How Data is Changing Our World
Data is changing our world and the way we live and work at an unprecedented rate. Depending on your viewpoint, we're either at the start of something incredibly exciting or we're entering a terrifying Big Brother era where our every move can be tracked – and even predicted. And both sides have a point. Business leaders and managers, however, have little time for data scepticism. Data is already revolutionizing the way companies operate and it will become increasingly critical to organizations in the coming years. Those companies that view data as a strategic asset are the ones that will survive and thrive. With the massive growth in big data and the Internet of Things, plus rapidly evolving methods for analysing data, the importance of data across every area of business will only grow. And the amount of data we're creating continues to increase rapidly. What does that even mean? If we learn how to read it properly, this huge amount of data can be put to use to do pretty much anything we want.
Data in itself isn't a new invention. Going back even before computers and databases, we still used data to track actions and simplify processes – think of paper transaction records and archive files. Computers, and particularly spreadsheets and databases, gave us a way to store and organize data on a large scale, in an easily accessible way. Suddenly, information was available at the click of a mouse. The difference today is that the most valuable data we have available is different now. Unstructured data is information captured in its ‘raw' form from the world around us. Pictures, videos, maps, text, speech recordings, social media posts – the list goes on. This data is potentially far more valuable than the business data we've worked with in the past because it can tell us a lot more about what we need to know. Imagine you run a shop and you want to know more about the people walking past – your potential customers. You could quite easily survey the situation and create data – counting the number of people that walk past, and perhaps ticking a box if they are male or female, old or young. This would be structured data, and for a simple analysis it would be fine.
What if you wanted to know more about these people? How are they acting? Are they stopping to look in shop windows or walking quickly as if in a hurry to be somewhere else? So you can set up a video camera. Your data would be unstructured – you wouldn't be able to easily upload it to a spreadsheet and analyse it. But that value would be there, locked inside the data, if you had a way to get it out. There's no doubt that the sheer amount of data we're creating is, well, big. But, if I'm honest, I've never been entirely comfortable with the term ‘big data'. It feels too simplistic to me, focusing on the volume of data rather than the incredible opportunities this data creates.
I wish there was a better term to describe this huge shift in our technology, culture and world. Your Internet service provider knows every website you've ever visited. Even in private browsing. In the United States, many cities make similar use of traffic cameras. Your phone also knows how fast you're driving. For now, that information is kept well guarded and is not routinely shared with, for example, police, who might be able to use it to prosecute you if it shows you have broken laws. But more and more insurance companies are starting to make use of smartphone data to deduce who is a safe driver and who's a riskier prospect. Your grocery store loyalty card tracks the brands you like and collects mountains of information on your purchasing habits and preferences. Retailers use this data to personalize your shopping experience, but it can also be used to predict what else you might want to buy in future. Today, things have moved on, and businesses like Amazon are moving towards being able to predict what we want to buy with strong enough certainty that they will ship orders towards us before we even place them.
Today, data analytics powers much more than ecommerce and targeted advertising, though. Its influence stretches to almost every aspect of modern life, from healthcare to space exploration, even to our politics. Elections are increasingly driven by analytics, and since Barack Obama's victory in 2012, candidates have spent increasingly large sums of money on predicting how we will vote, so they can focus campaign resources where they will make a difference – on undecided and swing voters.
Obama employed a team of more than 100 data analysts to run 66,000 computer simulations every day. Then everybody who had been identified was evaluated on their likelihood of voting for Obama, based on how well their data profile matched that of known supporters. Armed with their sophisticated demographic information, the team then launched targeted campaigns. These were aimed at increasing voter turnout and registration among sectors where the likelihood of backing their candidate was high, and influencing voter choice in sectors where the support metric indicated voters could go either way. This meant that targeted messages could be sent out by email, social media and browser display ads – depending on whether an individual needed to be convinced to register, vote or pick the correct candidate.
In the years since then, all parties and most candidates have enthusiastically launched their own analytics strategies. Donald Trump's successful campaign, led by Jared Kushner, grew daily donations from $8,000 per day to $80,000 per day with targeted social media advertising. To do this they used psychological mapping techniques developed by Michal Kosinski. By analysing 70 ‘likes', he was able to answer more questions about that person than a friend of theirs could.
