Seven in 7 – Agile @ Scale, Maturing AI, The Ring of Success, Defending Democracy and More…

Agile at scale
As we head into the TCS UK Innovation Forum this week, I’m preparing myself to discuss big ideas and disruptive changes. With that in mind, this week’s Seven in 7 looks at scaling AI, a startup that was bought for a billion dollars, and hacking democracy. But also, as we’re committed to becoming agile as an organisation, where better to start, than at the great article in the new HBR about how to drive agile at scale!

(1) Doing Agile at Scale

This is a very timely look at Agile adoption at scale in the enterprise. It starts with enshrining Agile values in leadership roles, which requires a continuous approach to strategy. The next key thing is a clear taxonomy of initiatives which may be classified into 3 categories: customer experiences, business processes, and IT systems. The next step is sequencing the initiatives, with a clear understanding of timelines. It can take 5-7 years for real business impact, but there should be immediate customer value. Enterprise systems such as SAP can be delivered using agile as well. But it needs the organisation to create and move with a common rhythm. There are businesses working in agile who use hundreds of teams, solve large problems, and build sophisticated products. This can be made easier with modular products and operating architectures – which essentially mean the plug and play capabilities of individual components. It’s important to have shared priorities and financial empowerment of teams. Talent acquisition management needs to be reshaped to meet the new needs. And funding of projects and initiatives needs to be seen as ‘options for further discovery’. After all, at the heart of agile is the ability to proceed with a clear vision but without necessarily knowing all the steps to get there.

(2) Artificial Intelligence At Scale – for non-technology firms

It’s clear that Tech firms from Google, to Amazon and Twitter, have all been able to deploy AI at scale – in enabling recommendations, analysis and predictive behaviour. For non-tech firms too, the time may have come for delivering scaled AI. One of the key areas where AI seems to be ready to scale is around computer vision (image and video analysis) – relevant to insurance, security, or agribusinesses. The article below from the Economist also quotes TCS’s Gautam Shroff, who runs the NADIA chatbot project. A critical assertion the article makes is that implementing AI is not the same as installing a Microsoft program. This might be obvious, but what is less so, is that AI programs by design get better with age, and may be quite rudimentary at launch. Businesses looking to implement AI may need to play across multiple time horizons. And while the short-term opportunity and temptation is to focus on costs, there role of AI in creating new value is clearly much bigger.


(3) The Ring of Success:

What makes a new product successful? I met Jamie, the founder of Ring a couple of years ago in London and was struck by his directness and commitment. He even appears in his company’s ads. Ring.com was recently acquired by Amazon for $1bn. Here one of the backers of Ring talks about the factors which made Ring a success. In a nutshell, the list includes (1) the qualities of the founder, (2) execution focus and excellence, (3) continuous improvements, (4) having a single purpose, (5) pricing and customer value. (6) integration of hardware and software. (7) clarity about the role of the brand.


(4) Blockchain and ICO redux:

Do you know your Ethereum from your Eos or your MIATA from your Monero? This piece from the MIT Tech Review will sort you out. And for those of you who are still struggling to understand what exactly blockchain is, here’s a good primer. Of course, you could always go look at my earlier blog post on everything blockchain.

Links:


(5) X and Z – The Millennial Sandwich

X & Z: Or the millennial sandwich. All the talk in the digital revolves around millennial, but there is a generation on either side. The generation X – followed the baby boomers, and it turns out they have a better handle on traditional leadership values than millennial. This article talks about Generation X at work.

On the other side, there’s a generation after the millennials – the generation Z. They’re the ones who don’t have TV’s, don’t do facebook, and live their lives on mobile phones. This article talks about how Financial services are being shaped by Gen Z.


(6) Big tech validates Industry 4.0

This week, the large tech players disclosed significant earnings, beating expectations and seeing share prices surge. In a way it’s a validation of the industry 4.0 model – the abundance of capital, data, and infrastructure will enable businesses to create exponential value, despite the challenges of regulation, data stewardship issues and other problems.  Amazon still has headroom because when push comes to shove, Amazon Prime, which includes all you can consume music and movies can probably increase prices still more.


(7) Defending Democracy

The US elections meets the technology arms race – this article presents experiences from a hacking bootcamp., run for the teams who manage elections. While the details are interesting, there is a larger story here – more than influencing the elections either way, the greater harm this kind of election hacking wreaks is in its ability to shake people’s faith in democracy. As always, there’s no other answer than being prepared, but that’s easier said than done!

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Reading List: 7 for 7 – April 23: Palantir > Facebook, Generative Design, Alexa With Eyes, and More…

The 7 most interesting things I’ve read over the past 7 days.
 future doors
(image credit: Pixabay)

(1) The One Thing You Should Read about AI this week: 
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
  1. Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
  2. Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
  3. Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
  • The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
  • Image data is the highest value, after structured and time series data, and ahead of text.
  • Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms

(2) Data: Facebook is a misguided amateur compared to Palantir 
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.

(3) Design: Welcome to Generative 3D Design 
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.

(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.

(5) Asset-light business models 
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.

(6) Alexa Fashion – a glimpse of the future 
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela

(7) Battery Wars 
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.

