When I was at school, there were no personal computers, no internet, and no Google.
All information came from the teachers and the library where knowledge was written down and printed into substantial volumes.
Then there was the newspaper and the television news that reported current events.
Data was few and far between present only in the world of nerds. We didn’t study statistics or the scientific method, just content.
It all felt much simpler back then.
Today is the information age; you can find out almost anything with a few clicks. Streams of numbers are available on nearly anything that can be measured. The data banks are so big that there is even a concern about carbon emissions from the energy that drives the computers in the server farms. Globally it could be as much as 2% of the total carbon footprint (surprising if it is true).
And this is only the beginning.
Here are a few more things…
- By 2021 there will be close to 600 hyper-scale data centres double the number in 2016. It seems that we love generating and storing data and will go to great lengths to keep hold of it.
- Already there are 500 million tweets per day, 1.5 billion active Facebook users per day, and 95 million photos and videos shared on Instagram per day.
- Satellites that take images across a dozen wavelengths at 10 m resolution pass over your head every five days
- Online health records of all the people in a country, millions of them, provide a mine of information on the prevalence and geographic distribution of diseases and ailments.
- Billions of online purchasing records create information on the rational and not so rational behaviour of people in markets.
All this big data has many uses, some good, some bad, and no doubt a few that are ugly.
Retailers are expecting that all this big data will tell them enough about us to stop their slide out of business. They should be worried about new players who will use the data to tailor products and services to their customer’s needs, not those of the retailer.
In the food, ecology and diet challenges of sustainably FED, there is data everywhere. Numbers on farming systems, yields, water use, fertilizer application and what the future looks like for business resilience and reliability over time. The ecology of production systems and landscapes can be understood at scales of time and space ecologists of previous generations could only dream about — just a glance at the satellite image of NSW below is such a boon for those who can make an ecological read. Then there is diet and diets where consumption and health data can be correlated with what folk take home in their supermarket trolleys.
Again the data possibilities are endless.
This continental scale image of southeastern Australia shows the dry centre of the island continent, the huge drainage basin, the productive agricultural land and the hilly forested land on the coastal hinterlands. You can almost feel the soils as they respond to the macro-scale conditions.
What does all this data mean?
So what? We have access to loads of data.
…what we learn from professionals in the real world is that data is not necessarily rigour.Nassim Nicholas Taleb, “Skin in the Game: Hidden Asymmetries in Daily Life”
As Nassim Taleb reminds us, having information is not enough, not even if it is vast volumes of the stuff. What you do with it is what matters.
And what you do is use the data to understand and bound likelihoods.
This is the key difference between data and rigour that Nassim Taleb outlines.
Rigour is the quality of being extremely thorough and careful with information. This means knowing where the data comes from what makes it up and details on its quality. Know these attributes so that any analyses selected are sensible and will lead to inferences (predictions) with a quantified likelihood (probability).
This is jargon for ‘no bullshit’ or no ‘cherry-picking’ the good bits.
In other words, having vast data banks is not even the start for alone data is meaningless.
Data must be treated very carefully.
Synonyms for rigour might make it clearer…
What sustainably FED suggests…
Data is not rigour.
Data on its own is meaningless. Data needs context, attribute description, and interpretation of trends and pattern all completed with great care and diligence. This is the domain of statistics, mathematics and the philosophical aspects of the scientific method.
But anyone can learn the difference between data and rigour with a few simple rules of thumb.
Rigour is not dangerous to individual or collective civil liberties if done well. Indeed it is essential if all kinds of decision making are to be effective.
Rigour underpins the opportunity to both generate and test any number of solutions to global, local and even personal problems.
We think there is a massive opportunity for people who learn how to ‘do it well’ and on this site and in our eCourses we provide some of the tools and skills to help you learn rigour and generate opportunities.
Feel free to browse around for more details.