Author Archives: Espen

About Espen

For details, see www.espen.com.

Analytics III: Projects

asm_topTogether with Chandler Johnson and Alessandra Luzzi, I currently teach a course called Analytics for Strategic Management. In this course (now in its third iteration), executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. We have just finished the second of five modules, and the projects are now defined.

Here is a (mostly anonymised, except for publicly owned companies) list:

  • An IT service company that provides data and analytics wants to predict customer use of their online products, in order to provide better products and tailor them more to the most active customers
  • A gas station chain company wants to predict churn in their business customers, to find ways to keep them (or, if necessary, scale down some of their offerings)
  • A electricity distribution network company wants to identify which of their (recently installed) smart meters are not working properly, to reduce the cost of inspection and increase the quality of
  • A hairdressing chain wants to predict which customers will book a new appointment when they have had their hair done, in order to increase repeat business and build a group of loyal customers
  • A large financial institution wants to identify employees that misuse company information (such as looking at celebrities’ information), in order to increase privacy and data confidentiality
  • NAV IT wants to predict which employees are likely to leave the company, in order to better plan for recruitment and retraining
  • OSL Gardermoen want to find out which airline passengers are more likely to use the taxfree shop, in order to increase sales (and not bother those who will not use the taxfree shop too much)
  • a bank wants to find out which of their younger customers will need a house loan soon, to increase their market share
  • a TV media company wants to find out which customers are likely to cancel their subscription within a certain time frame, to better tailor their program offering and their marketing
  • a provider of managed data centers wants to predict their customers’ energy needs, to increase the precision of their own and their customers’ energy budgets
  • Ruter (the public transportation umbrella company for the Oslo area) wants to build a model to better predict crowding on buses, to, well, avoid overcrowding
  • Barnevernet wants to build a model to better predict which families are most likely to be approved as foster parents, in order to speed up the qualification process
  • an electrical energy production company wants to build a model to better predict electricity usage in their market, in order to plan their production process better

All in all, a fairly typical set of examples of the use of machine learning and analytics in business – and I certainly like to work with practical examples with very clearly defined benefits. Over the next three modules (to be finished in the Spring) we will take these projects closer to fruition, some to a stage of a completed proposal, some probably all the way to a finished model and perhaps even an implementation.

How to write a teaching case

Since I sometimes give classes on how to do case teaching and have written a book on the subject with Bill Schiano, I am sometimes asked how to write a teaching case. The following blog post is a quick tips & tricks collection (essentially an update of this post.)

Why are you writing this case?
First and foremost: Cases are written for a teaching purpose – and to write a teaching case, you need to have a teaching objective in mind. It is not enough to have an interesting company. Even the best company story needs to have a pedagogical point, a theory or dilemma to illustrate. So don’t write a teaching case just because you happen to know someone in a really interesting company – it does need to be a good story, but it also need to have a purpose.

The standard outline
Cases – particularly the standard HBS case – follow an outline that can seem rather trite, but which is very effective. It is something like this:

  • 0.5 page: Intro: The protagonist is introduced, typically pondering a question of some importance. The idea is to tell the students from which perspective the case is written, to set the scene – and that is all there is to it.
  • 1 – 1.5 pages: Description of the company – not the whole history, but the relevant details, explaining what the company is doing, how they make their money. Most companies are to a very large degree formed by their history, so the relevant parts need to be told.
  • 1 page: Industry. Companies exist within a context, and you need to set it. Explain the industry, its evolution, and the company’s position within it. Do it succinctly, but leave more detail in than what is strictly necessary.
  • 1 – 5 pages: Specific issue. This is the meat of the case, the issue at hand, the story to ponder. Make sure you tell it logically and cooly, not leaving anything out, but also conveying the complexity of the situation.
  • 0.5 page: Conclusion, typically with the protagonist wondering what to do, often with some sort of event (board meeting, etc.) where he or she has to present a solution to the problem.