When Technology Talks

Conversational Systems aka chatbots are starting to become mainstream – here’s why you should stay ahead of the game:

chatbots

The shape-shifting of the yin-yang between humans and technology is one of the hallmarks of digital technologies, but it is perhaps most pronounced and exploit in the area of Conversational Systems. But to truly appreciate conversational systems, we need to go back a few steps.

For the longest part of the evolution of information technology, the technology has been the unwieldy and intransigent partner requiring humans to contort in order to fit. Mainframe and ERP system were largely built to defend the single version of truth and cared little for the experience. Cue hours of training, anti-intuitive interfaces, clunky experiences, and flows designed by analysts, not designers. Most of us have lived through many ages of this type of IT will have experienced this first hand. If these systems were buildings they would be warehouses and fortresses, not homes or palaces. Too bad if you didn’t like it. What’s ‘like’ got to do with it! (As Tina Turner might have sung!)

Digital technology started to change this model. Because of its roots in consumer technology rather than enterprise, design and adoption were very much the problem of the providers. This story weaves it’s way through the emergence of web, social media and culminates with the launch of the iPhone. There is no doubt – the iPhone made technology sexy. To extend the oft-quoted NASA analogy, it was the rocket in your pocket! With the emergence of the app environment and broadband internet, which was key to Web 2.0, it suddenly introduced a whole new ingredient into the technology cookbook – emotion! Steve Jobs didn’t just want technology to be likable, he wanted it to be lickable.

The balance between humans and technology has since been redressed significantly – apps and websites focus on intuitiveness, and molding the process around the user. It means that to deal with a bank, you don’t have to follow the banks’ convenience, for time and place, and follow their processes of filling a lifetime’s worth of forms. Instead, banks work hard to make it work for you. And you want it 24/7, on the train, at bus stops, in the elevator and before you get out from under your blanket in the morning. And the banks have to make that happen. The mouse has given way to the finger. Humans and technology are ever closer. This was almost a meeting of equals.

But now the pendulum is swinging the other way. Technology wants to make it even easier for humans. Why should you learn to use an iPhone or figure out how to install and manage an app? You should just ask for it the way you would, in any other situation, and technology should do your bidding. Instead of downloading, installing and launching an app, you should simply ask the question in plain English (or a language of your choice) and the bank should respond. Welcome to the world of Conversational Systems. Ask Siri, ask Alexa, or Cortana, or Google or Bixby. But wait, we’ve gotten ahead of ourselves again.

The starting point for conversational systems is a chatbot. And a chatbot is an intelligent tool. Yes, we’re talking about AI and machine learning. Conversational systems are one of the early and universal applications of artificial intelligence. But it’s not so simple as just calling it AI. There are actually multiple points of intelligence in a conversational system. How does a chatbot work? Well for a user, you just type as though you were chatting with a human and you get human-like responses back in spoken language. Your experience is no different from talking on WhatsApp or Facebook Messenger for example, with another person. The point here is that you are able to ‘speak’ in a way that you are used to and the technology bend itself around you – your words, expressions, context, dialect, questions and even your mistakes.

Let’s look at that in a little more detail. This picture from Gartner does an excellent job of describing what goes into a chatbot:

The user interface is supported by a language processing and response generation engine. This means that the system needs to understand the users’ language. And it needs to generate responses that linguistically match the language of the user, and often the be cognizant of the mood. There are language engines like Microsoft’s LUIS, or Google’s language processing tool.

Behind this, the system needs to understand the user’s intent. Is this person trying to pay a bill? Change a password? Make a complaint? Ask a question? And to be able to qualify the question or issue, understand the urgency, etc. The third key area of intelligence is the contextual awareness. A customer talking to an insurance company in a flood-hit area has a fundamentally different context from a new prospect, though they may be asking the same question ‘does this policy cover xxx’. And of course, the context needs to be maintained through the conversation. An area which Amazon Alexa is just about fixing now. So when you say ‘Alexa who was the last president of the US’ and Alexa says ‘Barack Obama’ and you say ‘how tall is he?’ – Alexa doesn’t understand who ‘he’ is, because it hasn’t retained the context of the conversation.

And finally, the system needs to connect to a load of other systems to extract or enter data. And needless to say, when something goes wrong, it needs to ‘fail gracefully’: such as “Hmm… I don’t seem to know the answer to that. Let me check…” rather than “incorrect command” or “error, file not found”. These components are the building blocks of any conversational system. Just as with any AI application, we also need the data to train the chatbot, or allow it to learn ‘on the job’. One of the challenges in the latter approach is that the chatbot is prone to the biases of the data and real-time data may well have biases, as Microsoft discovered, with a Twitter-based chatbot.

We believe that chatbots are individually modular and very narrow in scope. You need to think of a network of chatbots, each doing a very small and focused task. One chatbot may just focus on verifying the customer’s information and authenticating her. Another may just do password changes. Although as far as the user is concerned, they may not know they’re communicating with many bots. The network of bots, therefore, acts as a single entity. We can even have humans and bots working in the same network with customers moving seamlessly between bots and human interactions depending on the state of the conversation. In fact, triaging the initial conversation and deciding whether a human or a bot needs to address the issue is also something a bot can be trained to do. My colleagues have built demos for bots which can walk a utility customer through a meter reading submission, for example, and also generate a bill for the customer.

