Of destinations and feeds

In An Ambient Future, I had written about how Google was potentially poised for something really interesting because technically, it had things in place to harness mobile, social and sensor data and overlay it with machine learning and AI. An early version of how this data could be surfaced contextually and be shown in an interface would be Google Now, as Christian Hernandez had pointed out. And that was why I was quite surprised and dismayed when I read that most of the team that had been working on Google Now had left!

The larger context though is about content discovery and two possible approaches to it – destination (platform?) and feed. I remember reading Neil Perkin’s post on the subject last year (it’s a fascinating rabbit hole of related reads, you’ve been warned!) and it has had me thinking ever since, especially in recent times, with apps increasingly replacing the traditional website as a destination. So far, the feed largely served as a distribution method to destination, but I believe it is no longer that simple on the web, let alone mobile.  More

Brand Interfaces

A couple of months ago, I had written a post on the inevitable ambient future of what we now call the internet, and the role of AI in it. The post was mostly on the rapidly changing nature of interfaces. The ones we actively interact with – mobile, VR/AR, gesture/haptic based tech – and the relatively more ambient ones like a certain kind of wearables and IoT. In that post, the argument was that Google was best placed to tie together data from mobile, social, sensor, location etc and give it context with the help of AI. (Hello, Alphabet!) As this Wired post states, Google is not a search company, it is a machine learning company. Do read about Google Brain while you’re at it! It has a role in several Google products we use, and shows the potential of what is possible when machine learning really works on content surfacing.

But all that is only context setting. Something that has been occupying a lot of my mind space these days is the impact of these continuing developments on brand communication and distribution. For years, the limitations of traditional media have forced brands to communicate to lumpy masses of ‘target audiences’. As the internet transitions into a much more ambient an ubiquitous form, all of brand marketing will be digital either overtly or under the hood. But even digital’s early versions have been on the same path, with incremental changes based on intent/interest. That, I think, is about to change fast. This superb article on the same subject puts it really well – we need not simply digital strategies but strategies for a digital world. It also explores the technological and platform advances that will allow frictionless experiences for consumers and what it means for brands.  More

In an ambient future…

Digi-Capital claims that by 2020, Virtual and Augmented Reality combined would have hit $150 bn, eclipsing mobile. What is interesting is that a recent Juniper report predicts an $80 bn market for wearables by 2020. (via) If I read that together, by 2020 we would have witnessed three interface cycles – mobile, wearables and AR+VR. The shelf life of interfaces is shrinking, much like other business cycles. In fact, in Trendwatching’s No Interface trend brief, you can get a preview of this. I’d think that by 2020 web access would be much better than what we have now, and with other technology like IoT advancing sufficiently, we would be poised for ambient interfaces to consume and create what we do on the web and mobile now.

It is widely believed that Google is only a challenger in the  mobile and wearable domains – to Facebook and Apple, despite Android. With Facebook’s Oculus move and Glass’ demise, it would seem that the interface that follows the two above would also see a fight. In an insightful post, Ben Evans asks “What does Google need on mobile?” He notes that all of Google’s play is about reach – to collect and surface data. Mobile, and specifically apps, challenge this and create a world of perfect complexity. He ends with saying that Google needs to win at search,  whatever that means and wherever and however far from PageRank that leads you. Christian Hernandez goes further in his post ‘Into the Age of Context‘. He points out that the glue that connects mobile, social and sensor trends is data, but to take it to the next level, it needs machine learning and AI. He sees Google Now as the perfect example of The Age of Context. More

Algorithms of wealth

Some strange quirk in the cosmic order of things led to Landmark shipping me Piketty’s ‘Capital in the Twenty-First century’ instead of Rana Dasgupta’s Capital! I kept the book (yet to read it though) because economic disparity has been an interest area for a while now, I had touched upon it in the context of AI and job loss in Artificial Humanity. Reading The Black Swan has only accelerated this interest.