Most cases are just that – one case. You can have a B case and even a C case, but keep them short, since they have to be handed out and read in class. The B case should explain what the company did and perhaps introduce a new problem, the C case, if necessary, should bring some sort of closure, explaining what eventually happened. In my experience, it is very hard to get discussion after a C case – the students become exhausted. As a novice case writer, especially if you are writing about a company with a long history, it can be tempting to create a long string of small cases, but in practice this seldom works well – for one thing, it forces the discussion into a very predictable path.

The no-nos
No theory. A good case should be a description of an interesting situation, frequently a decision point – and nothing else. This means that there should be no theory and no discussion of the case in the case itself. Save that for the teaching note, or write a separate academic article about it. Not only does this make the case more realistic, it also means it can be used for more purposes than the one initially envisioned. This can be quite challenging for the traditional academic writer – but it is actually good practice to only present the facts (though, of course, which facts you choose to present constitute a discussion of sorts).

No hero, no villain. When teaching students how to analyze a case, I always start by saying that for most business situations, if is useful to begin the analysis with the assumption that people are not stupid and not evil. Consequently, when you write a case, make sure it has no heroes and no villains. If a case has a clear-cut hero or villain, it is a sign that you have not done enough research. Write things so that the students can see the issue from many perspectives.

No judgement. I frequently find, when reading prospective teaching cases, especially if written with the help of the communications department of a company, that judgements tend to be embedded in the text itself, though it is supposed to be as neutral and purely descriptive as possible. Do not write that a company is “leading” – instead, describe what they do, perhaps adding a bit of industry numbers for comparison, and let the decision about how leading the company is be up to the student. Do not write “Smith was a highly accomplished manager, leading a successful technology implementation.” Instead, write “Smith, at 29, was promoted to be the youngest vice president in company history after conceiving and overseeing the introduction of the first machine learning platform in the industry.”

No consultingese. Be very careful of weasel terms – is the company embarking on a strategic alliance or merely collaborating with another company for a specific purpose? (As a friend of mine said: “I buy all my socks at Marks and Spencer. That does not mean I have a strategic alliance with them.”) Avoid terms that are not well defined, and be precise in your language. Remember, a teaching case has the longest legs when it describes a human situation (since humans change slowly) and to not tie the narrative to specifically to a technology or a situation. (See the example of Fabritek below.) I quite often use old technology cases, and when the students complain, ask them to take out a pen and change the dates mentioned to current times, update the technology capabilities accordingly – and see if anything else changes.

Dramatic structure
A really well written case has dramatic structure – there is a beginning, a middle that builds up the story, and a really compelling issue at the end. The best cases are almost like a detective story, where you have to dig deep into the analysis to find surprising and sometimes counter-intuitive conclusions. One example of a “detective story” case is Fabritek 1992*, a very old (first published 1969, rewritten by Jan Hammond) case about a quality control issue in a small mechanical workshop. (Hat tip to Robert D. Austin, eminent case teacher, for making me aware of this case and showing me how to teach it.) The case is excellent because it starts with the company (strategic level), proceeds to describe a new situation and a new process (organizational or business logic level) and then introduces the problem (operational level.) Analyzing the operational details leads to one conclusion, which then can be discussed in terms of the organization and its business logic, which can then be placed into a strategic context. The case is excellent because it allows links between these levels – and also teaches the students that the devil indeed resides in the details, and that you as a manager better be very close to how the business you are leading works and makes money.

iPremier-front-pageA second case which shows quality and innovation is iPremier, written by Robert D. Austin and Jeremy C. Short, the first and only graphic novel (cartoon) case I am aware of. The story is about a small online gift company being attacked by hackers, exposing glaring gaps in their security procedures and forcing managers at various levels to make some really hard decisions. The graphic format is excellent in making the various characters real (though they, on average, tend to be way too good-looking for a normal business situation), illustrates technical issues in a way that is very understandable even by non-technology students, and has a cracking good storyline with a B and a C case. I like to introduce a few technical cases in my courses because, well, I don’t think there is enough technology in business schools, and this cases answers very well because it illustrates that certain technical decisions very much require top management attention – ignore (or mindlessly delegate) technology understanding and responsibility at your peril. The graphic format also provides a welcome break from the standard case verbiage, which can be a trifle dour on occasion.

