Strategy

Interesting 2 paragraphs from Fred Wilson's blog, talking about "what's next":

But the roadmap has been clear for the past seven years (maybe longer). The next thing was mobile. Mobile is now the last thing. And all of these big tech companies are looking for the next thing to make sure they don’t miss it.. And they will pay real money (to you and me) for a call option on the next thing.

It isn’t clear if the next thing is virtual reality, the internet of things, drones, machine learning, or something else. Larry doesn’t know. Zuck doesn’t know. I don’t know. But the race is on to figure it out. Trillions of dollars of collective market capitalizations are on the line. So a couple billion here or there is chump change. Except for the people who collect that chump change for selling them an option on the next thing. It’s real money to us.

I'm intrigued by the idea of a call option, I think it could be executed better than via VC funding though, Fred - now that would be disruptive

But I think Fred's largely right that Mobile was the last Next Thing - though strictly speaking its not "Mobile" now per se, but PC level processing power meeting Moore's Law and shrinking in size and price so it can be easily portable, with a damn good UI (think iPaq then iPhone). These "Smart" phones and "tablets" killed good old Planet Mobile dead in about 3 years (Motorola, Nokia, Blackberry - where are they now? They were earth shaking giants a few short years ago!)

Anyway, where is the Next Big Thing to be found is the question Fred asks. The future is of course here, just unevenly spread, so the trick is to see what bits of the future are here, now - and actually are going somewhere. Ten things that have changed exponentially in the "networked technology" areas we follow, in the time we've been writing Broadstuff (est 2006) are:

- Robotics (including the flying type)
- Data generation, usage and cost of processing it (aka "Big Data" - but I think the mpact will be felt indirectly - see this video by Phili Evans of BCG
- Penetration and usage of very cheap personal comms....
- .....especially those that are "Things"...
- ....and used to scrape data about "marketing value units" (aka people)
- WiFi Connectivity is increasingly ubiquitous, and whenever a powerful new comms system does that, big changes follow
- Smaller and smaller machine tools (including the over-hyped "3D Printer" but also a whole host of CNC tools)
- Games have moved from PCs to every device, and some games aren't really games any more as the principles move into other areas
- All manners of virtual/augmented reality devices (if Google Glasses don't make them irredeemably uncool)
- "Open" (ie other people's involuntarily exposed) Data - but sadly this will, in my opinion, probably be massively abused and then clamped down again, but its a short term shooting star.

As you can see, these are hardly New New Things, just things that were already here in 2006 and even then clearly had high potential. What's interesting is that they were all already on very predictable development vectors in 2006, but no one looked at them as killer technologies in those days. That was because at that time, their rate of development was still mainly all theoretical, and not provably valuable. To compare, here are 10 other things that were also floating around in 2006/7 that I thought also could happen sooner and haven't yet, but still may as they are all Big Next Next Things potentially.

- Widespread revolt against privacy-invasive free business models (it's starting now though)
- Modular hardware (Fred may recall Bug Labs from 2007)
- Open & portable social networks.
- "New" Search technologies (of which many have been tried over the last few years, a New New search hope appears every 2 years or so)
- Low cost and easy organisation of unstructured data
- Self learning software sorting out my media consumption, inbox and schedule (preferably materialising as a cross-media avatar with Marlene Dietrich's voice)
- VRM (Consumer to Business commercial tools)
- Effective voice recognition (the acid test - can it understand the Scottish accent...)
- And on that note, a near-real time "Babelfish" online service (to translate Scottish to English....)
- Various self-learning Algorithms that could predict things like the stock market, drive cars, etc etc

These are all here today, unevenly distributed, and still chugging along - but at slower rates than the various laws of networking, learning, Moores et al would predict. Typically there is a something in them that is missing, obstinately sticking at current capability or economically unavailable, awaiting the "key" to their leap over the Chasm. But all it takes is a small shift (think iPaq vs iPhone again) and over they go.

All you have to do to build your own mind-boggling portfolio of New Next Things To Watch is read the various Gartner Hype Curves for the last 10 years, and you will see a slew of things on the hot S curve one year and disappearing 2-3 years later. They don't go away though, and are still evolving in the Darwinian mud of technology species, it's just that something hasn't yet quite worked out for them yet. And somewhere in that stew already, are the next 10 New New Things.

Impact of mathematical techniques on operations, by industry - McKinsey


McKinsey has discovered you can use Operations Research (or Decision Maths as it is known these days) mathematical techniques to analyse and optimise manufacturing operations - McKinsey Insights:

The application of larger data sets, faster computational power, and more advanced analytic techniques is spurring progress on a range of lean-management priorities. Sophisticated modeling can help to identify waste, for example, thus empowering workers and opening up new frontiers where lean problem solving can support continuous improvement. Powerful data-driven analytics also can help to solve previously unsolvable (and even unknown) problems that undermine efficiency in complex manufacturing environments: hidden bottlenecks, operational rigidities, and areas of excessive variability. Similarly, the power of data to support improvement efforts in related areas, such as quality and production planning, is growing as companies get better at storing, sharing, integrating, and understanding their data more quickly and easily.

