#THINK 2013: the ORION conference on Extreme Data (part two)

Continuing my review of the #THINK2013 ORION conference … John Weigelt, the National Technology Officer of Microsoft Canada, presented a large buffet of Big Data-related technology trends and innovations, from the Canadian mint chip to robotic farm tractors to networked transportation and travel to intelligent washing machines. Touching on just some:

• The Royal Canadian Mint Chip Challenge  invited “… software developers to create innovative digital payment applications using MintChip, a R&D phase technology available only to challenge participants. Developers and the public are also encouraged to share ideas for how a digital currency can be used.”  In other words, the Mint opened the doors to a large numbers of contributors to create the new mint chip. This approach is similar to crowdsourcing the development of smart grid apps.
• Agricultural technologies that involve robotic tractors and soil sensors that monitor and report data on crops and soil, such as soil or leaf temperature, amount of fertilizer in soil, crop yield and much more. Looking at these Google images of agricultural sensors will help you appreciate the wide range of sensing practices in modern agriculture. Using sensors to collect data automatically  is a significant part of Big Data activity.  I will return to it.
• Wireless communication between automobiles which could allow, for example, coordination of cruise control to allow everyone on a highway to travel at speed and get there sooner. And it’s not just about cars connected to each other. To quote Dr. Ulrich Eichhorn, “The car of the future will be networked – with the environment, with the traffic infrastructure and with the world of the internet.”

So what does the above mean for you and I? The potential benefits of “wired” driving seem clear: higher speed, increased safety, and reduced fuel consumption and pollution. But might it also lead to drivers paying less attention to driving because they don’t need to do as much? I wonder if this simply, ultimately leads to self-driving automobiles which are already being road-tested.

Data-producing sensors and agriculture are nothing but wonderful if using them leads to increased food production at lower costs. But the technology itself costs money and may be a barrier, especially in developing countries. On the plus side: as the technology is further developed and costs decrease it will become more widely available, maybe even to home gardeners.

It’s too early to tell about the Mint Chip, which hasn’t been released yet and faces competition from other types of electronic payment technologies already available. It will be interesting to see if the Canadian Mint’s crowdsourcing approach to input and development will result in an innovative, easy-to-use product.

#THINK 2013: the ORION conference on Extreme Data (part one)

Big Data Week 2013 took place April 22-28.  Fortunately, Toronto was one of 25 participating cities, and I had the opportunity to go to the ORION THINK 2013 conference. My report will have to be  brief because it was a day jam-packed with overwhelming data in its own right. I can only give you the highlights focusing on the you-and-me perspective of this blog.  Even so, I think that I’ll have to do several posts.

The theme of THINK 2013 was “Extreme Data”  —  Big Data as the mind-boggling, ever-increasing amounts of data available AND the great potential that comes from those huge amounts of data, IF you use the right technology and the right approach. Privacy is a good example. Big Data is ever-more pervasive and Extreme, so it is essential that your privacy is protected. As Ann Cavoukian, Information and Privacy Commissioner of  Ontario, persuasively argued,  this can not only be done without getting in the way of Big Data, but it can enhance it by making Big Data become Smart Data. Privacy is really about control of your personal data as determined by you. Using artificial intelligence technology, that control can be embedded into the data itself so it can’t be misused, then your privacy is protected automatically without you or anyone else having to act as a watchdog.

Smart Data would know more than just how to take care of itself. Smart Data could be really smart. For example, you may want only a few people to have access to your medical information, and a different group of people to have access to your employment history. With Smart Data you could tailor the levels of access to your data very specifically.

If that sounds far-fetched, know that researchers at the University of Toronto are already working on Smart Data in partnership with the Commissioner’s Office.

You’ve got your number — Quantified Self

This blog is focused on Big Data and what it will do for (to?) me and you … the impact of Big Data. But this post is particularly about Big Data and little you — your Quantified Self.

As they say on their website: “The Quantified Self is an international collaboration of users and makers of self-tracking tools.” And why would you want to track yourself? Because data about yourself is useful to you!

Much of the time, this is done to improve one’s health or physical fitness, to measure progress towards a personal goal, and to gain insight into one’s own psychology. While it is perfectly fine to keep track of one’s data using pencil and paper, there are many, many devices and apps out there to help one do this, like FitBit and the Nike Fuel Band.