Details, details, details!
Research cases – the kind that is published in refereed journals – tend to be written from a very specific viewpoint, and only facts pertaining to that perspective is included, often in a very abstract format. A teaching case is the direct opposite: It needs lots of details, frequently made available as exhibits (graphs, pictures, documents, tables, etc.) placed at the end, after the main text. A teaching case writer, when visiting a company to write about it, needs to notice the small details, much like a really good journalist does. I tell my students that they should prepare each case so well that they feel like they have worked in the case company – and to allow them to do that, you need to provide the operational details necessary. (Incidentally, having more details than strictly necessary has the added benefit of making the case realistic – in the real world, you have to decide what is important and what is not.)

Doing it – and reading about it.
grandongillI am not aware of many books about how to write a good teaching case, with one exception: Grandon Gill (pictured), professor at University of South Florida and an excellent case teacher, has written a book called Informing with the case method, which is available for free download in PDF, MOBI and EPUB format from his web site. It has lots of details, tips and tricks, not just about case writing, but also about case teaching and course planning. (For the latter, of course, I am duty bound to recommend Bill Schiano’s and my book Teaching with Cases: A Practical Guide.)

Last but definitely not least: Don’t underestimate how much work writing a proper business case is. Getting the details right, describing the dramatis personae, and making the storyline compelling is quite a challenge, in many dimensions different from the traditional academic article. On the other hand, should you get it right, you will have a very effective teaching tool for many years to come.

Good luck!

Peter as the bionic man

Peter Scott-Morgan, former colleague of mine and a mercurial mind in so many dimensions, was relatively recently diagnosed with motor neuron disease (MND or, more popularly known as  ALS or Lou Gehrig’s disease. Steven Hawking had a slow-moving version of this disease. Charlie Osborne, a friend and fellow doctoral student of mine, died from it.) A devastating blow to anyone, Peter has, not unexpectedly, turned this into an opportunity to explore technology as a way of staying connected with his surroundings.

MND gradually shuts down the communication system between your body and your brain, and can leave you trapped with a fully functioning brain locked inside a body you cannot control. Peter, who has a Ph.D. in robotics and energy enough for a platoon, aims to do whatever he can to stay not just alive, but communicating and functioning, as long as possible. To do this, he has had several operations to facilitate technology access to his body and continued functioning (you eventually lose control of the smooth (involuntary) muscles as well as the regular ones) after his brain stops communicating naturally.

Dr Peter Scott-Morgan on Vimeo.

In order to install and to a large extent develop this technology, he needs help finding people with expertise and interest in developing new technology using him as the experimental subject and development partner. Peter is no stranger to being a pioneer – him and his spouse Francis Scott-Morgan were the first gay couple to be married in a full ceremony in the UK in 2005.

So – anyone with knowledge of medical technology and interest in this project – please get in touch with Peter! (And incidentally, there is already going to be a documentary about his quest.)

Anne-Cath Vestly on where children come from

anne-cath-_vestlyAnne-Cath Vestly (1920 – 2008) was a much loved Norwegian author of children’s books, who challenged many of the prejudices of her time by writing about non-stereotypical families where the mother worked and the father was home, children who lived in high-rises rather than in saccarine little houses, etc.

In 1953 she was reading a story on the radio program “Barnetimen” (The Children’s Hour) about the little boy Ole Aleksander whose mother was going to have a baby. Telling children how babies came to be was considered shocking at the time, so this created an uproar, even death threats. As she said, it seemed to be the grown-ups that were bothered – the children wrote her letters telling her not to feel sad.