Not only that, but you can apply Lean operating techniques in manufacturing companies too:

Nonetheless, to get the most from data-fueled lean production, companies have to adjust their traditional approach to kaizen (the philosophy of continuous improvement). In our experience, many find it useful to set up special data-optimization labs or cells within their existing operations units. This approach typically requires forming a small team of econometrics specialists, operations-research experts, and statisticians familiar with the appropriate tools. By connecting these analytics experts with their frontline colleagues, companies can begin to identify opportunities for improvement projects that will both increase performance and help operators learn to apply their lean problem-solving skills in new ways.

Amazing stuff....except its very, very old news. Monte Carlo simulations and capacity planning algorithms have been around for decades, a lot of it even pre-dates WW2. Value analysis started at 3M in the 1960's. Richard Schonberger wrote the groundbreaking Japanese Manufacturing Techniques in 1982 (I still have my copy) and he was merely Westernising something the Japanese had been doing for 2 decades by then. And then I saw this, which really made me smile wryly:

Similarly, a leading steel producer used advanced analytics to identify and capture margin-improvement opportunities worth more than $200 million a year across its production value chain. This result is noteworthy because the company already had a 15-year history of deploying lean approaches and had recently won an award for quality and process excellence. The steelmaker began with a Monte Carlo simulation, widely used in biology, computational physics, engineering, finance, and insurance to model ranges of possible outcomes and their probabilities

The wry smile was because I did much the same, in 1994-5, for a steelmaker, using some of these exact same techniques - while I was consulting at McKinsey to boot. I have the obligatory picture of big rolling mills from a grateful client, and the prize I won in the McKinsey internal "Practice Olympics" to prove it In fact I'd bet the McKinsey Quarterly in the 1970's, 80's and 90's will be full of analyses like this one. There truly is nothing new under the sun.

But with New Improved Big Data it can all be rebadged bright and new....except it doesn't work this way. There was a shedload of Big Data in the Old Days too (shop floor data capture techniques underpin most of the Internet of Things, and did you know some of the first broadband networks in the world went in at manufacturers in the 1980's). Manufacturing has always had a lot of data, and Big Manufacturers bought Big Iron to process Big Datasets then too (except it was called data with a small "d" then). The Monte Carlo methods, or N jobs on M machines Optimisation (for examples) are still the same algorithms they were in the 1930's and 50's.

And you know what - you just cannot simulate the minute operation laden details of a shop floor or logistics network reliably. No matter how big your dataset, or your computers, or your machine tool onboard intelligence, there is just too much variability. Which is why the Just In Time/Lean movement came about as the better approach - the aim was to simplify the problem, rather than hit it with huge algorithm models and simulations so complex no one fully understood what they were doing anymore (just ask the banks what happens going down that route) - the aim of JiT/Lean was to actually reduce the problem variability, to get back to Small Data if you like.

And you know what else - despite the analytical miracles I and many others performed in the day, despite the extraordinary efforts by managements and workers, so many of those steel mills (and clothing companies, and manufacturers of a million other widgets) moved East. There is only so much you can do against cheap labour, national subsidies and guaranteed government contracts.

And that brings me to something else in the story, which is what is really going on here I suspect - its not Big Data, its Big Economics:

Many [of these companies] are lean veterans: these companies cut their teeth during the 1990s (when sagging prices hit a range of basic-materials companies hard) and more recently doubled down in response to rising raw-materials prices. The benefits they’re enjoying—an extra two to three percentage points of margin, on top of earlier productivity gains (from conventional lean methods) that often reached 10 to 15 percent—suggest that more big data applications will be finding their way into the lean tool kits of large manufacturers.

Sure, its partly about raw material prices changing - when they are too high to buy or too low to sell you really have to be efficient at manufacturing. But when you are getting to this level of number crunching, after 20 years of Lean projects, in my experience it's because the endgame is appearing on the horizon, its a last of the summer wine story, the end of an S curve. Interestingly, it seems like all the McKinsey consultants and the project were in India, and Eastern labour costs are rising, as is oil for those long ship rides back to the European and US markets, so much so in in fact that there is an increasing trend in re-shoring, as production is coming back to the US and EU. Big picture, the low cost Eastern windfall is ending, and you have to start getting much smarter again about the actual manufacturing process. You can get benefits from doing it right with Big Iron and Big Algorithms, no doubt - but this sounds like back to the future....I suspect they are now using bigger and bigger number crunching to eke the last 20% of improvements from the various kaizen projects ongoing, trying to keep the factories in situ as the Big Economics shift yet again.

