Speaker 1: When Hurricane Katrina was about to hit the coast of the United States, a large retailer did a study to prepare themselves by asking what products they might sell out of and what they should stock up on. A room full of intelligent and experienced executives thought through what those products might be and came up with reasonable answers such as flashlights, batteries, water, canned food, sandbags and more. But when they ran the data and analytics, the number one product turned out to be Budweiser beer. This is the power of data to illuminate insight, to take us beyond intuition and help us make data empowered decisions, and it has relevance for almost everything we do. More and more of our actions and interactions with the world are becoming mediated by data. This alters how we interact and the choices we make. Studying and seeing data can completely change the ranking of a set of options available to us and hence how we allocate our resources, both as individuals and collectively. Almost everything can be tested, measured and improved and this is truly bringing about a quiet but fundamental cultural transformation in how we make decisions. Datification brings about a more objective form of decision making, what is called data driven decision making. For example, when it comes to choosing a movie, we used to go to the store and pick up the movie, browse through all the titles, read the description and decide which one we wanted to see. Now we're confronted with algorithms that make recommendations based upon data from the last films that we have seen, as well as who our friends are, what films they have seen and liked and the aggregation of feedback from thousands and millions of other users. Madeleine McIntosh from a book publishing house talked about the culture of publishing changing with the arrival of Amazon's data driven approach. The traditional culture of publishing was what she called a culture of lunches, a culture of conversations where people had hunches and ideas about books and then discussed them. Amazon then brought a data driven, numbers and math driven approach to this decision and was able to basically figure out much better what was working and what wasn't working, with the result being that they've essentially taken over the market. This transformation is happening in many areas of our economy. More traditional companies are being displaced by companies that have embraced this new technology and the cultural paradigm of data. Think about wine tasting, which you might think of as a quintessentially human skill. There are human experts who look at and smell the wine to tell you what it tastes like and if it's of good quality. This is a highly refined skill and sensory ability. But it's also true that wine is at the end of the day just a certain molecular composition and you can analyse that with numbers. The wine analytics company, Analytics, have been able to figure out that you can predict how an expert will rate it before they've even tasted the wine with remarkable accuracy. And this applies to more and more spheres of life. Wall Street is no longer full of people on seats making trades based on intuition and hope, but up to 70% of those decisions are now made by algorithms acting on data. Likewise, decisions on healthcare diagnostics are increasingly made by analytical systems. Sports decisions are based on big data extracted from cameras around the court or pitch and sensors in the shirts of players. The implicit premise of big data is that decisions can be made based fully upon data and computerised models, shifting the locus of decision making from people and institutions to data and former models. Bill Schmarzo from EMC describes well how decisions are currently made based upon management's gut feeling. One of the most critical aspects of big data is its impact on how decisions are made and who gets to make them. When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well placed people make decisions, which they do on the basis of experience they've built up and patterns and relationships they've observed and internalised. Intuition is the label given to this type of inference and decision making. People state their opinions about what the future holds, what's going to happen, how well something will work and so on, and then plan accordingly. The term HIPPO is an acronym now used to describe this type of corporate decision making process, where the highest paid person in the room gets to make the final call. Much of our approach to decision making has been a function of simply not having data and not knowing. In the past, we have had to make decisions about complex environments and complex systems without being able to see or know what they were really like, just based upon some intuition. But big data analytics offers this new telescope with which to actually see these systems, and the difference between having a hunch and actually seeing the data can be huge in terms of the decisions that get made. Every minute, the world loses an area of forest the size of 48 football fields, and deforestation in the Amazon basin accounts for the largest share of this, contributing to reduced biodiversity, habitat loss, climate change and other ecologically devastating effects. But better data about the location of deforestation and human encroachment on forests could help governments and local stakeholders respond more quickly and effectively. A project called PLANET is currently developing the world's largest constellation of Earth imaging satellites. They will soon be collecting daily images of the entire land surface of the Earth at three to five meter resolutions. While considerable research has been devoted to tracking changes in forests, it typically depends on coarse resolution images. Furthermore, these existing methods generally can't differentiate between human causes of forest loss and natural causes. This project, PLANET, is challenging the analytics community to develop machine learning models for labelling satellite image clips with atmospheric conditions and various classes of land cover and land use types. Resulting algorithms will help to better understand where, how and why deforestation happens all over the world. A much clearer image of this complex system would enable action-orientated decisions to take place, thus switching the dynamic from hunches, guesses and intuition to that of a data-driven decision-making approach. Data holds out huge potential to revolutionise how we make decisions, to shake up existing inert patterns of thought and action-taking, to overthrow unquestioned bias, to question established assumptions. But data also has its limitations, and this is what we'll look at in the next module, as we go further into the conceptual foundations of the big data paradigm, talking about what's come to be called dataism, the belief in data.