Bots are by themselves individual micro-apps which are trained to perform certain tasks. You can have a meeting room bot which just helps you find and book the best available meeting room for your next meeting. Or a personal assistant bot that just manages your calendar, such as x.ai. We are building a number of these for our clients. Bots are excellent at handling multi-modal complexity – for example when the source of complexity is that there are many sources of information. The most classic case is 5 people trying to figure out the best time to meet, based on their calendars. As you well know, this is a repetitive, cyclical, time-consuming and often frustrating exercise, with dozens of emails and messages being exchanged. This is the kind of thing a bot can do very well, i.e. identify (say) the 3 best slots that fit everybody’s criteria on their calendars, keeping in mind travel and distances. Chatbots are just a special kind of bot that can also accept commands, and generate responses in natural language. Another kind of bot is a mailbot which can read an inbound email, contextualise it, and generate a response while capturing the relevant information in a data store. In our labs we have examples of mailbots which can respond to customers looking to change their address, for example.

Coming back to chatbots, if you also add a voice i.e. a speech to text engine to the interface, you get an Alexa or Siri kind of experience. Note that now we’re adding yet more intelligence that needs to recognise spoken words, often against background noises, and with a range of accents (yes, including Scottish ones). Of course, when it’s on the phone, there are many additional cues to the context of the user. The golden mean is in the space between recognising context and making appropriate suggestions, without making the user feel that their privacy is being compromised. Quite apart from the intelligence, one of the real benefits for users is often the design of the guided interface that allows a user to be walked step by step through what might be a daunting set of instructions or forms or a complex transaction – such as an insurance claim or a mortgage quote.

Gartner suggest that organisations will spend more on conversational systems in the next 3 years than they do on mobile applications. This would suggest a shift to a ‘conversation first’ interface model. There are already some excellent examples of early movers here. Babylon offers a conversational interface for providing initial medical inputs and is approved by the NHS. Quartz delivers news using a conversational model. You can also build conversational applications on Facebook to connect with customers and users. Chatbots are also being used to target online human trafficking. Needless to say, all those clunky corporate systems could well do with more conversational interfaces. Imagine just typing in “TravelBot – I need a ticket to Glasgow on Friday the 9th of February. Get me the first flight out from Heathrow and the last flight back to either Heathrow or Gatwick. The project code is 100153.” And sit back while the bot pulls up options for you, and also asks you whether you need to book conveyance.

Conversational systems will certainly make technology friendlier. It will humanise them in ways we have never experienced before. I often find myself saying please and thank you to Alexa and we will increasingly anthropomorphise technology via the nicknames we give these assistants. You may already have seen the movie “Her”. We should expect that this will bring many new great ideas, brilliant solutions and equally pose new social and psychological questions. Consider for example the chatbot that is desi§gned just for conversation – somebody to talk to when we need it. We often talk about how AI may take over the world and destroy us. But what if AI just wants to be our best friend?

My thanks to my colleagues and all the discussions which have sharpened my thinking about this – especially Anantha Sekar – who is my go-to person for all things Chatbots.

My book: Doing Digital – Connect, Quantify, Optimise – is available here, for the price of a coffee!

As with all my posts, all opinions here are my own – and not reflective of or necessarily shared by my employers.

2016/2017 Shifting Battlegrounds and Cautious Predictions for Digital

Innovation slows down in mobile devices but ramps up in bio-engineering. Voice goes mainstream as an interface. Smart environments and under the hood network and toolkit evolution continues apace.

For most people I know, 2016 has ranged between weird and disastrous. But how was it for the evolution of the digital market?

The iPhone lifecycle has arguably defined the current hypergrowth phase of the digital market. So it’s probably a good place to start. In the post Steve Jobs world, it was always going to be a question about how innovative and forward thinking Apple would be. So far, the answer is not very. 2016 was an underwhelming world for iPhone hardware (though Apple has tried harder with MacBooks). Meanwhile, Samsung which you suspect has flourished so far by steadfastly aping Apple, ironically finds itself rudderless after the passing of Steve Jobs. It’s initial attempts at leapfrogging Apple have been nothing short of disastrous with the catastrophic performance of the new inflammable Note phones/ batteries. Google’s Pixel Phone could hardly have been timed better. By all initial accounts (I’m yet to see the phone myself) it’s comparable but not superior to an iPhone 7, Google’s wider range of services and software could help it make inroads into the Apple market. Especially given the overwhelming dominance of Android in the global OS market. The market has also opened up for One Plus, Xaomi and others to challenge for market share even in the west. Overall, I expect the innovation battleground to move away from mobile devices in 2017.

While on digital devices, things have been quite on the Internet of things front. There have been no major IOT consumer grade apps which have taken the world by storm. There have been a few smart home products, but no individual app or product stands out for me. As you’ll see from this list – plenty if ‘interesting…’ but not enough ‘wow’. I was personally impressed by the platform capabilities of enabling IOT applications, form companies such as Salesforce, which allow easy stringing together of logic and events to create IOT experiences, using a low code environment.