She also did radio programs for grown-ups, so her response was to write a short story about a little boy who is going to have a sibling, and how he found out about it. And just the other day, I met a little American boy who was very confused about how someone could have a baby without being married (apparently, he thought it was the marriage ceremony that produced the offspring), so I decided that a translation into English of Anne-Cath’s story was in order. Totally unauthorised, of course, errors are all mine, but very much in her spirit. (And you can find some of her books in English – at astonishing prices – at Amazon.)

A Children’s Hour for Grown-Ups

Anne-Cath Vestly, 1953

Once there was a small boy named Anton. He lived in a small house in a small town with his father and mother. One day, just before dinner, his mother was knitting a small jumpsuit. Perhaps you know what that is?

Anton was sitting and looking at the knitting – for a long time. Then he said:

— That is way too small for me, Mum.

— So you think it is for you?, said Mum, and her face suddenly looked a bit peculiar.

— Isn’t it?, said Anton.

— No, this for a little baby, said Mum. Would you like it if we had a little baby?

— Oh yes, could we? said Anton. But, Mum — how can we get a baby?

Mum was quiet for a little bit, and then she started to knit very fast. Then she said: Those babies … we buy them out in the countryside.

— Out in the country they sure have many things, said Anton. I wish we lived there, because then we could go and look at those babies every day.

— Yes, said Mum.

— But Mum, when we were in the country this summer, do you remember that lady in the red house? She went to the city, and when she came back she had a baby?

— Oh yes, said Mum, those who live in the country go to the city to buy their babies!

— Oh, said Anton. So I was bought in the country?

— Yes, said Mum.

— Was I expensive??? asked Anton.

— Oh, not too bad, said Mum.

— When are we going to go and buy that baby? asked Anton, and now he was really excited.

— Not too long now, said Mum, but, you see, you cannot come with us.

— Why not? said Anton. I bet I could pick the best baby for you!

— Now, you should not ask so many questions, said Mum. You were really cute when you were smaller. You didn’t ask so many questions then…

— Couldn’t I talk then? asked Anton.

Right then, they heard Dad coming home, and Mum ran out to the hallway and whispered to him: Now I have finally told him about the baby!

— Great! said Dad, coming in. Hello, my boy! I guess you are really excited about whether it will be a girl or a boy the stork will bring?

— The stork? said Anton.

— Yes, don’t you know that it is the stork that brings the small babies? It flies all the way from the Nile with the little baby in its blanket, and then it flies over all the houses and drops the little baby right down to us.

— Well, um, said Mum, I just told Anton that we buy the small babies out in the country.

— Oh, said Dad. Well, I, at least, came with the stork. I remember it distinctly.

— Come and have dinner, said Mum.

After dinner, Mum and Dad wanted to rest for a while, and Dad said: Con you sit in the living room and mind the house for a while, Anton?

— Yes, said Anton.

In a little while, there was a knock on the door. Anton opened, and there was old Aunt Agnete.

— Is you mother home? she asked.

— Yes, she is home, but she is so very tired, so she is resting.

— Is she now, said Aunt Agnete. Can’t be too long now, she murmured to herself.

On the table was Mum’s knitting. Anton looked at it and said: Aunt Agnete, you know what? We are going to buy a baby!

— So you know? said Aunt Agnete. Her face turned very peculiar for a while, then she cleared her throat and made her voice almost normal and said: But you are not going to buy one, little friend. Don’t you know that the little babies lie in a large pond, up in heaven above the skies. There they swim around all the time until they find that they want to come down to us, and then they glide down one quiet night and snuggle into their bed under the duvet. And when you wake up next morning, there is a baby there!

— Where you born that way? said Anton.

— Yes, said Aunt Agnete.

— Can you swim? said Anton.

— No, I can’t, said Aunt Agnete. She taught it was a bit odd that Anton was not more interested in the babies she talked about, but she was mistaken.

— Have you forgotten how, then? said Anton. You knew how to swim when you were crawling around that pond…

— How you talk, said Aunt Agnete. Imagine, crawling…

— Maybe you were just swimming the breaststroke, said Anton.

— What a naughty boy you have become, said Aunt Agnete. Why don’t you go out and play for a while, I’ll mind the house in the meantime.