Taleb divides the world  into Mediocristan and Extremistan to point out the extent of predictability in the context. Mediocristan can safely use Gaussian distribution, (bell curve)  but in Extemistan, that’s dangerous. From what I understand, given that there’s no real limit upper limit of scale, individual wealth will increasingly behave in a more Extremistan way. To quote his own example, “You randomly sample two persons from the US population. You are told that they earn jointly a million dollars per annum. What is the most likely breakdown of their income? In Mediocristan, the most likely combination is half a million each. In Extremistan, it would be $50,000 and $950,000.” He states that almost all social matters are from Extremistan. More

The IoT battlefield

The last time I wrote about the Internet of Things, I hoped for an application layer that could sense and collect data and convert it into use cases. In fact, the title of the post was Interweb of Things, the nuanced difference between them being connection (IoT) and interoperability. (WoT) (read) In the few months since that post, there has been quite some activity in the space. I saw a very useful classification a few days ago that illustrated both the ‘things’ as well as the infrastructure and showed the possibilities of interoperability. (via)



An Interweb of Things

Since the time I wrote An Internet of Things narrative, its trajectory and pace has seen tremendous acceleration, to an extent where TC has claimed that it has reached escape velocity. Indeed, there is a whole lot of activity happening that would back this claim – startups, larger companies getting interested in the space, geographic expansion and so on. In fact, the article has what seems like a comprehensive chart on applications, platforms etc.

In my earlier post (linked above) I had pointed to the distinction between the Internet of Things and the Web of Things. What was then a nuance seems much more wider now and is even more relevant. Another article on TC, titled The Problem with the Internet of Things is actually about this. One of the products that has fascinated me for a while is Mother, from To me, it aims to solve this problem, and the last two points in their ‘Creating the Internet of Life’ document is proof of it. (Like wearables in 2014, I plan to get a consumer IoT experience in 2015, and this is most likely going to be my preference) Another simplistic but potentially very useful product I have seen is Flic. The last example is Signul, which uses a beacon system to automate things used in daily lives. (both on Indiegogo)


The Change Imperative

Ever since I first wrote about institutional realignment, I have been more conscious of it and its implications on our lives. To a certain extent, even paranoid, because of the pace of change. Ray Kurzweil is hard at work to make himself immortal, and believes we should get really close by the 2030s. He has been right before on many things of this nature. Moore’s law, digitisation and everything related are also getting us really close to the singularity. I am reasonably convinced that I will see both in my lifetime. If you live to be 200 and have robots smarter than you around, what does that do to education, money, marriage, work and pretty much everything that constitutes life? On the flip side, natural resources are running out, and I can see the complications already. It’s not a good sight, or experience!

I am finding it impossible to wrap my head around what all of  this would mean to our concept of life. In the meanwhile, I do know that everything is changing at breakneck speed, and in order to survive, we need to be cognizant of things that can impact our lives – as individuals, and as organisations.  I have deliberately avoided the word ‘disruption’ because it gives me a sense of suddenness and it is a furiously debated topic these days. Rather, to quote John Green (said in another context) I think we’re in the first state of “Slowly, and then all at once”.  This, is my take on ‘Change’.

(Thanks Nikhil for helping on a couple of alphabets and Amit for Unsplash, the source of many images used)


An Internet of Things narrative

Towards the end of last year, I’d written a post on the ‘social product‘. Its premise was that given social’s conversion to media, the opportunity for fulfilling social’s initial promise would fall on ‘product’ – using data, network effects, and relationships to connect consumers along a shared purpose. In the last few weeks, I have seen rapid acceleration happening on this front. I can see at least two narratives working in tandem, and I’m sure that at some point they will begin to augment each other really well. In this excellent post on technologies that are shaping the future of design, sensors occupy the top slot, and they are at the basis of both the narratives – one on humans, and one on things. The official classification, roughly, translates into Wearables and Internet Of Things respectively for the scope of discussions here.

This post is about the second. So, what is the Internet of things? The wiki definition is simple, but effective –  “The Internet of Things (IoT) refers to uniquely identifiable objects and their virtual representations in an Internet-like structure.” The best primer I have come across would be this infographic, which has everything from a quick technology explanation, applications and challenges to market size, statistics, and interesting use cases. For a really solid perspective, look no further than this deck titled ‘The Internet of Everything‘.

How does it affect us? For now, it is about convenience. If you’re familiar with Android launchers, imagine an IoT version – it’s almost there, using iBeacon! There’s more – Piper, which works as an IFTTT for your home, the smart fridge that can order groceries from the online store, the smart TV that can learn preferences and help us discover content, the washing machine that can help order detergent, the egg tray that will let you know about the number of eggs it holds and their ‘state’, the automated coffee machine, Philips’ connected retail lighting system, Pixie Scientific’s Smart Diapers, the GE a/c that learns your preferences, the smart bulb that doubles up as a bluetooth speaker, (!) and so on. Some of the products are really useful and solve a need, while some others are more fads and probably not adding the value that reflects the potential of IoT. But that’s just the learning curve in progress, as the market starts separating needs and wants.