AR and VR have collectively been in the news a lot, without actually having breakthrough moment. Thanks to the increasing sophistication of VR apps and interfaces, with Google Cardboard and the steady maturing of the space. But the most exciting and emotive part of AR / VR has been the hololens and holoportation concepts from Microsoft – these are potentially game changing applications if they can be provided at mass scale, at an affordable cost point and if they an enable open standards for 3rd parties to build on and integrate.

Wearables have had a quiet-ish year. Google Glass has been on a hiatus. The Apple Watch is very prominent at Apple stores but not ubiquitous yet. It’s key competitor – Pebble – shut shop this year. Fitbits are now commonplace but hardly revolutionary beyond the increasing levels of fitness consciousness in the world today. There are still no amazing smart t-shirts or trainers.

The most interesting digital device of 2016 though, has been the Amazon Echo. First, it’s a whole new category. It isn’t an adaptation or a next generation of an existing product. It’s a standalone device (or a set of them) that can perform a number of tasks. Second, it’s powered almost entirely by voice commands “Alexa, can you play Winter Wonderland by Bob Dylan?”, third, and interestingly it comes from Amazon, for whom this represents a new foray beyond commerce and content. Echo has the potential to become a very powerful platform for apps that power our lives, and voice may well be the interface of the future. I can see a time the voice recognition platform of Echo (or other similar devices) may be used for identity and security, replace phone conversations, or also become a powerful tool for healthcare and providing support for the elderly.

Behind the scenes through there have been plenty of action over the year. AI has been a steady winner in 2016. IBM’s Watson added a feather to it’s cap by creating a movie trailer. But away from the spotlight, it has been working on gene research, making cars safer, and even helping fight cancer. But equally, open source software and the stuff that goes behind the websites and services we use every day have grown in leaps and bounds. Containerisation and Docker may not be everybody’s cup of tea but ask any developer about Docker and watch them go misty eyed. The evolution of micro services architecture and the maturing of APIs are also contributing to the seamless service delivery that we take for granted when we connect disparate services and providers together to order Uber cabs via the Amazon Echo, or use clever service integrators like Zapier

All of this is held together by increasing focus on design thinking which ensures that technology for the sake of tech does not lead us down blind alleys. Design thinking is definitely enjoying its moment in the sun. But I was also impressed by this video by Erika Hall that urges us to go beyond just asking users or observing them, and being additionally driven by a goal and philosophy.

2016 has also seen the fall of a few icons. Marisa Meyers has had a year to forget, at Yahoo. Others who we wanted to succeed but who turned out to have feet of clay, included Elizabeth Holmes at Theranos, and the continued signs of systemic ethical failure at Volkswagen. I further see 2016 as the year when external hard drives will become pointless. As wifi gets better, and cloud services get more reliable, our need to have a local back up will vanish. Especially as most external drives tend to underperform over a 3-5 year period. Of course, 2016 was the year of the echo-chamber – a reminder that social media left to itself insulates us from reality. It was a year when we were our worst enemies. Even through it was the Russians who ‘Hacked’ the US elections and the encryption debate raged on.

One of the most interesting talks I attended this year was as the IIM Alumnus meeting in London, where a senior scientist from GSK talked about their alternative approach to tackling long term conditions. This research initiative is eschewing the traditional ‘chemical’ based approach which works on the basis that the whole body gets exposed to the medication but only the targeted organ responds. This is a ‘blunt instrument’. Instead, the new approach takes an ‘bio-electronic’ approach. Galvani Bioelectronics, set up in partnership with Alphabet will use an electronic approach to target individual nerves and control the impulses they send to the affected organ, say the pancreas, for diabetes patients. This will be done through nanotechnology and by inserting a ‘rice grain’ sized chip via keyhole surgery. A successful administration of this medicine will ensure that the patient no longer has to worry about taking pills on time, or even monitoring the insulin levels, as the nano-device will do both and send results to an external database.

Biotech apart, it was a year when Google continued to reorganise itself around Alphabet. When Twitter found itself with it’s back to the wall. When Apple pondered about life beyond Jobs. Microsoft emerged from it’s ashes, and when Amazon grew ever stronger. As we step into 2017, I find it amazing that there are driverless cars now driving about on the roads, in at least one city, albeit still in testing. That we are on the verge of re-engineering the human body and brain. I have been to any number of awesome conferences and the question that always strikes me is, why aren’t we focusing our best brains and keenest technology on the worlds greatest problems. And I’m hopeful that 2017 will see this come to fruition in ways we can’t even imagine yet.

Here are 5 predictions for 2017. (Or around this time next year, more egg on my face!)