Anton went out, where he met old Martin who used to sweep the streets in the little town.

— Martin, said Anton. How were you born?

— I, said Martin, came sailing down the river on a board.

That night Anton lay awake thinking for a long time before he fell asleep. When he woke up the next morning, Mum had indeed gone, and Anton went out to play with a little boy named Lars.

— Mum has gone to the country to buy a baby, he said proudly.

— That’s not how it works, said Lars. Don’t you know that little babies are inside their mother’s tummy, and when they come out they are all finished with eyes and a nose and everything. Isn’t that great?

— Yes, said Anton. That is really great.

Then he sighed and said to himself: If only it had been that simple…

Summer reading for the diligent digital technology student

eivindgEivind Grønlund, one of my students at the Informatics: Digital Business and Leadership program at the University of Oslo sent me an email asking about what to read during the summer to prepare for the fall.

Well, I don’t believe in reading textbooks in the summer, I believe in reading things that will excite you and make you think about what you are doing and slightly derail you in a way that will make you a more interesting person when Fall comes. In other words, read whatever you want.

That being said, the students at DigØk have two business courses next year – one on organization and leadership, one on technology evolution and strategy. Both will have a a focus on basics, with a flavor of high tech and the software business. What can you read to understand that, without having to dig into textbooks or books that may be on the syllabus, like Leading DigitalThe Innovator’s SolutionEnterprise Architecture as Strategy, or Information Rules?

Here are four books that are entertaining and wise and will give you an understanding of how humans and technology interact and at least some of the difficulties you will run into trying to manage them – but in a non-schoolbook context. Just the thing for the beach, the mountain-top, the sailboat.

  • 816Neal Stephenson: Cryptonomicon. The ultimate nerd novel. A technology management friend of mine re-reads this book every summer. It involves history, magic reality (the character of Enoch Root), humor, startup lore, encryption and, well, fun. Several stories in one: About a group of nerds (main protagonist: Randy Waterhouse) doing a startup in Manila and other places 1999, his grandfather, Randall P. Waterhouse, running cryptographic warfare against the Germans and Japanese during WWII, and how the stories gradually intersect and come together towards the end. The gallery of characters is hilarious and fascinating, and you can really learn something about startups, nerd culture, programming, cryptography and history along the way. Highly recommended.
  • 7090Tracy Kidder: The Soul of a New Machine. This 1981 book describes the development process of a Data General minicomputer as a deep case study of the people in it. It could just as well have been written about any really advanced technology project today – the characters, the challenges, the little subcultures that develop within a highly focused team stretching the boundaries for what is possible. One of the best case studies ever written. If you want to understand how advanced technology gets made, this is it.
  • 24113Douglas Hofstadter: Gödel, Escher, Bach. This book (aficionados just call it GEB) was recommended to me by one of my professors in 1983, and is responsible for me wanting to be in academia and have time and occasion to read books such as this one. It is also one of the reasons I think The Matrix is a really crap movie – Hofstadter said it all before, and I figured out the plot almost at once and thought the whole thing a tiresome copycat. Hofstader writes about patterns, abstractions, the concept of meta-phenomena, but mostly the book is about self-referencing systems, but as with any good book that makes you think it is breath-taking in what it covers, pulling together music, art, philosophy and computer science (including a bit on encryption, always a favorite) and history. Not for the faint-hearted, but as Erling Iversen, my old boss and an extremely well-read man, said: You can divide techies into two kinds: Those who have read Hofstadter, and those who haven’t.
  • 34017076Tim O’Reilly: WTF? What’s the Future and Why It’s Up to Us. Tim is the founder of O’Reilly and Associates (the premier source of hands-on tech books for me) and has been a ringsider and a participant in anything Internet and digital tech since the nineties. This fairly recent book provides a good overview of the major evolutions and battles during the last 10-15 years and is a great catcher-upper for the young person who has not been been part of the revolution (so far.)

And with that – have a great summer!