All of this also means that consumption patterns will begin to change, as more purchases become automated, and more importantly data-driven. In my post on the driving forces of 2014, I had brought up technology as the biggest disruption that marketing has seen. This is most definitely one of the manifestations.


What can brands do? For starters, get interested. Think about the tangible benefits that can be offered to consumers. What are the kind of data patterns that devices (or products) can surface to help the consumer make better consumption decisions? What kind of contexts can be relevant? Instead of force feeding advertising on traditional channels and fracking social platforms, can communication to consumers be made seamless using data, contexts and easy processes? While ‘device’ brands might have an initial advantage, ‘product’ brands need not be left behind at all. As the washing machine post (linked earlier) suggests, a Unilever or P&G might subsidise a machine, because it’s pre-sold with 500 washes worth of their detergent. It could even be real time, with SDK, API systems telling a partner brand to push a contextually relevant communication to a consumer. As things start storing and communicating data, privacy will be a major factor that decides whom consumers will share what with. Unlike media, trust cannot be ‘fracked’, it needs to be earned over a time frame.

Where does it go from here? A common language/protocol/registry is a good start, as is a white label platform – both are trying to connect an assortment of devices and gadgets. While there is value in data at an individual level (more on that in the next narrative) one of the critical factors in the success of this phenomenon is the devices talking to each other – humans acting as middle men to pass on data may not be a smart way ahead!  Digital Tonto has an excellent nuanced perspective that differentiates IoT from the web of things. (WoT sounds cooler!) The difference is in connection and interoperability.


Equally important is this phenomenon’s ability to solve human needs. (Internet of Caring Things)

Collaborative consumption is fast becoming a consumer reality. As always, brands (generalising) are bound to be a few years behind, but the hope is that the web of things will force them to start collaborative creation and distribution and more importantly, focus on consumer needs.

until next time, #WoTever

P.S. In a corruption of Scott Adams’  idea, I think #WoT is paving the way for robot domination. 😉

P.P.S. If the subject interests you, check out my Internet of Things Pinterest board.

The questions in Big Data

In my last post that touched upon Big Data, I had mentioned how the seeming intent of Big Data is to synthesise actionable insights from processed and unprocessed information at touch points related or unrelated to the enterprise, and then use it to target consumers better. While this is probably true for the short-medium term, I read a wonderful perspective at GigaOm by Beau Cronin on its true potential – the path to building the equivalent of global-scale nervous systems. As I tweeted after I read it, it reminded me of something I’d written a couple of years back before I’d heard of #BigData – if we could actually use data to go beyond that to answer life’s profound questions. Before we go into the subject, here’s a nice video by OgilvyOne titled “Big Data for smarter customer experiences” (via) though it’s skewed more towards the experience rather than the data.

Beau Cronin has mentioned several possibilities this would give rise to, and the post made me think if something like the hive mind concept would mesh into it as well – a sort of hybrid neural network. He has also pointed out the hurdles we would face while we get there – gathering, processing and conversion into actionable insights, and how phenomena such as priming,expectations, and framing matter so much in how we perceive our physical and social environments. In essence, a fascinating read.

I was particularly intrigued by framing, and began thinking about it in the context of the unstructured data – tweets, posts, mails, videos – that is a major component of Big Data. The fundamental question being – is it unstructured because we’re framing it ‘wrong’? Based on the enterprise’ intent and not the users’? Ironically, I couldn’t frame the questions right until I met the ever-brilliant S, who has always maintained that the answer is easy to find once the question has been framed right. He has developed (Tulpa -to build or construct in Tibetan – is the concept he enlightened me on while we were discussing semantics) something that at a rough level mashes the MECE principle with Frame Semantics and the entity-relationship model. There’s IPR involved, so no more beans shall be spilled, but as always, I learned much from the conversation.

In essence, structure can definitely be derived from what we currently call unstructured data, provided we frame the queries right. I can intuitively begin to understand that in the era of data abundance, the only way we can make sense of all of it is by focusing on an intent that is derived from a common purpose, so that the sources of data (users) will be more open to help solve the challenges of data collection. The processing and inferences that follow would yield the best results when the right questions are asked. I have a feeling that the questions asked by a business in an earlier era might not cut it.

until next time, role models

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