  • Apple needs some magic – where will they find it from? They haven’t set the world alight with the watch or the phone in 2016. The new MacBook Pro has some interesting features, but not world beaters yet. There are rumblings about cars, but it feels like Apple’s innovation now comes from software rather than hardware. I’m not expecting a path breaking new product from Apple but I’m expecting them to become stronger on platforms – including HomeKit, HealthKit and to seeing much more of Apple in the workplace.
  • Microsoft has a potential diamond in LinkedIn, if it can get the platform reorganised to drive more value for its, beyond job searches. Multi-layered network management, publishing sophistication, and tighter integration with the digital workplace is an obvious starting point. Microsoft has a spotted history of acquisitions, but there’s real value here, and I’m hoping Microsoft can get this right. Talking about Microsoft, I expect more excitement around Hololens and VR based communication.
  • I definitely expect more from Amazon and for the industry to collectively start recognising Amazon as an Innovation leader and held in the same esteem as Apple and Google. Although, like Apple, Amazon will at some point need stars beyond Bezos and a succession plan.
  • Healthcare, biotechnology, genetics – I expect this broad area of human-technology to get a lot of focus in 2017 and I’m hoping to see a lot more news and breakthroughs in how we engineer ourselves.
  • As a recent convert, I’m probably guilty of a lot of bias when I pump for voice. Recency effect, self referencing, emotional response over rational – yes all of the above. Voice is definitely going to be a big part of the interface mix going forward. In 2017, I see voice becoming much more central to the interface and apps planning. How long before we can bank via Amazon Echo?

Happy 2017!

The Unbearable Bigness Of Data

(And What We Should Be Doing About It)

Big-data


Welcome to the Data Deluge.  

By now you’ve probably gotten sick of hearing about big data, little data, fat data, thin data and all manner of data. You’ve gotten your head around Terabytes, Exabytes and Zetabytes. You’ve noted that the price of data has crashed by 90% over the past few years on a per unit basis. Your CIO has mastered Hadoop and MongoDB and you understand the benefits of data lakes over, say, data puddles. The scary part of all of this is that we’re still in the early days of the data deluge. We are hurtling into a quantified universe fed by smart cities, homes and cars; platform driven models and clickstream driven relationships. In fact, I was having coffee this morning with the well travelled, well informed, and always insightful John McCarthy from Forrester, and we were positing that in a few years from now, data will take over from ‘Digital’ as the centrepiece of the organisational transformation and focus across the world.

Right now, though, we’re caught in a deluge with no real clarity about how we’re going to actually use all the data that’s floating around. And here are three key challenges we’re going to have to deal with:

What, not Why – A New Mindset

A question I often ask my colleagues who are experts in data sciences is as follows: let’s suppose that when it rains, people drink more cappuccinos. Now, if Starbucks knew this, it could advertise or promote cappuccinos every time it rained. It could even launch branded umbrellas. But how would it discover this? Historically, the story would be one of a smart store manager who one day realises that rainy days increases his cappuccino sales, and having defined the premise, starts to collect the data to validate his hypothesis. Or even more traditionally, Costa Coffee runs focused groups, and the link between weather and coffee preferences is established. Critically, a qualitative hypothesis would be at the front of the process and data collection would follow. Because, how else would we know if it’s the rainfall or the pollen count or indeed, the volume of traffic on the roads that we should be correlating coffee sales with?

In the new world of data, or ‘big data’, this works the other way around. A brand like Caffe Nero could take all their sales data across the world, and run hundreds or thousands of analyses, searching for correlation, with any number of external and easily accessible data sources. This includes the obvious ones such as weather, or transport, but also for example days of week or month, time of day, and train and bus schedules, sales in other retail stores, etc. This list is only limited by your creativity and the data availability.

But most fundamentally, this is a shift from why, to what. As well highlighted by Cukier and Schonberger in their book on Big Data, in this new world, we find the correlation first and then the hypothesis. And we actually don’t care why. Let’s suppose we discovered that the coffee consumption actually varied with the tides. We would need to verify whether this was simply a spurious correlation, but from there on, we could go straight to predictability and dispense with the causality, or the ‘why’ question. This is a mind shift for those of us who are used to a ‘scientific’ mentality which requires us to establish causality in order for any approach to rise beyond heuristics into a scaled and logical argument.

The Crown Jewels?

If you haven’t read Adrian Slywotzky’s great book on Value Migration, this would be a great time to start. The book talks through how value migrates from older to newer business models or from a segment to another, or even one firm to another.

We are going to see significant value moving to those companies in each industry that get the value of the data. Be it healthcare, or education, or automobiles, or even heavy industry. Either an incumbent, such as GE, with it’s smart engines and its Predix platform, or challengers such as Amazon, in retail, or upstarts such as 23andme.

The question you want to be asking yourself is, in your industry and in your firm, what are some of the areas of opportunity where you can create new platforms to data-enable processes, or value to customers. How can you converge the primary and ancillary meaning in your data onto areas of your competitive strategy? And also, you may want to perform an audit of what data you might be giving away, perhaps because you feel that it’s not core to your business or you have a player in the industry who has historically be collecting this data. For example, Experian and credit scores. Ask yourself are you merely giving away data that you don’t use, or are you handing over the source of competitive differentiation in your industry? Remember the story about IBM, Microsoft and Intel? I argued this point in my post about Uber and taxi companies, too.

To underscore the earlier point, I believe that value will increasingly migrate, in each industry, to those who best manage, and build strategic & competitive alignment with their data strategies and/ or new offerings based on the data and its meaning.

Adding Love To Data

A couple of years ago, at the annual FT Innovate conference, a lively round table discussion followed after a well known retail CEO had made a presentation about data and analysis. The presentation covered examples of analysing customers to great and occasionally worrying insight, within the industry. From knowing if a woman is pregnant even before she knows it herself, to people having affairs, or stacking beer and nappies together, in front of the stores, all of this can today be deduced from data itself. The debates afterward spilled over onto lunch led to the insight that while there’s been a lot of talk about analysing customers, it misses the point of empathy.

