data. What matters are the concepts and your ability to use data to build meaningful information and knowledge.
The ability to gather meaningful data is as important as the insights the data can generate. Those insights, the end result of any data collection, is what people see and judge.
The hard truth here is that bad data leads to bad decisions. Thus, it is important to take the time necessary to build a proper data collection process.
Data is meaningful if we have some way to act upon it. Otherwise, we are mere spectators. This is one of the most problematic aspects of the current fetish of data visualisation, which appears to treat data as an unquestionable justification for itself, rather than as a proxy for things that we actually want to understand or probe.
You generally can’t put yourself into a visualisation, tell it a little about yourself, and nudge it towards a better understanding of the questions you want to ask of it (like you would any person you want to find out more about).
If we are satisfied with mere data, datasets or data visualisations as the end goal – rather than all the contextual complexity behind who, why and how it was collected, and what was excluded from the presentation – then we are contenting ourselves with just one dimension, not four.
Data doesn’t need to be numeric, digital or electronic; it’s anything that helps you to make an assessment, and in many senses if it’s non-digital it can integrate a whole host of other phenomena, providing a much deeper, if more complex, proxy.
A wonderful example of this was an air quality experiment led by professor Barbara Maher of Lancaster University. In the test, four houses had 30 potted birch trees placed directly outside their doors; and four households, acting as control subjects, did not have any trees placed outside.
A major innovation in the experiment was that levels of particulate pollution were evaluated by collecting dust particles that settled on television screens, which had been wiped clean at the beginning of the experiment, and comparing the two sets of households to see which had amassed more particulate. The experiment showed – viscerally, visibly and physically – that planting trees reduced particulate. It didn’t require a digital sensor sitting on a mantelpiece.
One of the best ways to make data more meaningful is to make it yourself. Measure something – your body, your home, your neighbourhood – and it helps you to not only understand something about it, but more importantly it helps you to figure out the questions you want to ask and the hypotheses you want to assess. Measuring something yourself (the way your body temperature fluctuates; the cycles of noise in your neighbourhood) means you can better decide why and what you might do to affect or act upon it.
A city hackathon bringing dozens, if not hundreds, of software developers together for a short space of time to work for free on government-approved historical datasets is all well and good, but you have to ask how transformative it actually is to work on something without questioning why and how the data was collected, or which data has been excluded.
When you join with others to measure something, you make meaning by having conversations about the data you are collecting. Sensemaking in this situation becomes a collective activity – you don’t even need to be using the same measuring equipment, you just need to be able to talk about what you’re doing with each other. “I’m measuring air quality,” you say. “Well I’m recording atmospheric humidity levels,” says your neighbour. Have a discussion and you’ll start to build up an intuition of how they correlate, or even better, look at ways of affecting them together, ideally for the better.
The most important aspect of making data more meaningful is to experience it, somehow, in situ. Even if you were not part of the process of collecting a dataset, to be near to where and when it was captured you are far more likely to be able to integrate all the unspoken, ambient, implicit, informal and unrecorded metadata that datasets and visualisations strip out with their numeric authority.
To stand in a space, a neighbourhood or a city and experience its windy mess while simultaneously being able to interrogate, prod and affect a dataset provides you with the kind of multivalence that is crucial to constructing any useful meaning. You are far more likely to be held accountable, and to hold others accountable, for making use of the data in any decision making process.
Most captivating storytellers grasp the importance of understanding the audience. They might tell the same story to a child and adult, but the intonation and delivery will be different. In the same way, a data-based story should be adjusted based on the listener. For example, when speaking to an executive, statistics are likely key to the conversation, but a business intelligence manager would likely find methods and techniques just as important to the story.
In a Harvard Business Review article titled “How to Tell a Story with Data,” Dell Executive Strategist Jim Stikeleather segments listeners into five main audiences: novice, generalist, management, expert and executive. The novice is new to a subject but doesn’t want oversimplification.
The generalist is aware of a topic but looks for an overview and the story’s major themes. The management seeks in-depth, actionable understanding of a story’s intricacies and interrelationships with access to detail. The expert wants more exploration and discovery and less storytelling. And the executive needs to know the significance and conclusions of weighted probabilities.
Discerning an audience’s level of understanding and objectives will help the storyteller to create a narrative. But how should we tell the story? The answer to this question is crucial because it will define whether the story will be heard or not.
As Stewart Butterfield once said:
“Hard numbers tell an important story; user stats and sales numbers will always be key metrics. But every day, your users are sharing a huge amount of qualitative data, too – and a lot of companies either don’t know how or forget to act on it.”