Towards the Internet of Underthings

#WearableTech #InternetOfThings Once upon a time, the wires in an undergarment merely provided structural support. Now, people may have all sorts of wires and wireless devices hidden under their clothing. Here are some interesting examples.

• The Foxleaf Bra delivers cancer-fighting drugs through the wearer's skin.
• An aunt’s death led Kemisola Bolarinwa to develop a wearable device that can pick up Nigeria’s most common cancer much earlier.
  
• The @tweetingbra reminds women to examine themselves. (?)
• The Lumo Lift helps improve posture through app-enabled coaching.
• Various manufacturers (including Clothing+, OMsignal and SmartLife) produce health vests and sportswear packed with monitors to track your heart rate, breathing rate and the amount of calories you've burnt.

We are now encouraged to account for everything we do: footsteps, heartbeats, posture. Until recently this kind of micro-attention to oneself was regarded as slightly obsessional, nowadays it seems to be perfectly normal. And of course these data are collected, and sent to the cloud, and turned into someone else's big data. (Good luck with those privacy settings, by the way.)

If a device is classed as a medical device, it will be subject to various forms of accreditation and regulation. For this reason, many device makers will be careful to avoid any specific medical claims, but devices that offer some health advice are considered a borderline area.

Another borderline area is hi-tech underpants that protect men from the evil rays allegedly produced by all those wireless devices. Especially the radiation from mobile phones. (Including the Bluetooth that links your underwear to your smartphone.) One brand of underpants that claims to use a mesh of pure silver to create a Faraday cage around the genitals has been banned by the UK Advertising Standards Authority from making any medical claims.

Or maybe you could just switch the whole lot off.

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The Wearable Medical Device in Your Future…Is Now! (Marketing Research Association, 28 April 2015)

Jennie Agg, The hi-tech bra that helps you beat breast cancer - and other clothes that can treat or prevent illness (Daily Mail, 10 March 2015)

Valentine Benjamin, Can a bra detect breast cancer? This Nigerian entrepreneur thinks so (Guardian, 9 Aug 2023)

Sarah Blackman, Student designs cancer-fighting bra (Lingerie Insight, 10 Feb 2015)

Britta O'Boyle, SmartLife clothing claims to make sure you never miss a beat (Pocket-Lint, 12 March 2015) 

Rob Crilly, Hi-tech pants "protect sperm from phone waves" (Telegraph 22 October 2014)

Julie Papanek, How Wearable Startups Can Win Big In The Medical Industry (TechCrunch, 19 Feb 2015)

Hannah Jane Parkinson, Lumo Lift review: posture-tracking gadget is a straight shooter (Guardian, 14 November 2014) 

Helen Popkin, Tweeting bra exposed: Genuine support or publicity lift? (NBC News 25 October 2013)

Meera Senthilingam, How a high-tech bra could be your next doctor (CNN, 11 May 2015)

Brendan Seibel, High-Tech Underwear for Adventurous Geeks (21 April 2010)

Mark Sweney, Hi-tech underwear advert banned (Guardian 13 August 2014)

Dan Sung, World Cancer Day - The Real Wonderbra (Wearable, 14 Feb 2015)  


Related Posts Have you got big data in your underwear (December 2014)

Have you got big data in your underwear?

Apparently, women's breasts aren't all the same. (Who knew?) True+Co. uses an algorithm based on customer feedback to recommend comfortable and flattering bras for its customers. A visitor to the website completes a questionnaire, and the website recommends some suitable bras. If the customer orders the bra, she then completes another questionnaire providing feedback on comfort and appearance. To date, over a million women have completed the questionnaire, providing 15 million data points.

@tetradian reckoned this is a great example of #bizmodel #bigdata for mass-uniqueness. But I didn't see this example the same way: I don't see anything here that turns Mass Customization into what Tom likes to call Mass Uniqueness.

Tom's favourite example of "mass uniqueness" is Picasso. I bet the algorithm couldn't find a bra for the breasts of Picasso's Demoiselles (NSFW).

Breasts of Picasso’s Demoiselle (NSFW)…

A single questionnaire, even from a million women, doesn't get into the big data league. Maybe it would when they start analysing pictures and videos of customer breasts, rather than relying on a simple questionnaire.