And you didn't need Big Data to tell you that....

(Hat tip to my colleagues at the Agile Elephant for the link)

News just in that Facebook has bought WhatsApp for $SillyMoney - $19.6bn - TechCrunch:

With 450 million monthly users and a million more signing up each day, WhatsApp was just too far ahead in the international mobile messaging race for Facebook to catch up, as you can see in the chart above we made last year. Facebook either had to surrender the linchpin to mobile social networking abroad, or pony up and acquire WhatsApp before it got any bigger. It chose the latter.

Facebook couldn't afford not to have it, if someone else had bought it that would have made a direct attack on Facebook's chosen strategic way out of its own declining user engagement, i.e. mobile applications and messaging. Facebook is absolutely determined not to be overtaken by the "next wave" Social Networks. But its own "new wave" systems were kludgy, so the price of not having your lunch eaten in 2016 is c $20bn in 2014. There is a bit of irony in that WhatsApp strongly do not believe in advertisng, their founder once saying that:

"There's nothing more personal to you than communicating with friends and family, and interrupting that with advertising is not the right solution,"

Clearly $19bn is a mind changing amount

What this really shows is that the days of Facebook's organic growth are over, and from now on in they are going to have to acquire revenue, and thats a very expensive way of doing it at their size if you are continually needing to buy the guys who will eat your lunch. You had to believe a lot to believe Facebook's valuation - that just got harder. But its the Bubbletime, so all will be good - for a while

Update 1 - According to El Reg, Whatsapp does indulge in datascraping of a user's address book, so that does make it an interesting prospect for added value datamining.

Update 2 - Azeem Azhar of PeerIndex has a smart bit on analysis - if WhatsApp had stayed independent it still would have destroyed Facebook's mobile story, which really outlines why Facebook had to act to stop itself being eaten for lunch:

- WhatsApp would continue to grow: It was rumoured to be growing at 1m downloads per day, so on an annualised basis close to doubling its user base over the next 12 months, and showing few signs of slowing down.
- It would (and still will) race past Facebook: WhatsApp has already grown faster than Facebook to date. The proposition is terrifically clear and simple. It's like SMS messaging but faster, easier, "free'r" and more fun. Fun Facebook and Twitter may be, there is a platonic simplicity about WhatsApp. There is very little new stuff to learn about its interaction paradigms. No question about what is public or private. Just get up and go. And WhatsApp had been able to stretch us into photo messaging.
- The network effects would take hold: If all (or most) of your friends are on WhatsApp, Viber or Kik or BBM are going to be very lonely places to be. The network effects would take deep root and except in markets where there is limited crossover of users (thing China), WhatsApp will dominate, the way eBay dominated auctions. The best network for a user is the one your friends are on.

Bertrand Duperrin's summary presentation


I've written up the 2 days of case studies from the Enterprise 2.0 Summit I attended last week in Paris, they are over here - Day 1 and Day 2 - on the Agile Elephant blog. There are a number of common threads emerging from the studies. Being lazy, I've copied Emanuele Quintarelli's list to start with, based on his study (as they concur with my analysis), I've added my thoughts in italics:

- The project is explicitly supported and sponsored by the top management (70% vs 34% for laggards). Long lasting processes, technology and process change should be somewhat mandated by the formal organization. Informal projects are easy to start but need formal acceptance to embed themselves in the enterprise

- Social collaboration doesn’t happen without a significant effort. While Enterprise 2.0 may appear as inspired by public social networks, your intranet or employee community isn’t Facebook. Successful projects have a dedicated and professional support (sponsor, community managers, other departments involved) in 91% of the cases (vs 51% of the others). Point was made again and again that the service has to be actively seeded in its early days and reach a critical mass to work

- The right budget should be there. Like any other project, social transformation requires meaningful investments. This means more than 500K Euros or more for 27% of the leaders versus 8% of remaining participants. Even more importantly, the available money should be spent more on the people and strategy dimension than on the technology. Informal systems are point systems, they won't deliver ful value unless they can integrated with other social systems and the other business systems.

- A hybrid roll-out strategy is by far the most effective approach. Top performers use a mixed top-down and bottom-up way process two times more than laggards (52% vs 27%). Senior management involvement should then be balanced with a key role attributed to end-users in shaping both the priorities and the chosen solutions. When uncertain, end-users come first and early-on in the design process. The concept of "chasm jumpers" was interesting, having a group of super users able to explain to the "early majority what the benefits for them are, as early adopters cannot usually explain this n terms later users can understand.

- Culture and business potential are no longer an issue. The strategic importance of social initiatives is proven by the ability of leading organizations in overcoming the “wrong culture” and “not a priority” objections. These two arguments still represent the primary reason of failure for all the others (40% vs 15% for the business priority and 27% vs 10% for the culture). The conclusion again and again was that the projects have to be seen to be solving business problems - and must be usable - for people to use them.

