Tuesday, April 12, 2016

Platforms always win

The blogger in me is always alert. Usually I don't feel it is there, sleeping like my dog at my side, while I work.

Browsing through Harvard Business Review, April 2016, I discovered a series of articles about pipeline versus platform products. The first one  Pipelines, Platforms, and the New Rules of Strategy starts like this:
Back in 2007 the five major mobile-phone manufacturers—Nokia, Samsung, Motorola, Sony Ericsson, and LG—collectively controlled 90% of the industry’s global profits. That year, Apple’s iPhone burst onto the scene and began gobbling up market share.
By 2015 the iPhone singlehandedly generated 92% of global profits, while all but one of the former incumbents made no profit at all.
We all knew that. But these two short paragraphs, made my Blogger instinct beep. How was this possible? Because
Nokia and the others had classic strategic advantages that should have protected them: strong product differentiation, trusted brands, leading operating systems, excellent logistics, protective regulation, huge RandD budgets, and massive scale. For the most part, those firms looked stable, profitable, and well entrenched.
 iPhone had an innovative design and novel capabilities. But in 2007, Apple was a weak, nonthreatening player surrounded by 800-pound gorillas. It had less than 4% of market share in desktop operating systems and none at all in mobile phones
 Apple (along with Google’s competing Android system) overran the incumbents by exploiting the power of platforms and leveraging the new rules of strategy they give rise to. Platform businesses bring together producers and consumers in high-value exchanges. Their chief assets are information and interactions, which together are also the source of the value they create and their competitive advantage.
The authors state:
When a platform enters the market of a pure pipeline business, the platform virtually always wins 

Nokia fall 

Ziyad Jawabra, ex CEO of Nokia writes in a a LinkedIn post
During the press conference to announce NOKIA being acquired by Microsoft, Nokia CEO ended his speech saying this “we didn’t do anything wrong, but somehow, we lost”. Upon saying that, all his management team, himself included, teared sadly.

They missed out on learning, they missed out on changing, and thus they lost the opportunity at hand to make it big. Not only did they miss the opportunity to earn big money, they lost their chance of survival.

The moral of the story  

Why Nokia management did not see the change from product to platforms?Because at that time , there was no clever article in HBR to document this strategy.

Large corporations even today hire consultants behemoths  to advise on strategies for survival and growth, all coming from ivy-league schools like Harvard. They know nothing incumbent, still invisible  strategies, not yet ready, that may be included in the their clients business planning and execution.

Where were those consultants when Steve Jobs decided the platform strategy for iPhone? Steve Jobs all he studied and never graduated, was calligraphy. He created the strategy for platforms and proved it works, as we read in this 1st paragraph, decimated the competitors market share. It was all intuitive, contrarian, apparently impossible to achieve.

The moral of the story is that large companies must tolerate and protect crazy, off-beat ideas of their own people, which one day may become the norm. Discipline does nor mean leveling everyone to a single corporate dogma.

See http://my-inner-voice.blogspot.com/2015/12/does-digital-age-require-digital.html

Thursday, April 07, 2016

Do you believe in God? Your Product Stories

A good story does wonders to your brand and sells your product almost irrationally.

But most products do not sell like this and there is nothing iconic in them. Their stories are either missing, or are replaced by user guides, plain vanilla customer success stories, like our widget and icons are better the other's widgets and icons

The final push, why we are better is then summarized in this Matrix Chart, us versus them, that looks like this.

This feature comparison is best tool to introduce lies and inaccuracies, and even if we are honest, our would be customers will feel cheated by the mere sight  of this table. It doesn't take an Einstein mind to see we sell product 3.

We know what a Product Story is NOT. Let's see what is it.

Emotion comes first

Do you know who Zosia Mamet, the actress from series Girls on HBO? She is featured in Forbes magazine April 4, 2016 Zosia Mamet And Evan Jonigkeit: 'The Product Is Secondary To The Storytelling'

Mamet, best known for her role on “Girls,” is the face of an experimental Kate Spade ad campaign that “encapsulates the brand’s essence,” she says. In a series of short films, Mamet plays a woman who is “quirky, kooky and is just being herself.” Much of it is improvised, where she’s says she given a script but told to “just be yourself.”

  • And it’s moving merchandise. “It’s so random. I had to pick out a pair of sunglasses and chose one because they’re cool.” Turns out, she says, all the featured merchandise in the shoot, including the sunglasses, are selling out. “They’re the ones they want to buy. It’s using a creative way to sell something that’s more enticing than just making ads (print, TV, etc.) that show this beautiful girl carrying a bag, saying, ‘You should buy it.’ People want to be told stories that they can connect to. The product is secondary to the storytelling.”