Let’s remind ourselves though – the customer does not want to be analysed. As with any relationship, he or she wants to be loved, cherished, understood and served better.At the end of the day, for most businesses, this translates to a mind-shift again, of adding a layer of human understanding to data, to creatively and emotionally assess the customers’ needs and to allow the analytics to feed off the empathy and emotional connect, rather than be driven purely by the algorithm.

In Sum:

You will hear a whole lot more about data in the coming weeks and months. However, for starters, you could keep these 3 guidelines in mind:

  • Look for correlations, not causality. You want to throw tons of data together and find patterns that aren’t born in some logical causal hypothesis but is simply an observed correlation done at the data level.
  • Be aware that the future of your industry, just like any industry, will involve the value of data. So try to identify and own areas of data which help you drive competitive advantage and/or new products and services, and start building proofs of concept.
  • Add love to data. Don’t just analyse your customers. Bring observation and empathy to the table as well, and marry the analytics with the empathy for best results.
What are your lessons from working with big, small and tiny data so far?

Meet The New Consumer

New consumer

The word revolution is overused, but in the past five odd years, there has been a significant change in how customers engage with products and service providers. Thanks to a combination of technologies, the consumer of 2015 is vastly different from 2010.

Let’s look at 6 specific points of change, which will reshape how you need to engage with consumers today.


The Encyclopaedia Effect: The Consumer Knows More
When a customer walks into a TV showroom today, the smart money is on the probability that he or she knows more about the product than the person behind the counter. In part this is exacerbated by the high turnaround and relative inexperience of shop floor staff, but also because consumers today have all the means and have learnt to thoroughly research their purchase – including features, price comparisons, technologies, accessories and performance. Contributing to this is the ease of garnering information via social media.

How ready are you to deal with this consumer? If you’re a retailer, is this a nightmare scenario or are you able to use this to your advantage? Do you arm your shop floor staff with information? Do you enable consumers to do their own research in the store? Do you provide enough authentic information out there for consumers – so they can trust your information?

In many ways, this puts the onus back squarely on the product or service delivery. You can no longer paper over the inferiority of your products through better marketing or better sales. This is a wake up call for product development and service design people. Get it right or you will be found out.

BYOW: Bring Your Own Web

When I last bought an airline ticket from Pune to Chennai in India, I asked the question on facebook about whether I should fly Airline X who had recently had some bad press and I wanted to check if it was a good idea to fly with them. About twenty people responded. Eighteen said it was a really bad idea (one person was being ironic and one was professionally involved with the airline). I was able to make a decision based on a cumulative 5 minutes of research.

It’s the internet to go. It’s carrying wikipedia, amazon, google and the the world’s product databases in your pocket. Earlier, the physical world and the digital world were distinct. You did your comparison shopping on the web and then took a print out to the store. Now you take the web with you to the store. You scan items with your phone and price compare then and there. Or pull up reviews

and product comparisons. Or check calorific values and nutritional advice. This is not a small evolutionary change. It is game changing.

We know that the mobile phone has already in larger or smaller measures replaced wrist watches, calculators, sat-navs & maps, time-tables and a host of other products. Even a spirit level, if you’re into DIY. But it’s ability to tap into the www wherever and whenever you like is arguably its killer app.

And what about your consumer? What is she checking for while selecting your product? Are you making it easier to find that information? Are you enabling or constricting this behaviour? Does your sales process factor in the always addressable consumer?

Generation M: Beyond Millenials

You’ve probably become accustomed to classifying yourself as a digital immigrant or a digital native. Maybe your kidds are the natives in your household. The “digital generation” aren’t even a homogenous group any more. The internet generation is a different breed from the mobile generation.

The mobile generation, or as Tammy Erickson calls it in this HBR article, the Re-Generation, was born around 1995 or later, is the generation that wants to swipe every screen they come across, and expects to be on multiple screens at the same time. This generation is all about expectations of connectivity, and being willing participants in solving issues – digital activists or at least aware of their role and influence by the virtue of a simple “like”.

If we are to go with this classification, this generation is about to enter the work force, armed with the ability to touch-text like their parents could touch-type. This generation can start a flash mob or, unfortunately, a riot from their hand held devices.

If you’ve been thinking about the “mobile-first” mantra – this is probably the generation of users it is critical for. Expect your first point of contact with consumers from this generation to be on the mobile device. Maybe even a significant part of all your interactions will be on the device. 

Perhaps it’s time to start thinking beyond mobile-first, to mobile-only. How geared up for you for this mobile-only relationship?

The Shazam Effect: Telescoping AIDA

Back then (10 years ago), you heard a song, you tried to find out what it was, maybe you heard it again, then on the radio. Somebody told you what the song was if you were able to hum it. Or you searched the lyrics on the internet. You went to the music store / Amazon and bought the cd, if it was worth the £7.99, or whatever the arbitrary price point for the cd was.