- Measuring participation and business outcomes is not optional. An amazingly high portion of companies aren’t yet looking at collaboration from a quantitative perspective given that 50% of our sample hasn’t adopted any metric. This is true only for 9% of the top performers and many of them look not just for the level of activity (like, views, comments, etc) but especially for the business outcomes (revenues, cost reduction, new products, etc). There is still a strong debate about measurement, some "old schoolers" believe it loses the human element, some pragmatic managers worry that it will put people off using the systems if they feel watched and others that what you measure gets done, but not other things you may want. I think its early days with this area and needs a gentle touch.

Other stand-out observations so far from the case studies:

- Any system needs time to embed/mature/settle in (words varied, but the concept was the same) before it becomes stable and self sustaining, you can't "make a baby in 1 month with 9 mothers" as it were.

- A communication plan is necessary to spread the word, business systems don't "go viral". Also, making onboarding as hassle free as possible and knocking out the "rocks in the road" of the systems as quickly as possible once they appear - and expect them to appear - the projecst that iterated rapidly as user behaviousr exosed flaws seemed to do better.

- Software cost matters - there seemed to be a general pattern of low risk, i.e. if the software already existed in the stack (MS Yammer or Lync, IBM Connexions) it tended to be used.

Over the 2 days I did sense a departure of what the case studies were showing vs. what some of the Social Business theorists were espousing, in general the case studies showed that pragmatism and evolutionary development (what Dachis' Dion Hinchliffe called "Sustainable Transformation") was the order of the day vs more revolutionary/dramatic transformational approaches.

Update - I have also put up Bertrand Duperrin's slides (at top) now that they have been posted up, it was a very good talk on the subject as well

Just had a chance to finally read Janet Parkinson's latest post on this subject - it was to me the stand-out concept of our recent Patchwork Elephant conference, but she has pushed her ideas further - in essence, she argues that “The future is here, it’s just not evenly distributed yet” for businesses becoming just another part of the social network:

Let’s take the social platform Airbnb to illustrate what we mean. Founded in 2008 by Brian Chesky and his roommate when they charged visitiors to sleep on their apartment floor, Airbnb has risen within 6 years to arrange 10m stays in 550 000 rooms in 34,000 cities and is likely to become the world’s largest hotelier within the next year.

As the collaborative economy grows there will no doubt be thousands of other markets – if not all markets eventually – which will become nodes in the social networks. Social networks will allow any capacity to find any demand. Functions like Airbnb, Uber and Lyft will become part of the infrastructure. Just as once we needed AOL to have a web experience now the internet is part of the infrastructure. So it will be with online markets with rating services. That means that any business may have no option but to eventually become just a node in this social mesh – in other words a social object.

In other words "the social network" has become a business market that has already allowed a new form of hotel business to rapidly become almost the biggest market for beds in just a few years. As Janet points out, this is just the beginning:

The [minimum] size of the firm in the case of a supplier to Airbnb is nothing more than your spare room and an internet connection. Ebay was a forerunner to this – but it’s becoming clearer that eventually all the world will become a social market. Any business which tries to limit transparency and remain opaque or is trying to create arbitrage where there is none will find it difficult to compete and maintain their strategic position.

And eventually this will just be a part of "the matrix". Janet also mentions Ronald Coase's theories about transaction costs in making this happen (ie the social network will match buyers and sellers at a fraction of the present costs), I hope thats because she's heard me bang on about them all the time - but the endgame she sees is actually more Nick Carr:

Over time, this mesh will become regulated – infrastructures always do. Electricity, water, telephony all ended up as part of the utility infrastructure and this will be no different. The main problem for the individual will be the sheer scale of the mesh – we will need tools to navigate it. Some tools will come from the infrastructure itself but we imagine that some tools will come from yourself. This ties in closely with the VRM concept of tools being created for individuals to manage and control their own data, allowing access only to those to whom they give permission. We could imagine us all owning our own smart systems with data controlled by ourselves – a bit like owning an electric appliance which you plug into the mesh – that could source the relevant data, barter the deal and present the options in order of importance, then automatically make all the necessary arrangements for you. The opportunity for profiteering in these transactions would be minimal – regulation would be complex.

I think that is still the optimistic outlook, I can imagine quite a few stones on the road - but history has shown other once hard-to-do things have just been absorbed into the infrastructure....eventually.

Google sold Motorola to Lenovo for a lot less than they bought it for - TechCrunch:

Motorola Mobility is being sold to Lenovo, in a deal worth $2.91B. Google is divesting itself of the handset division it purchased for $12.5B in 2011, but it will keep some of the assets — including patents.

Why the drop in value? Well, nothing crashes quite like last year's Mobile Tech Company. Just ask Blackberry, or Nokia, or Palm. And smartphone innovation is slowing anyway.