Or if the company were to fit sensors to its underwear, monitoring stretch during a range of activities, collecting millions of data points every minute via the Internet of Things.

Do you think I'm joking? Microsoft is working on a Smart Bra, which will monitor the mood of the wearer and detect stress. The Daily Mail suggests that this will help women to lose weight.

To stop women reaching for the cookie jar when things hit a low, Microsoft's new prototype bra predicts when the wearer is likely to comfort eat and warns against it. The software company's high-tech undergarment features sensors in the cup pockets and side panels that detect changes in heart rate, skin temperature and stress levels - apparent precursors to overeating. All of the data is then streamed via Bluetooth to a smartphone app providing real-time mood-triggered eating alerts.

Now that's what I call big data. Scary, huh?

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Valentine Benjamin, Can a bra detect breast cancer? This Nigerian entrepreneur thinks so (Guardian, 9 Aug 2023)

Jillian Goodman, Cup Size Isn’t Everything (Fast Company, October 2014)

Tom Graves, On Mass Uniqueness (23 May 2014)

April Joyner, Big Data: Coming Soon to Your Bra? (Fast Forward, 6 September 2014)

Hayley Krischer, The underappreciated artistry of the professional bra fitter (Guardian 4 June 2015)

Sadie Whitelocks, Supporting your body in more ways than one! The high-tech bra designed to stop women from comfort eating (Daily Mail, 28 November 2013)

Microsoft working on a smart bra to measure mood (BBC News, 3 December 2013)


See also Towards the Internet of Underthings (November 2015), Weaving in three dimensions (November 2015), On the Ethics of Technologically Mediated Nudge (May 2019), Trial by Ordeal (July 2019)

 
Links added 16 July 2020, 9 August 2023

Misunderstanding CRM and Big Data

Listening to @peter_w_ryan, @markhillary and Alexey Minkevich talking about #CRM and #BigData at the Institute of Directors, sponsored by IBA Group.

Peter cites an Ovum survey showing that Customer Satisfaction is now the number one concern of management, and argues for what Ovum calls Intelligent CRM. (CA announced something under this label back in October 2000. Other products are available.)

Mark says that CRM and Big Data are widely misunderstood, which is certainly true. My own opinion is the first misunderstanding is to think CRM is about managing THE relationship with THE customer, and I completely agree with Clayton Christensen (via Sloan) that this isn't enough. What we really need to focus on is the job the customers are trying to get done when they use your product or service.

Who is good at CRM? Peter cites an example of a professor of marketing who got a personalized service at a certain chain of hotels and has been talking about it ever since. (That's a pretty good coup for the hotel, if we take the story at face value.) Mark cites the video game market, where both the console manufacturers and the large game publishers are able to collect and analyse huge quantities of consumer behaviour.

Is CRM with Big Data merely a new way of taking advantage of customers? Although most people seem oblivious to the privacy and trust risks, the Wall Street Journal this week suggested that the consumer is becoming more savvy and less susceptible to exploitative loyalty schemes and promotions. This might help to explain why Tesco, once a master of the science of retail, now seems to be faltering.

If there is a sustainable business model based on CRM and Big Data, it must surely involve using these technologies to engage intelligently, authentically and ethically with customers, rather than imagining that these technologies can provide a quick fix for stupid organizations to take advantage of compliant customers.

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Related Blogs

Customer Orientation (May 2009)

The Science of Retail (April 2012)

Other Articles

Martha Mangelsdorf, Understanding your customer isn't enough (Sloan Review May 2009)

Shelly Banjo and Sara Germano, The End of the Impulse Shopper (Wall Street Journal 25 November 2014)

Intelligent CRM

AI-CRM "An intelligent CRM system with atuo-learning-tunning engine (sic), Aichain offers the most widely used open source business intelligence software in the world." Last updated March 2013

CA rolling out customer relationship management software (ComputerWorld October 2000)

IBA Group "maintains its focus on IT outsourcing that has become a strategy for many organizations seeking to improve their business processes"

Does Big Data Release Information Energy?

@michael_saylor of #MicroStrategy says that the Information Revolution is about harnessing "information energy" (The Mobile Wave, p 221). He describes information as a kind of fuel that generates "decision motion", driving people - and machines - to make a decision and take a course of action.