The history of software engineering

grady_booch2c_chm_2011_2_cropped

The History of Software Engineering
an ACM webinar presentation by
ACM Fellow Grady Booch, Chief Scientist for Software Engineering, IBM Software
(PDF slides here.)

Note: These are notes taken while listening to this webinar. Errors, misunderstandings and misses aplenty…

(This is one of the perks of being a member of ACM – listening to legends of the industry talking about how it got started…)

Trust is fundamental – and we trust engineering because of licensing and certification. This is not true of software systems – and that leads us to software engineering. Checks and balances important – Hammurabi code of buildings, for instance. First licensed engineer was Charles Bellamy, in Wyoming, in 1907, largely because of former failures of bridges, boilers, dams, etc.

Systems engineering dates back to Bell labs, early 1940s, during WWII. In some states you can declare yourself a software engineer, in others licensing is required, perhaps because the industry is young. Computers were, in the beginning, human (mostly women). Stibitz coined digital around 1942, Tukey coined software in 1952. 1968-69 conference on software engineering coined the term, but CACM letter by Anthony Oettinger used the term in 1966, but the term was used before that (“systems software engineering”), most probably originated by Margaret Hamilton in 1963, working for Draper Labs.

Programming – art or science? Hopper, Dijkstra, Knuth, sees them as practical art, art, etc. Parnas distinguished between computer science and software engineering. Booch sees it as dealing with forces that are apparent when designing and building software systems. Good engineering based on discovery, invention, and implementation – and this has been the pattern of software engineering – dance between science and implementation.

Lovelace first programmer, algorithmic development. Boole and boolean algebra, implementing raw logic as “laws of thought”.

First computers were low cost assistants to astronomers, establishing rigorous processes for acting on data (Annie Cannon, Henrietta Leavitt.) Scaling of problems and automation towards the end of the 1800s – rows of (human) computers in a pipeline architecture. The Gilbreths created process charts (1921). Edith Clarke (1921) wrote about the process of programming. Mechanisation with punch cards (Gertrude Blanch, human computing, 1938; J Presper Eckert on punch car methods (1940), first methodology with pattern languages.

Digital methods coming – Stibitz, Von Neumann, Aitken, Goldstein, Grace Hopper with machine-independent programming in 1952, devising languages and independent algorithms. Colossus and Turing, Tommy Flowers on programmable computation, Dotthy du Boisson with workflow (primary operator of Colossus), Konrad Zuse on high order languages, first general purpose stored programs computer. ENIAC with plugboard programming, dominated by women, (Antonelli, Snyder, Spence, Teitelbaum, Wescoff). Towards the end of the war: Kilburn real-time (1948), Wilson and Gill subroutines (1949), Eckert and Mauchly with software as a thing of itself (1949). John Bacchus with imperative programming (Fortran, 1946), Goldstein and von Neumann flowcharts (1947). Commercial computers – Leo for a tea company in England. John Pinkerton creating operating system, Hoper with ALGOL and COBOL, reuse (Bener, Sammet). SAGE system important, command and control – Jay Forrester and Whirlwind 1951, Bob Evans (Sage, 1957), Strachey time sharing 1959, St Johnson with the first programming services company (1959).

Software crisis – not enough programmers around, machines more expensive than the humans, priesthood of programming, carry programs over and get results, batch. Fred Brooks on project management (1964), Constantin on modular programming (1968), Dijkstra on structured programming (1969). Formal systems (Hoare and Floyd) and provable programs; object orientation (Dahl and Nygaard, 1967). Main programming problem was complexity and productivity, hence software engineering (Margaret Hamilton) arguing that process should be managed.

Royce and the waterfall method (1970), Wirth on stepwise refinement, Parnas on information hiding, Liskov on abstract data types, Chen on entity-relationship modelling. First SW engineering methods: Ross, Constantine, Yourdon, Jackson, Demarco. Fagin on software inspection, Backus on functional programming, Lamport on distributed computing. Microcomputers made computing cheap – second generation of SW engineering: UML (Booch 1986), Rumbaugh, Jacobsen on use cases, standardization on UML in 1997, open source. Mellor, Yourdon, Worfs-Brock, Coad, Boehm, Basils, Cox, Mills, Humphrey (CMM), James Martin and John Zachman from the business side. Software engineering becomes a discipline with associations. Don Knuth (literate programming), Stallman on free software, Cooper on visual programming (visual basic).