Now you hear and like a song that you’ve never heard before, you “Shazam” it, and it tells you the song, artist, and offers you the chance to buy it with a single click off itunes. In 30 seconds, from never having heard the song, you now own it.

This telescoping of the traditional “AIDA” marketing and sales cycle is what the mobile world is accelerating. Real time is in. Waiting is out. Consumers are starting to expect this in more and more areas. Whether its your bank account or your energy bill, or an itemized break up of your estimate for fitting out a new nursery, there is an increased expectation to make it available now.

How real time is your business? How long do customers have to wait for information about your products and services? How much self service do you enable in the information buffet?


The Interface is Dead: Long Live The Interface

We’ve argued about multi-screens, second-screens and even third screens, but what is happening now, is much more amorphous. The screen is vanishing, yet it’s everywhere. On your watch, in your line of sight from a wearable frame, on your shoes and in your car. In fact, sometimes it’s not a screen at all, just a natural interface. Think of Nest, or Amazon Echo, and it’s not a screen that comes to mind, is it? And once we get into the internet of things, the environment will be one giant interface.

With both computing and interfaces becoming much more amorphous, you and your consumer will always be connected in multiple ways. Are you ready for this kind of commitment?

Federated Identities

The two biggest challenges historically, used to be creating a single view of consumer data and marketing to a segment of one. Today, both are addressable with current technology. This project from Metlife, US is a great example of the former, and Amazon, Apple and Google all do a good job of the latter. The conceptual battle ground has moved. What’s even more granular than the individual? Federated identities.

Your customer in her office and your customer at the park with her kids are not really the same persona. Her needs are different, different receptors are at work, her emotional states are different. How can you tailor the messaging to this kind of contextual personas which are segments of an individual? This is very relevant if we’re going to talk about real time and always connected consumers. You have to model the different personas within a single person, based on context. This is your next assignment, should you accept it.

In Conclusion

These categories are just useful labels to stick onto a wide set of complex and ongoing changes. The journey isn’t over yet, but already, not recognising and adapting to these changes could mean that you are out of step with the consumer of today.

Simplicity – A Very Complex Problem

The holy grail of almost every product and service is simplicity. After all, nobody sets out to create a complicated product. But with digital success so squarely predicated on  engagement and user experience, simplicity has evolved from being an unstated philosophy to a raucous war-cry, uttered often and fervently in board-rooms and product meetings, and KISS (Keep It Simple, Stupid) signs adorning complex project plans. 

Yet, simplicity remains a fiendishly complex challenge. 

Simplicity is a loaded term, it typically refers to things which are basic, or easy to use, or intuitive. It can be interpreted as doing things that bring calmness. In the Indian epic Mahabharata, Judhishtir was so named because he could be calm, in battle. We are usually able to point to examples of simplicity. Whether it’s a home-cooked meal, or the joy of a sunset, our favourite beverage consumed in our favourite chair. The consensus for simplicity in products, tends to be towards intuitiveness. Something that does not require education, training or a manual for example, the Nokia phones and an the iPhone, both in their way wonderfully intuitive. The power of simplicity is also obvious. Simplicity drives acceptance and adoption. It is the reason why soccer is the worlds favourite game, or why the world wide web is indeed world-wide. Simplicity for many people is a deeply held philosophy. For some it is typified by a child like state. 

One might argue that simplicity is born, not created. Some people have a great ability to make things appear incredibly simple. Listen to Alex Fergusson speak about football strategy and tactics, and it will seem like anybody could have done it. And yet, he stands ruthlessly alone in his decades-long and consistent success as a football manager. Closer to home, one of my closest friends from childhood, Kabir, is a well respected and successful leader in the retail industry in India. There are two things I will always remember about Kabir. Almost 20 years ago, when he was a middle manager, handling a menswear category, I asked him why he didn’t stock shoes in his store and he said there was too little return per square-foot of space, where space is the most expensive part of retail, in India. I used to tease him about his fascination about returns per square foot but he had already drilled down to the nub of the problem. The other vivid memory from about the same time, was walking into his office and seeing his desk, an expanse of table surface with nothing but a desk-phone. For somebody like me, who lives and works in perma-clutter, this has always been a utopean and other-worldly fantasy. We see examples of simplicity all around us – Steve Jobs famously had a home environment that embodied simplicity. Mark Zuckerberg’s wardrobe rationale is now well known. Living simple clearly has a contribution to make to your output. But those who can simplify effectively often are seemingly able to reach a higher plane of thinking about a problem. 

Some people naturally simplify, some don’t analyse it, but can go with the flow, for some it’s not desirable – simplicity isn’t a universal desire. It’s not a panacea. But clearly it only makes sense in context. Hence, like beauty, it depends on the eye of the beholder. It may be contextual – if you give a hunter-gatherer a plough, she may not know what to make of it. To a farmer, it may be intuitive. It stands to reason therefore that to design something simple you must be able to see it from the users frame of reference, and appreciate his or her skill and capability. It also means that a way of solving complex problems is to change the frame of reference. Not surprisingly, some of the quoted methods involve asking ‘what would a child do?’ or ‘what would granny do?’. Seen this way, simplicity implies a kind of innocence. A happy naïveté even. Einstein’s warning is probably relevant here. “You must make things as simple as possible, but no simpler.” Over-simplification is a trap to avoid – lest you bore the user. And of course, when your toddler son or daughter asks you how babies are made, you  are caught in the very vortex of the problem of simplification. 