We already know that putting twice as much fuel into a vehicle doesn't make it twice as fast or twice as reliable. (Indeed, aeroplanes sometimes dump fuel to enable a safer landing.) But Saylor explains that information energy is not the same as physical energy.

1. Information energy doesn't follow conservation laws. Information can be created, consumed repeatedly, but never depleted or destroyed. (Unless it is lost or forgotten.)

2. Whereas physical energy is additive, the energy content of information is exponential.

3. The value of information depends on its use, and who is using it.


Let's look at his example.

"Total wheat production for a single year is valuable information; but total wheat production for ten years, combined ten years of rainfall data and ten years of fertilizer represents thirty times more data droplets, but probably contains one hundred times more information energy, because it shows trends and correlations that will drive a greater number of decisions." (pp 221-2).

In other words, thirty times as much data produces a hundred times more information. He doesn't say this extra information MAY drive more decisions, he says it WILL drive more decisions. In other words, the Information Revolution (and our increasing reliance on tools such as MicroStrategy's products) is a historical inevitability.

But is it really true that more data produces more information in this exponential way? In practice, there is a depreciation effect for historical or remote data, because an accumulation of small changes in working practices and technologies can make direct comparison misleading or impossible. So even if the farmer had twenty years' worth of data, or shared data from thousands of other farmers, it would not necessarily help her to make better decisions. Five years' data might be almost as good as ten years'.

Data is moving faster than ever before; we're also storing and processing more and more of it. But that doesn't mean we're just hoarding data, says Duncan Ross, director of data sciences at Teradata, "The pace of change of markets generally is so rapid that it doesn't make sense to retain information for more than a few years." (Charles Arthur, Tech giants may be huge, but nothing matches big data, Guardian 23 August 2013)

According to Saylor, the key to releasing information energy is mobile technology.

"The shocking thing about information is not how much there is, but how inaccessible it is despite the immense value it represents. ... Mobile computing puts information energy in hands of individuals during all waking hours and everywhere they are." (p 224)

What kind of decisions does Saylor imagine the farmer needs to make while sitting on a tractor or milking the cows? Obvious it would be useful to get an early warning of some emerging problem - for example an outbreak of disease further down the valley, or possible contamination of a batch of feed or fertilizer at the factory. But complex information needs interpretation, and most decisions require serious reflection, not instant reaction.

So it is not clear that providing instant access to large quantities of information is going to improve the quality of decision-making. And giving people twice as much information often leads to further procrastination. Surely the challenge for MicroStrategy is to help people deal with information overload, not just add to it?

Furthermore, as I said in my post Tablets and Hyperactivity (Feb 2013), being "always on" means that you never have long enough to think through something difficult before you are interrupted by another event. There is always another email to attend to, there is always something happening on Twitter or Facebook, and mobile devices encourage and reinforce this kind of hyperactivity.

Saylor concludes that "the acid of technology etches away the unnecessary" (p 237). If only this were true.

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Related posts

Service-Oriented Business Intelligence (September 2005)
On The True Nature of Knowledge (April 2014)


Updated 19 June 2014

OrgIntelligence - Are Better Tools the Answer?

Information Gathering

Managers spend up to two hours a day searching for information, and more than 50 percent of the information they obtain has no value to them.  In addition, only half of all managers believe their companies do a good job in governing information distribution or have established adequate processes to determine what data each part of an organization needs.

Source: Accenture January 2007

The average interaction worker spends an estimated 28 percent of the workweek managing e-mail and nearly 20 percent looking for internal information or tracking down colleagues who can help with specific tasks.

Source: McKinsey July 2012

See also HBR Blog August 2012

Knowledge Management

Traditional knowledge management has failed to address the problem of knowledge worker productivity. Tools that have been developed in KM focused on information management and do not support many of the key knowledge work processes. Knowledge workers have therefore adpated the email client to suit their needs. It has become the most successful knowledge work tool because it combines personal control with personalisability and integrates communication.

Source: Martin Roell, August 2004

Big Data

By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

Source: McKinsey May 2011

Big data can create big value. But like all the big-data predecessors – i.e., databases, data warehousing, data mining, data analytics and business intelligence – you need to know what you’re looking for, why you’re looking for it, what’s it worth to you, and how will you take advantage of it BEFORE you start. Otherwise, big data will just be a big waste of money.