Arpanet and Internet changed things again: Sutherland and SCRUM, Beck on eXtreme prorgamming, Fowler and refactoring, Royce on Rational Unified Process. Software architecture (Kruchten etc.), Reed Hastings (configuration management), Raymond on open source, Kaznik on outsourcing (first major contract between GE and India).

Mobile devices changed things again – Torvalds and git, Coplien and organiational patterns, Wing and computational thinking, Spolsky and stackoverflow, Robert Martin and clean code (2008). Consolidation into cloud: Shafer and Debois on devops (2008), context becoming important. Brad Cox and componentized structures, service-oriented architectures and APIs, Jeff Dean and platform computing, Jeff Bezos.

And here we are today: Ambient computing, systems are everywhere and surround us. Software-intensive systems are used all the time, trusted, and there we are. Computer science focused on physics and algorithms, software engineering on process, architecture, economics, organisation, HCI. SWEBOK first 2004, latest 2014, codification.

Mathematical -> Symbolic -> Personal -> Distributed & Connected -> Imagined Realities

Fundamentals -> Complexity -> HCI -> Scale -> Ethics and morals

Scale is important – risk and cost increases with size. Most SW development is like engineering a city, you have to change things in the presence of things that you can’t change and cannot change. AI changes things again – symbolic approaches and connectionist approaches, such as Deepmind. Still a lot we don’t know what to do – such as architecture for AI, little rigorous specification and testing. Orchestration of AI will change how we look at systems, teaching systems rather than programming them.

Fundamentals always apply: Abstraction, separation, responsibilities, simplicity. Process is iterative, incremental, continuous releases. Future: Orchestrating, architecture, edge/cloud, scale in the presence of untrusted components, dealing with the general product.

“Software is the invisible writing that whispers the stories of possibility to our hardware…” Software engineering allows us to build systems that are trusted.

Sources: https://twitter.com/Grady_Boochhttps://computingthehumanexperience.com/

Brilliance squared

Stephen Fry and Steven Pinker are two of the people I admire the most, for their erudition, extreme levels and variety of learning, and willingness to discuss their ideas. Having them both on stage at the same time, one interviewing the other (on the subject of Pinker’s last book, Enlightenment Now), is almost too much, but here they are:

(I did, for some reason, receive an invitation to this event, and would have gone there despite timing and expense if at all possible, but it was oversubscribed before I could clink the link. So thank whomever for Youtube, I say. It can be used to spread enlightenment, too.)

Interesting interview with Rodney Brooks

sawyer_and_baxterBoingboing, which is a fantastic source of interesting stuff to do during Easter vacation, has a long and fascinating interview by Rob Reid with Rodney Brooks, AI and robotics researcher and entrepreneur extraordinaire. Among the things I learned:

  • What the Baxter robot really does well – interacting with humans and not requiring 1/10 mm precision, especially when learning
  • There are not enough workers in manufacturing (even in China), most of the ones working there spend their time waiting for some expensive capital equipment to finish
  • The automation infrastructure is really old, still using PLCs that refresh and develop really slowly
  • Robots will be important in health care – preserving people’s dignity by allowing them to drive and stay at home longer by having robots that understand force and softness and can do things such as help people out of bed.
  • He has written an excellent 2018 list of dated predictions on the evolution of robotic and AI technologies, highly readable, especially his discussions on how to predict technologies and that we tend to forget the starting points. (And I will add his blog to my Newsblur list.)
  • He certainly doesn’t think much of the trolley problem, but has a great example to understand the issue of what AI can do, based on what Isaac Newton would think if he were transported to our time and given a smartphone – he would assume that it would be able to light a candle, for instance.