But those patents, of course, have real value. There are 12,000+ of them, ammunition for defending the barriers to entry against future entrants for years, in all sorts of related technologies, like, oh, mobile Internet of Things tech. And getting cross licencing deals with the other big boys. And just when you've got a new mobile play in your nest.

Innovation is great, M&A is faster, but just in case...lawyers and loads of patents never hurt.

And for Google, writing down $10bn of sunk cost is chump change on an option for 12000 patents. And they keep $3bn in cash. And a nice little tax write-off to boot .

We are told fairly frequently these days that data is the new Oil (or not?). If it is, then is Google the new Standard Oil?

To refresh everyone's memories (Wikipedia):

Standard Oil Co. Inc. was an American oil producing, transporting, refining, and marketing company. Established in 1870 as a corporation in Ohio, it was the largest oil refiner in the world. By 1890, Standard Oil controlled 88 percent of the refined oil flows in the United States.Its controversial history as one of the world's first and largest multinational corporations ended in 1911, when the United States Supreme Court ruled that Standard was an illegal monopoly.

Standard Oil dominated the oil products market initially through horizontal integration in the refining sector, then, in later years vertical integration; the company was an innovator in the development of the business trust. The Standard Oil trust streamlined production and logistics, lowered costs, and undercut competitors. "Trust-busting" critics accused Standard Oil of using aggressive pricing to destroy competitors and form a monopoly that threatened consumers.

John D. Rockefeller was a founder, chairman and major shareholder.

In other words, Standard Oil built up a near monopoly position in oil, not unlike Google's near monopoly position in digital data. Like Standard, Google also uses profits in one area (advertising) to offer free services in many other areas, making it very difficult for competition to emerge in those spaces.

Opinions are still divided about the breakup of Standard by the way:

Some economic historians have observed that Standard Oil was in the process of losing its monopoly at the time of its breakup in 1911. Although Standard had 90 percent of American refining capacity in 1880, by 1911 that had shrunk to between 60 and 65 percent, due to the expansion in capacity by competitors. Numerous regional competitors had organized themselves into competitive vertically integrated oil companies, the industry structure pioneered years earlier by Standard itself. In addition, demand for petroleum products was increasing more rapidly than the ability of Standard to expand. The result was that although in 1911 Standard still controlled most production in the older US regions of the Appalachian Basin (78 percent share, down from 92 percent in 1880), Lima-Indiana (90 percent, down from 95 percent in 1906), and the Illinois Basin (83 percent, down from 100 percent in 1906), its share was much lower in the rapidly expanding new regions that would dominate US oil production in the 20th century.

We are certainly seeing China emerge as an independent digital data region, and othes are splitting off various segments of the digital data mining and refining business. It remains to be seen whether the expected rapid growth in digital data moves at such a pace that Google can no longer supply it all. That will probably be the acid test in the Digital Oil business.

Interestingly, after the breakup, bits of Standard slowly got together again - Two of the resulting companies were Jersey Standard ("Standard Oil Co. of New Jersey"), which eventually became Exxon, and Socony ("Standard Oil Co. of New York"), which eventually became Mobil. They are now Exxon-Mobil. Other Standard spin offs re-combined to form Amoco and Chevron.

Microsoft, came under antitrust investigation for being inherently too large for market competition. The only company since the breakup of Standard Oil that was broken up like Standard Oil was AT&T, and as with the breakup of Standard Oil, many of the "Baby Bells" ended up merging together after changes in regulations and technology, with one of them eventually buying AT&T and adopting the AT&T name.

But, little surprise that Google is ramping up its lobbyist engine in Washington, as reported today:

Google Inc. is moving its Washington office closer to Capitol Hill after spending $18.2 million on lobbying, more than Northrop Grumman Corp. and enough to rank the technology company as the eighth-biggest advocacy spender.

Google is one company that understands that those that forget the past are doomed to repeat it.

Its been a very busy last few weeks what with conference season. work and whatnot so stories ave been piling up in the Broadfstuff inbox. First to go out then is the notes from the FT Innovate Conference. I found this year the best one of all those I have been to over the years as I think its really beginning to sink in that Innovation does NOT mean doing what you did last year, just a bit better, if you want to survive in the modern world order - especially if you a re a high wage, low growth OECD country.

Anyways, notes from Day One here. As per usual, I select what really struck me as new/novel.