There are probably tomes of scientific papers dealing with simplicity and complexity. Daniel Kahneman’s System 1 thinking is an obvious surrogate for simplicity. The purpose of this post is not to delve into the scientific understanding of how we deal with simplicity, although it would probably be a useful perspective to add. 

The problem is, while many of know how we would define it, and almost all of us would recognize it if we saw it, very few are able to find the path to it. And yet, simplicity is as much a journey as a destination. 

The next time, when you switch on your TV set at the end of a long week and settle in to watch a primetime show on Saturday night, maybe the next series of Breaking Bad, all you’ll need to do is switch on the TV, the set top box, and find the right channel. But for the broadcaster, the process may have started almost a year before. In fact considering the complexity of a broadcast operation – including all the scheduling, planning, program acquisition, ad-sales operations, the movement of physical and digital media, transcoding, automation and transmission, compliance and legal and other areas, it is an everyday miracle that you switch the TV on and there’s something there to watch. (And this is without even considering the effort and challenge of producing the show, manufacturing the TV set and getting the signal into your living room). 

Think of some of your other simple examples – withdrawing money from an ATM, receiving the newspaper at your door in the morning, turning the ignition key in your car; each of these and a hundred more simple tasks often mask an ocean of complexity that goes on unnoticed behind the scenes. This is the first lesson of simplicity. Often, to make something simple, especially for an end user of a product or process, you have to take on and resolve an enormous amount of complexity. Personally speaking, nothing annoys me more than managers who cut short a discussion around a problem by claiming the faux high-ground of simplicity. Complexity doesn’t vanish, it gets resolved, in great detail, by somebody else, and kept under the hood, so you can just turn a key or press a button to start a car. 

In the lives we live today, simplicity is often reductive – it involves removing the noise and complexity. Very rarely is our starting point for anything we’re trying to design, simple. We need to untangle, and even unlearn. And more often than not, it’s a journey. A sentiment echoed by a lot people who responded to my request for interpreting the term. Achieving simplicity is therefore, anything but simple. Especially so, for an organisation or a group, rather than an individual. It therefore involves the mastery of concepts such as minimalism, essentialism or lean. In each of these, we are trained to take Occam’s razor to a problem space. But it can take years of experience to arrive at a state where the eye can spot the waste and the extraneous. The second great lesson of simplicity therefore is the wisdom of spotting the signal in a sea of noise. Or conversely, identifying the waste from the core. This is as true of everyday life, as it is of product design. 

Identifying the waste is just a part of the problem. Sometimes, the far greater challenge is the choice making. To deliver the shiny elegance that is the iPhone, Steve Jobs & co had to make some pretty big choices. An obvious one amongst them being the complete inability of the user to add memory, or change the battery of the phone. Features as core to a phone, you might think, as a leg, to a table. And yet, this was the stark choice exercised by Apple. When was the last time you let go of something significant, to achieve simplicity? 

There are some very successful digital platforms and products out there who owe their success at least to a significant part to their simplicity. Off the top of my head, I can think of Dropbox, Evernote, Trello, and Spotify as some of the highly popular and simple products. The bar for simplicity can often be reset. Salesforce.com is a platform which has succeeded because it greatly simplified the sales management process, but today, if you look at tools like Pipedrive, they make salesforce look like the older, complex model. Remember Einstein’s wisdom about ’no simpler’ – and yet, this can be redefined and barriers can be broken. The journey is the point. 

You only have to look at some of the most successful products, apps and websites to understand how visually simplicity works. From fonts and colours, to choices and tasks, there is now as much science as art to creating the intuitive usability in products that we all crave. But of course, simplicity does not start and end on the screen, it needs to be carried through a range of interactions and experiences. Is it simple to speak with somebody on the phone? Is it simple to return goods? Pay a bill? Find information? Upgrade? Downgrade? And remember for each of these, the simpler you make it, the more complex it often is, behind the scenes. 

A part of the problem is that there is no formula. Most people tend to view simplification as decluttering, removal, or reduction to the basics or essentials. Yet, while this idea is by itself appealing, there are times when simplicity may come through synthesis. Remember the story about the blind men and the elephant. Or imagine trying to describe a car by it’s components. Sometimes simplicity comes from the whole, rather than the parts. Focusing on the whole provides clarity of thought. 

And then there’s the problem of time. What starts of as simple invariably grows complex. Just look at the number of sects of any major religion. Or consider Twitter – from the idea of creating a simple 140 character update, an entirely new language, syntax and ontology of acronyms has risen. Reading a tweet is anything but simple today. It is human nature to complicate, it seems, so the yin and yang of simplicity and complexity must coexist, over time. 

The bottom line is, whether you treat simplicity as a philosophical foundation of your life, or a professional preference, and whether it manifests in your relationships or your products, its clear that you may need to treat it as a series of simplifications and pay as much attention to the process as the outcome. And the journey may be far from easy, and paved with false results. After all, as HL Mencken said, “For every complex problem there is an answer that is clear, simple, and wrong”.