Filed Under B2B, Big Data, Blog, data analytics, data protection, eLearning, Industry Analysis, internet, mobile content, privacy, Publishing, Search, semantic web, social media, Uncategorized, Workflow | 2 Comments
During this period of enforced convalescence I have had to come to grips with the idea that my brain only works effectively when supported by the memory in all the devices around me. And that this state of dependency is now global. Without our membership of a globally networked society we would become slow and inefficient: with it we become dependent. And it is this dependency which seems to me the first stop on a mental route march which we need to make. I am far from the first to try to examine what Internet of Things (IoT) or, as some will say, Internet of Everything (IoE) will mean for social, industrial or commercial aspects of society. But I do not yet hear much examination of this phenomena in terms of the information industry, let alone the businesses we insist on still calling “publishing” or the “media”.
Let’s start at a point of common agreement. We are in the middle of a new industrial revolution. For evidence, check the websites of IEEE or IET: the latter have just published a splendid “Ones to Watch” report (http://www.theiet.org/policy/media/campaigns/ones-to-watch.cfm?utm_source=redirect&utm_medium=any&utm_campaign=onestowatch#.VHt6PmB0imw.mailto).
They see the vanguard industries in this fundamental change in the nature of commerce and society – think what happened in the UK between 1780 and 1830 – as driven by space exploration, robotics. 3D printing (I would rather they had spoken of additive manufacturing), new energy networks, food manufacturing and cyber-security. I buy all of those, but would add drug manufacture driven by individual DNA analysis.
Underlying this social and industrial revolution is the revolution that makes it all possible: the global connectivity of network – attached computing power, and it’s ability to exploit intelligence and data generated in the network. Only this week Professor Steven Hawking has pointed out the dangers of AI outside of man’s control. My feelings run the other way: it amazes me that while we have spoken of machine intelligence for 30 years we have so little to show for it. Only in the past few years has the ability to harvest data more effectively, and the ability to cross- search it without restructuring it, produced real results in terms of the impact of the data analytics advances (“Big Data”) really struck home. While we will always be seduced by thrills and tricks (Google Glasses?), we can now see machine intelligence built into most common workflows and at a variety of levels.
Here is a list, posted by Vincent Granville at DataScienceCentral, of impact areas for data analysis in the next ten years:
- Automated piloting (cars talking to cars) will reduce accidents and optimize your commute.
- Better fraud detection will catch IRS fraudsters and terrorists before they strike.
- Better encryption and monitoring systems will allow the creation of new, private currencies, avoiding the speculation that surrounds Bitcoin.
- Early detection of epidemics thanks to crowdsourcing.
- Detection of earthquakes, solar flares – including intensity forecasting.
- Customized, on-demand, online education with automated grading.
- Customized drugs based on patient’s history.
- Optimizing electricity trading and delivery (smart grids).
- Better search technology (on Google, Amazon, online product catalogues, search engines), and customized searches with increased relevancy.
- Better user profiling, more targeted marketing.
- Better detection of fake reviews for systems based on collaborative filtering (in short, superior collaborative filtering technology).
- Better detection of authorship/original posters (via attribution modeling) to make journalism and publishing better; detection of duplicates and plagiarism on the web; news feeds quality scoring; early detection of high quality, popular news and tweets posted by unknown authors (to help journalists).
- Better taxonomies to classify text (news, user reports, published articles, blog posts, yellow pages etc.).
- More customized (optimized) pricing and automated bidding on a number of exchanges.
- Detection of duplicate and fake accounts on social networks.
- Geo-marketing via cell phone (restaurants, retail).
Just look at how many of these impact the information industry marketplace. As our world of work changes so the very survival of information market players will depend upon how easily we are able to track change and react to it. But what part of this struggle to survive can we lay at the door of IoT/IoE? And can we picture an IoT world which is less trivial than sports wearables or more useful than a car that turns on the house lights at home when you are still a mile away? Well, obviously we can, but the unacceptable passengers riding on the back of IoT must then be taken into account. Yes, it does mean that we shall move from the age of privacy into the age of transparency – and we are halfway there already. And, yes, it does mean that employment is going to be very different. We will lose millions of jobs, and we are surprisingly far down this track as well. The UK public sector will lose a further Million jobs in the next five years, we learnt this week. Some of those will be outsourced but governments do not give up governing lightly – and many of those jobs will become automated systems roles in the outsourcing process . And it may well mean that, at last, we have to properly rethink what Capitalism means. After all, a zero marginal production cost society will ask questions about how the profit mechanism works.
For a good review of many of these questions see Sue Halpern’s review article in New York Review of Books (vol. LXI, Number 18, 3December 2014). Cisco famously predicts that all of this adds up to a 14.4 trillion dollar boost to the global economy between now and 2022. The 10 million sensors that measured our world in 2007 will number 100 trillion by 2030. In Rotterdam docks all containers will be engineered for auto drive by 2018. Uber, a precursor of the automated driving world, was as valuable as Time Warner this very month. For better or worse, this world is with us now. This is not 1780 in the original British experience, but 1820 and the railway boom is just beginning. And for information companies of every type there is a corresponding possibility of mega growth, as long as we read change accurately. Wherein lies a problem that I want to address later.keep looking »