Coca Cola

- Innovation is increasingly about getting products to the remoter areas, and finding new products to push through the channels ( aka fuller understanding of the consumer.....).
- Huge use of consumer research plus experts in new ways of packaging/dispensing.
- Shift in what they are looking for in R&D from deep science, to people who can influence and get things done. Technical capability is taken for granted. Global talent pool is good, but competition for scientists from ICT businesses

Werner Vogels, Amazon

The talks was on the Amazon Cloud - Pay as you Go, stick to core competencies, hire flexibly when you need something, great for small companies etc etc but really its basically an argument for the asset rental model. As Nick Carr predicted, someone had to do it, I'm more interested that it remains Amazon in the forefront rather than Telco or a Hoster after several years. I guess they already have the asset base doing something else, so can cost optimise retail hosting at offset prices. Vogels says Amazon Cloud operation has No CIO, No R&D, no innovation teams etc - but the business units are under pressure to improve performance. Sounds like its still small, or it runs on top of an Amazons infrastructure platform that is managed elsewhere. Having been part of large scale Web Hosting in my time, no way do I believe that you can run something like this without a CIO, a large Ops staff and a control room that looks like Cape Canaveral somewhere in the architecture

CapGemini report on Cloud based disruption

Report over here

3 main Cloud drivers:

- Customer experience
- Business model change
- Internal operation

Their test for a true Cloud infrastructure rang true to me too

- Self Provision
- PAYG
- Flexible capacity automatically

Alec Ross, on various trends

- Data is the raw material of the information age, as land was for the agricultural economy
- No shift of power to east, shift of power to data owners - from heirarchy to heterarchy (question from me.....how does one prevent robber barons capturing all the data)
- Wide difference in attitudes to entrepreneurial activity, Europe lowest. Too much "grey twilight" in Europe - nothing ventured, nothing lost. Quotes Disraeli - success is the child of audacity
- The best strategy for any society is one that reduces barriers to participation of women in the economy - 50% of brainpower (is this true - see more money chasing same things)
- But girls fall away from STEM very rapidly post 16 y/o
- Need to manage pregnancy and childrearing as part of corporation operation, ditto mentoring
- Next generation will be a very tough time for middle class - need to learn how to change with the winds, learn new skills etc. Don't havevto be an entrepreneur, but need to know how to work for them.
- The Cloud is just another means of driving labour costs down. If Europe puts up privacy requirements they will go elsewhere.
- Government procurement via lowest cost provider is like buying cheap clothes for a big date
- Government is poor at surgical intervention, good at big picture intervention eg allow SEs to grow to MEs.
- Baby boomers are slowing innovation, high youth countries are increasing innovation. Robotics is being driven by needs of old people care

Gerd Leonard, on various trends 3 - 5 years out

Privacy/Security - Faustian bargain, we are increasingly reneging now - market in tools emerging
- biometrics future? GL thinks there will be limited adoption until it is settled who owns the data and the exchange - digital bill of rights. Technologies not backed up by social contract won't take off
Increasing automation will take rote knowledge jobs away eg Gerds bookkeeper with Xero. Machines will start to do our thinking
Increasing issue around trusting the facts
Shell - looks at 30 years horizon, big infrastructure plays tend to do this
How to force thinking in scenarios - wildcard game (similar to our high sensitivity analysis)
Existing players try to use current tools (eg legislation, regulation) to put off short term changes (online music example)
China - safer? GL thinks they will have to play by global village rules
Opportunities - efficiencies, solutions to major issues
UK is the most tracked country - why? We are a more dysfunctional model, UK bases itself on what US used to do.
Use of data in organisations - the quantified employee - but does it measure the qualified employee. Qualified employee is "economical with the truth"
Can you use this to create a super-intelligent business, but need permission and payback

Startups Session

XL - hard to get data out of old systems, not always accurate in company need to think out the box, size matters but so does innovation and speed
Burberry - some bits very innovative, some bits startup
HSBC - no shortage of ideas, how do you make innovation happen
Shell - good at innovation in own business, harder to work with emergent non traditional models
Ericsson - ex startup guy, how to get mentality in large companies
Independent guy - Reputation management

Decoded (Facilitators) - teach people to code in a day, give people ability to code, changes mindsets of people. Argues that anyone can be one...seems to me they are selling a dream of a startup stereotype though (Developer Rockstar, big spiel around salary jump) Genesis was people who can't code/do computing in companies desperately feeling they need to know. Is this due to structural problem in UK culture/education. Demand is global.

How should innovation happen - everybody, or in a department. Can you manage it in, or can you just cultivate it.
Challenge for large Cos is affording the huge number of wasted work and deaths in an innovative startup ecosystem - what are legal risks of deploying failures
Do you need to have a VC mentality in management, bet on probabilities
Burberry - councils with younger members (more involved in detail, digital natives) to think about solutions

Kimberly Holmes XL - Big Data

Big Data is like teenage sex - lots of talk, not a lot of action. But no currency in talking about small data anymore

Companies who use data driven models are more likely to be correct - models show 6% increase in profit, 5% in productivity. Big data has real ROIs
You can't see patterns in small chunks, eg lots of datapoint arrayed correctly is a movie
Plan for the Big Data, it will come - too many wins for people not to use it - Billy Bean, Moneyball example. Knew he couldnt win otherwise. Biggest risk is doing nothing
XL is first company in space to have centralised analytical team, big benefits quickly.
Aim is not to outrun the bear, but to outrun the others.