The Emotions that Make Marketing Campaigns Go Viral

This comes  from the Harvard Business Review. It shows how important the emotions are, how to measure them and see if they work. Typical Harvard business stuff: elegant, witty, thoroughly researched and quite useless to apply immediately in practice.

Here is a summary
  • When users engage with brands via content they choose, rather than content they’re given, they are more engaged with the content and the brand.
    •  Create a Viral Coefficient > 1
    •  Viral coefficient can be thought of as the total number of new viewers generated by one existing viewer. 
  •  Use strong emotional drivers to make people care and share it is important to create maximal emotional excitement quickly.
    • Hit them hard and fast with strong emotions, but remember to keep the branding to a minimum. Heavy use of branding can cause many viewers to disregard the content as spammy or salesy, resulting in loss of interest, abandonment, or even backlash.
The last bullet is brilliant.

How to select the emotions that get best viral results? Here is a sample heat map after  six month research in various emotions

This chart seems to emphasize Trust as the most important emotion.

The best product story are a collection of stories 

I found this simple statement is easier said than done. How can you have one story in a corporate culture of many employees, each one with his own emotions?

Can we create a coherent stream of emotions, depending on the audience?
Yes. According Jan Platzer, COO Apcera
Diversity as a business goal is just that – a business goal. It’s not about charity. It’s not giving a hand out. And it is certainly not to patronize.
Special care needs to be taken to avoid the “cultural fit” trap. Many companies list “cultural fit” as a top priority for hiring. To me, this means companies should hire individuals that are passionate about the company’s mission and values. To encourage diversity, “cultural fit” should not be defined as like-minded individuals that are similar in personality, background and thinking.  

The role of fiction

In a recent research Plos One
... they found that the fiction readers who were more emotionally engaged in the narrative became more empathetic over the course of the week. Fiction readers who were not emotionally engaged were less empathetic the following week, and non-fiction readers did not display these effects to a significant degree.

The Product Story author as a ghost writer 

If you like soccer, as I do and if you like Stieg Larsson Millenium Series suspense books, as I do, you may know of a Swedish soccer player names Zlatan Ibrahimovic . His book I am Zlatan sold a million copies, and  it was written by David Lagercrantz,  who will become 5 years later also the author of the book The girl in the spiders web, a huge best seller

A biography Messi, the top soccer player in the world by Luca Caioli is rather boring. Just see this review on Amazon:
This is hands down one of my favorite "autobiographies." I purchased the UK version at Heathrow back in December and could not put the book down. (I tried reading a Messi bio afterwards and it was such a snooze-fest in comparison I still haven't finished it.) I loved the book so much, I purchased the US version to see if there were any noteworthy changes. (There's not and it is definitely worth a second read.) I loved Zlatan's honest, unabashed, attitude towards soccer and life. My only criticism is that this should be available in an audiobook. I would pay good money to hear Zlatan tell the story of Zlatan. I hope Zlatan finishes up his career in the USA so we can get an additional few chapters of in a future version.
 What the reviewer does not feel, what she reads is not the real Zlatan: He is a fictional Zlatan,  a literary creation of the real player from Paris St. Germain team

Writing a book about a soccer player can not be simple. They have no time to spend reading. The ghost has to extract, infer, discover, wonder or hide  all the time

Do you believe in God?

The answer to this question resulted in a book that sold 1,000,000 copies.

Email me at miha at ahrono dot com

Sunday, April 03, 2016

The Machine Learning prophecy from Apcera, Part 2 of 2

This is the continuation of the Part 1 The Machine Learning prophecy from Apcera is proven true

Please read it first

Are applications build by machines? Answer: No!


So now let's bump up a layer. How applications will get built?, (1) are applications built by machines or (2) do people now use those predictions as a sort of composable galleries to build those applications?


It is the latter, the applications become a sort of composable gallery. We have seen even  without machine learning: if you want to build something faster, build less and assemble the pieces, like the services Amazon does. That is amazingly powerful to an enterprise  who's trying to go to the cloud , because they don't want to build a sustainable database of a key-value database whatever that is . That trend will continue, there is value, business value of the application for the developer, but I agree it is just composable service.  I see the cloud wars waged and its services ecosystem front:  (1) Data services  including Big Data, (2)  Human Machine learning  and then (2) Human Machine interfaces.
Whoever comes up with the best classes services is going to win.


Can  you elaborate on the Human Machine interfaces?


Cortana,  Alexa, Siri

The Apcera role


So what is Apcera's role into that? Are you pulling these together and putting the rules around how  to have them deployed and  consumed within the guidelines of a particular organization?


Exactly. Everything becomes more and more complex;   the notion of "how to trust all these pieces that are moving around?" We do get in three to four  years ahead this notion of pumping data into a machine learning algorithm and getting back  some insight for our business.