Source: Silver Peak Systems, May 2012

Social Media

Social media is addictive. And if you’re not too careful, it can seriously eat into your productivity.

Source: MediaBistro All-Twitter October 2012

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Places are still available on my Organizational Intelligence Workshop (Feb 1st).

Business Signal Optimization

@DouglasMerrill of @ZestFinance (via @dhinchcliffe) tells us A Practical Approach to Reading Signals in Data (HBR Blogs November 2012)

If we think of data in tabular form, there are two obvious ways of increasing the size of the table - increasing the number of rows (greater volume of cases) or increasing the number of columns (greater volume of signals). This can either involve a greater variety of variables, as Merrill advocates, or a higher frequency of the same variable. I have talked in the past about the impact of increased granularity on Big Data.

As I understand it, Merrill's company sells Big Data solutions to the insurance underwriting industry, and its algorithms use thousands of different indicators to calculate risk.

The first question I always have in regard to such sophisticated decision-support technologies is what the feedback and monitoring loop looks like. If the decision is fully automated, then it would be good to have some mechanism to monitor the accuracy of the algorithm's predictions. Difficulty here is that there is usually no experimental control, so there is no direct way of learning whether the algorithm is being over-cautious. I call this one-sided learning,

Where the decision involves some human intervention, this gives us some further things to think about in evaluating the effectiveness of the decision-support. What are the statistical patterns of human intervention, and how do these relate to the way the decision-support software presents its recommendations?

Suppose that statistical analysis shows that the humans are basing their decisions on a much smaller subset of indicators, and that much of the data being presented to the human decision-makers is being systematically ignored. This could mean either that the software is too complicated (over-engineered) or that the humans are too simple-minded (under-trained). I have asked many CIOs whether they carry out this kind of statistical analysis, but most of them seem to think their responsibility for information management ends when they have provided the users with the requested information or service, therefore how this information or service is used is not their problem.

Meanwhile, the users may well have alternative sources of information, such as social media. One of the challenges Dion Hinchcliffe raises is how these richer sources of information can be integrated with the tabular data on which the traditional decision-support tools are based. I think this is what Dion means by "closing the clue gap".

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Dion Hinchcliffe, The enterprise opportunity of Big Data: Closing the "clue gap" (ZDNet August 2011)

Dion Hinchcliffe, How social data is changing the way we do business (ZDNet Nov 2012)

Douglas Merrill, A Practical Approach to Reading Signals in Data (HBR Blogs November 2012)

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Places are still available on my forthcoming workshops Business Awareness (Jan 28), Business Architecture (Jan 29-31), Organizational Intelligence (Feb 1).

Predictive Showrooming

Retailers are being urged to adopt big data and predictive analytics as a defensive weapon against showrooming - the growing phenomenon of customers looking at a product in a store and then ordering it online.

For example, TIBCO suggests "companies that are most effective at combating this type of new threat are those who have used data analysis and predictive analytics to predict what type of engagement will resonate most effectively with their existing and new customers". Meanwhile, @ericylai talks about marrying Big Data and Mobile, while @larryfreed talks about providing a unified, cross-channel experience.

However, @barneyjopson reports a new twist. Consumers can now use big data and predictive analytics themselves, by using a service from decide.com, which predicts future retail price changes (based on retailers' past behaviour) and encourages its members to use these predictions to optimize the timing of key purchases.

Update: In September 2013, decide.com was acquired by eBay and effectively closed down.
See Predictive Analytics for the Smart Consumer? (April 2014)

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Larry Freed, 5 Tips to Turn Showrooming Consumers into In-store Customers (Dec 2012)

Barney Jopson, Torn between loyalty and a bargain (Financial Times Dec 2012)

Eric Lai, How Brick-And-Mortar Retailers Can Beat 'Showrooming' And Amazon.com (ZDNet Oct 2012)

Bryson White, Did Black Friday showrooming overshadow Cyber Monday shopping online? (Adobe Digital Marketing Blog, Nov 2012)

Data Analysis Versus ‘Showrooming’ (TIBCO Spotfire Blog Nov 2012)

See also Showrooming and Multi-Sided Markets (Dec 2012)



Updated 23 April 2014