Execution is an issue, eg

- Correlation is not causality - need subject matter experts to interpret
- not an IT thing, it's a business thing.
Volume, Variety, Velocity
Who do you hire?
- 2 actuaries, modellers. Curiosity is key - means you are enthusiastic and not wedded to status quo
- need people who are tech skilled, but must have EQ skills, also must be able to ask the right question.

Notes from Day 2 of the FT Innovate Conference

Hal Gregersen

Survey - 7000 respondents, mapped behaviour of innovators. Some points on characteristics:

- Ask disruptive questions ie questions they dont know answers to. Drucker - need to know what questions to ask.
- Questions without actions are useless - 3 types of solvers: Observers, Networkers, Doers. Need all to see and solve problems.
- Look for the Surprises. Find an inside/outside stimulus - networking, find people outside of the people surrounding the problem
- Just experiment, test, try.....key is to reduce the cost of experiments so many experiments can be done

Other points

- Innovation is a Combinatorial Play (Einstein allusion) - putting new combinations/permutation
- Try and work out where you get new ideas, and do it more often.
- Biggest driver of innovation is curiosity, back to asking hard questions.
- Innovators spend 2x more time finding ideas, c 4 months a year vs 2 months. Numbers game, they discover/generate more combinations to play with.

Lynda Grattan, LBS

London Business School - Future of Work trends - 32 trends on future of work (book The Shift - website Hotspots), 10 big ones now:

1. People going on line - growth of online 2x faster than growth of PC usage ie hugely mobile

2. Connectivity drives brain cycles available, 14yo poor Pakistani girl wrote exams for Stanford online, not possible 5 yrs ago. India does have a way to get this talent identified at 13 (does Britain?). Baby boomers did not have to compete with the global talent pool. Lecturer - one of her kids did Medicine, "he is fine". Business school books increasingly not US/EU focus.

3. Innovation is created in 2 structures
- people who are experts getting together
- diverse people getting together
- Globalization/online collaboration will drive huge innovation.

4. Data being available online means brains anywhere can see data these day and work/ learn/make impact.

5. Hollowing out of work in the medium skill area (used to have 1/3 splitbof skilled, medium skill and unskilled. Outsourcing is moving to more skilled work now. Rise of the Specialist, and it's becoming an Art based world. Low Skill jobs will also always be salary limited by new immigrants

6. urbanization is increasing, the dream of electronic cottages is not true (is this due to - but not all cities are the same, seeing creative clusters (patents filed very spiky by city). Specialists need to be near each other. great cities need to attract great people, need great schools, lifestyle etc

7. Demographics/Generation Groups - Boomers, Gen X, Gen Y etc are different. Also look at demographic pyramid. Huge numbers of unemployed educated men equals massive instability. There is no demographic dividend if you don't educate people. Kids today will live to 100, will have to work to 83 to pay for pension at 50% - impossible. What are demographics of companies, and how does that impact things?

8. Huge rise of developing world middle class while hollowed out middle class in West is disappearing.

9. Need to build intangible assets/resilience

- educational
- social
- vitality/emotional

No signals in western countries on what kids should study - art history will no longer get you a job. Have to be educated to be able to transition.

10. Climate change is a fact (even if it isn't, but perception = reality) driving lots of silliness.

Other notes

Social Media is a huge enabler.
Patents are a poor measure of Innovation,also look at R&D budgets

Panel on New Era Workforce

1. Strategos - founded 20 years ago, Gary Hamell.

Innovation - not enough to get talented people, but need to follow through when they want to do stuff or they will bugger off/lose motivation.

2. Grundfos

2 options globally to deal with global differences:
- Franchisers recruit locally
- Corporations try and mandate global cultures

Pumps are not cutting edge.....but are 10% of energy consumption in world. Message to potential recruits is they want to contribute to sustainable world via innovations - very attractive message to talented people. Be crystal clear on how Grundfos changes the world, motivates and inspires. Key to getting output is leadership - giving people freedom, setting up communities that share knowledge.

3. Funding Circle

Allows investors to lend directly to small busineses, c £180m throng it so far, recently started in US.

4. Index Ventures

Real shortage is execution ability. Looking for Doers, ideas is not enough. Isn't this just for small startups? Bias is energy over wisdom, youth over experience - main tell though is making stuff happen with severe capital constraints.

Grundfos - measure young on output, older on targets - can't use one model for all. Thing you do for all is create common understanding of the why.

2 types of innovation - exploration and exploitation. Exploitation only goes to local optima, very exciting as feedback loops are faster, but need to keep eye on exploration.