Who is going to access that? Who is allowed to write new applications to actually consume this information and make better decisions? This world of microservices, who decides which decomposition of the software system we do, that great but this increases the complexity and increases risk. Who is going to take up those risks? All these details are included on the Apcera 's platform, even in more impressive fashion, in my opinion


Derek, this is as always, profoundly insightful

Derek Collison, Apcera and Jason Hoffman, Ericsson

Saturday, April 02, 2016

The Machine Learning prophecy from Apcera is proven true

Hadoop future is changing

At Strata + Hadoop World 2016  from March 28-31, Hadoop turned 10 year old . As Doug Cutting, it's co-creator says:
 [I think ]the continuing changes it helped unleash likely will result in a diminished role for the Hadoop core technology itself in future big data applications.... [I see Spark] as a replacement for MapReduce, 
This  was as a bombshell for many people who consider Hadoop the sine qua non tool for Big Data. Kuldip Pabla and I wrote in 2012 a  Hadoop 101 paper   explaining to the layman.

I discovered it was much easier to write a popular article on Hadoop than using Hadoop.
"Hadoop it’s damn hard to use." Todd Papaioanou  Chief Cloud Architect at Yahoo and his 120-person team were tasked with setting up 45,000 Hadoop servers in Yahoo’s 400,000 node private cloud.
 “Hadoop is hard – let’s make no bones about it, It’s damn hard to use. It’s low-level infrastructure software, and most people out there are not used to using low-level infrastructure software.”

Apcera 2016  Predictions

Among Apcera's 2016 Predictions For The Future Of Enterprise Cloud Derek Collison , the Founder and CEO has the #1
...  Machine Learning, and not what we think of as Big Data today, will provide the insights, predictions, causation and correlations that will drive the modern enterprise within the next two years.
This tweet from November 23, 2015 summarizes why:
The vision (a prediction is after all a vision) surfaced in an interview  at Structure show 2015.  George Gilbert, (George) the analyst from Wikibon.com asks very sharp questions. Derek Collison (Derek) replies.

If you are like me, listening to the tape is not enough. I transcribed some key segments from the video, keeping the casual conversational flow.

Google's do-over


A lot of what is now manifesting  itself in  the enterprise ecosystem, which was the initial MapReduce, and doing laws, and lets slap SQL in front of it, and add more memory, you see all these parallels in the industry. Eventually Google came and said: let's start a do over.
(Miha's note: Google is no longer a search company. It’s a machine-learning company.  For example Google Dream  
  1. It uses probabilities rather than the true/false binary.
  2. Humans accept a loss of control and precision over the details of the algorithm.
  3. The algorithm is refined and modified through a feedback process.)
What we see, right now, is that notion of "whatever Hadoop 3.0 becomes" you know the evolution of Spark, the automation of setting the things up, you know, one click and you spin this thing up - maybe in containers , orchestration, whatever - and you tear them down maybe 10 minutes later.

Something is coming very quickly in our rear-view mirror

What is more interesting though, - as we struggle how to get the data spinning up the back end systems and making sense from it - I think there is something coming up very quickly in our rear-view mirror, that passes us before anyone knows what is going on. This is the Machine Learning. 

No one will be talking Big Data in 24 month (November 2017)

It  is going to get so good, so fast, so in 24 month, I mean literally in less than 24 months, that no one will be talking about Big Data. We will just spit in data like using a fire hose, and then spit out patterns predictions, correlations, causation, that we could never  understood. These technologies are compressing things so hard and our brains are built linearly. That why we can not see it we can not see this.

We will not bother with Hadoop 3.0. We will use "this thing"

I do believe that the notion of Hadoop 3.0 would  simply be, "we will not even bother with it."  We are going to plug our data in the Google Brain project  or other things coming out from Amazon Machine Learning or IBM Watson. Whatever this thing is, we don't have to operate it, or worry about it. We just simply pump data into it and get an amazing amount of value . I truly believe this will happen faster than the people think

Profound Insight and Profoundly Unsettling


This is just a profound insight and profoundly unsettling. Along those lines,   my impression is we always will add more data feeds to improve the context of those automated decisions, and that's a manual process. You can't tell IBM Watson: "Look at all the data feeds that are in the world  and figure out which ones are relevant for improving a false positive I have."


Right now the big problem is how do you model the data correctly to put into these systems. But what I am saying, and I may be wrong,  the ability for them to auto-figure that stuff out , is coming faster than we think. We don't have to teach (the systems) how to do it. 98% of our learning, even as children, is not supervised. I may sound outlandish, and I might be wrong, but my gut tells me this wave of computing and where are we going is coming fastest than our ability to predict when.    

Read Part 2 of this blog

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AI and ML for Conversational Economy