I have attended Toronto Quantified Self meetings and used my own QS approach to help me lose weight. I kept track of food, calories and quantities (I weighed my food) on a spreadsheet along with my mood on a scale of 1 to 10. I lost weight, but the recorded data gave me other insights. For example, I learned that the more fruits and vegetables I ate, the better my mood was — about two days later.  In general, far from feeling that I had a finger-pointing nanny to chide me on the days when I ate a little less healthily,  it was rather like I had a partner working beside me. A partner who knew me very well, who gave me real feedback that I could use to understand and better myself.

Of course, it was a lot of work to manually keep track of even this small amount of data.  An Big Data approach to Quantified Self that effortlessly automates collection  of self-data and analyzes it for patterns and trends has a strong potential to improve your life. QS is moving in that direction;  there is already something like this. And maybe that intelligent refrigerator could help, too …

The Power of a Smart Grid

What is an electrical grid? It’s the infrastructure and transmission network of electrical power that spans large areas. It’s what went sideways during the big blackout of the eastern seaboard in summer 2003.

But 2003 was 10 years ago. Now grids are getting smarter and more secure. Utility companies can collect (lots of) data from the grid and use it to keep the grid running healthily, monitoring for problems and redistributing power as necessary. This is how utilities determine and predict heavy demand and boost prices accordingly.

These are large operations, covering vast geographical areas. But Big Data & smart grids can also benefit us more directly.

Here is a sample of what is going on here in Ontario, Canada:

“A working group led by the province and the MaRS Discovery District will explore an Ontario Green Button initiative that would use innovative smart grid technology to give families and businesses direct, timely access to their own energy data through a secure download from their utility’s website. A standard data format for Ontario’s utilities would also encourage app developers to create new tools to help Ontarians manage their energy use from their computers and smart devices.”

Judging by green button initiatives in the U.S. , individual developers will be encouraged to make their own apps.

There are many such customized apps in U.S., including apps that show savings on solar panels,  track how your habits affect your power consumption, reward desired behaviours with points or air miles, electrical appliance usage, pattern of use detection, calculation and forecasting of costs, and comparing your energy usage with others. It sounds like a wonderful example of crowdsourcing (itself a type of Big Data), but I am struck by the similarity of the apps. It looks as though it’s simply a developers’ free-for-all, with no coordination or evaluation of the products offered. Makes it difficult for people to know what to do …

Smart Appliances make Life Better (or at least Good)

Home appliances, like refrigerators, stoves, washers and dryers, are becoming smarter, better-informed and able to communicate information.

Take LG SmartThinQ appliances, for example.

Yes, the appliances are connected to a network so you can monitor and even control your appliances remotely from a smart phone, (but that’s almost expected now). But how about advice from your refrigerator on what to cook for supper, based on the data it has on what food is in the refrigerator and how old it is? How about your clothes dryer detecting a mechanical problem and putting in a request for repair, all by itself? And, if you are charged variable rates according to demand for electricity, how about the appliances “knowing” when it’s a good,cheap time for them to be used?

Smart appliances clearly have the potential to save money and make meal-planning and home-making somewhat more convenient, depending on the extent to which the smart features are actually used. But there is room to take smart appliances to another level. For example, if there was a way for the refrigerator to get data on food in the cupboard , it could suggest a wider range of meals drawn from all the food in the house, not just what’s in the refrigerator. It may require scanning of barcodes on cans or RFID, or (gasp!) manual entry of the data into the system. Would it be worth the trouble to tap into the refrigerator’s awesome powers of food data analysis? It would certainly be a step up, especially if wine cellar data could be included.

Another thought: if the appliances are on the same network, they should be able to communicate with each other. The LG stove also stores data about recipes. If the refrigerator and stove could talk to each other, they could share recipe information. The refrigerator could even tell the stove what the next likely meal will be, based not only on what food is available but also based on previous family preference data gathered by the refrigerator (or stove) — then, on spec, the stove could turn its oven on to the appropriate temperature, at an appropriate time (and turn itself off after a while if it’s not used in a reasonable amount of time).  Are there any other ideas out there?