Questions

Q. Conference focusses on Technology innovation,what about Management innovation (are despots like Jobs better than democrats - tent pole companies tend to be led by despots)?
A. Too much focus here on former.

Q. Source of the Skilled Workforce?

A. Grundfos - Universities are not preparing people for future, are still in the industrial paradigm. Not seeing speed of change in Education market. This is not being debated in public forum, esp how to have high skills in West?

A. LSE - universities can never produce the workers that companies need Now! Can only play on longer trends. Companies need to do some of the training themselves,again,as theyonce did.

A. Strategos - kids need broader exposure than narrow pipe courses

A. Index - many small startups in Europe are people who have worked with each other awhile, impossible to do in Valley - Cohesiveness as competitive advantage.

Some points from Samsung Experience of Innovation and reserach done

3 Pillars:
Crisis Awareness (6 months)
Creativity & Innovation bridges gap between 6 month and 10 years
Future Thinking (10 years)

Crisis today is in delivery, usually because ambition is too high, funding is too low. Lockheed Skunkworks was very well resourced and funded.

Another Crisis is in consumer research, it's too facile. Ethographic research is very expensive, consumer research can be lied to.

Big Data is like Big Oil, to be useful it needs to be separated into constituent parts. Using Black Swan Co to help with data mining.

Biggest issue is Experience.

Different people see different pictures eg human researcher, consumer researcher, technologist, commercial analyst all see world differently, need all to experience everything.

Anybody can buy the process of innovation, what is hard is the management knowing when to persevere vs drop something - entrepreneurs good at that, companies less so. What are your peoples experience? Users?

Process alone gives you gadgets, need experience to get innovative. Experience is a flow chain from product via company, partners and consumers, very few see across this value chain.

Kickstarter is a great pre-test of innovation of "virtual products", great way to test viabiliity of products before you've made them. Also being used as "moral patents". Web being used to float virtual product.

"Experience Farm" - pooling innovation between non competing companies. What is impact on IP though? IP should only be to protect investment in innovation.


Innovation in Developing Markets

Singapore - is small, has to be high value innovation, has to be nimble, multi racial/culture. 3.5% of GDP in public innovation. See integration of design to manufacturing/technology. Nation is doubling support for small companies. Set up institute of consumer insight.

Asia is a Different market, different people - even to skin and hair type. A lot of US/EU data mining/biz model assumptions just don't work

Latin America

In last 10 years 20% of LA consumers became middle class, while there is 15% population growth = 160% market growth. Huge upgrade in quality of life.

What is signal of cost/benefit - in EU it's the product, in LA it's the brand.

Older technologies in US/EU still very attractive, also adoption of stuff by demographic is very different owing to culture. Favela people have very high hygiene/beauty standards, poor people buying US Middle Class stuff (signal of status as they can't afford more expensive signals)

Big lesson is that OECD HQ stuff won't work in these markets, needs local change ie innovation. Eg hygiene habits differ, need different products.

There are infrastructure gaps in Asia and LA, but it differs by country so different products have different problems.

Quite easy in large companies like P&G to see same habits in different country, need to join the dots between products that work and similar countries - key is analysing data.

(Social Business patchwork elephant - Image courtesy of Elmer and www.echidnaontheloose.com)

 

Three years ago, in Social Media Week London, we took an in depth look at the emerging Social Business world. We called it a "patchwork elephant" as it is very large, in the room, but it's hard to see the whole thing!

This year we are doing a review (see here to book) - next Friday, September 27th from 1 to 5.30 pm, at the Hub, Westminster. We will look at what lessons we can learn from the last 3 years since our first event, what is happening today, and where it is all going.

This event is for people interested in discussing the evolution, current state, and future of Social Business, We will discuss issues such as the implementation challenges it will face, how it integrates with existing systems today, which industries and parts of companies are likely to be early adopters, what is the impact on how work is organised and done, and what is still required for it to succeed.

The session will feature talks, Q&A sessions and case studies from companies, consultants and academics working in various parts of the Social Business value chain.

The aim is to leave attendees with a good appreciation of where social business came from and where it is today, and the main opportunities - and threats - going forward. For those already working in the area it will give visibility of more of the whole area from a variety of other viewpoints. Also the event is an opportunity to meet like minded interesting people from the London/UK scene, we will repair to a pub nearby afterwards!.

Our star-studded speaker line up is, in strict alphabetical order:

- Will McInness, Nixon McInnes

- Mat Morrison, Star Media

- Anne-Marie McEwan, The Smart Work Company

- Luis Suarez, IBM

- Neil Usher, WorkEssence

Plus some thoughts from us, the organisers - the Patchwork Elephant motley crew are:

- Janet Parkinson, Technotropolis

- Alan Patrick, Broadsight

- David Terrar, D2C


Twitter Hashtag is #smwsocbiz

 

The event is sponsored by ComparetheCloud.net