Worth a listen..

Beyond Default

71gkby-vpilDavid Trafford and Peter Boggis are those kinds of under-the-radar strategy consultants that ever so discreetly (and dare I say, in their inimitable British way) travel the world, advising enormous companies most civilians have never heard of about such issues as how to organise your internal departments so that they are capable of responding to technical change. (I should know, because I worked with them, first in CSC and then in the Concours Group, between 1994 and 2009.)

Now David and Peter have begot a book, Beyond Default, that provides a perspective on strategy and organisational change less built on fashionable frameworks than on solid experience. Their focus is on how organisations fail to see changes in their environment and develop strategies – real strategies – to adapt to them. The reasons are many, but most important is the fact that organisations have developed processes and measures to do what they currently do, and the focus on those particulars does not permit stepping back and seeing the bigger picture. Instead, companies carry on towards a “default” future – and, crucially, that future may be declining. Companies need to know what they don’t know and what they do not have the capabilities to do – and to acquire those capabilities when necessary. To do that, the authors advocate experiential learning – seeing for yourself what the future looks like by seeking it out, preferably as a group of managers from the same organisation experiencing and reflecting together.

The authors have a background as IT consultants, and it shows: They very much think of organisations as designed systems, with operating practices and (ideally) articulated operating principles. While eminently logical, this way of organising is hard to do – among other things, it requires thinking about organisations as tools for a purpose, and that purpose has to be articulated in a way that gives direction to its members. Thinking about your principles can make you articulate purpose, but it is very hard not to make the whole process a bit self-referential. Perhaps the key, like for Newton’s second law of thermodynamics, is to keep adding external energy, constantly identifying and understanding ramifications of technical and other change – a process that requires energy, if nothing else.

Both authors care about language and explaining and discussing what happens in a way that can be understood by the organisations they are trying to help. This means that they primarily use examples and stories, rather than frameworks (beyond simple illustrations), to convey their points. They end each chapter with a set of questions the reader can has him- or herself about the organisations they manage – and do not, in any way, try to offer simple solutions. As such, the book works best when it talks about how to explain strategic necessities and start on a strategic journey – through collective leadership, not “great man” charisma. It works less well when trying to explain strategic analysis, perhaps because the authors have too much experience to settle on a simple, all-encompassing method.

Well worth the read, not least for the senior executive trying to understand a new world and wanting an explanation held in a language that fosters understanding rather than just excitement.

Neural networks – explained

As mentioned here a few times, I teach an executive course called Analytics for strategic management, as well as a short program (three days) called Decisions from Data: Driving an Organization on Analytics. We have just finished the first version of both of these courses, and it has been a very enjoyable experience. The students (in both courses) have been interested and keen to learn, bringing relevant and interesting problems to the table, and we have managed do what it said on the tin (I think) – make them better consumers of analytics, capable of having a conversation with the analytics team, employing the right vocabulary and being able to ask more intelligent questions.

Of course, programs of this type does not allow you do dive deep into how things work, though we have been able to demonstrate MySQL, Python and DataRobot, and also give the students an understanding of how rapidly these things are evolving. We have talked about deep learning, for instance, but not how it works.

But that is easy to fix – almost everything about machine learning is available on Youtube and in other web channels, once you are into a little bit of the language. For instance, to understand how deep learning works, you can check out a series of videos from Grant Sanderson, who produces very good educational videos on the web site 3 blue one brown.

(There are follow-up videos: Chapter 2, Chapter 3, and Chapter 3 (formal calculus appendix). This Youtube channel has a lot of other math-related videos, too, including a great explanation of how Bitcoin works, which I’ll have to get into at some points, since I keep being asked why I don’t invest in Bitcoin all the time.)

Of course, you have to be rather interested to dive into this, and it certainly is not required read for an executive who only wants to be able to talk intelligently to the analytics team. But it is important (and a bit reassuring) to note the mechanisms employed: Breaking a very complex problem up into smaller problems, breaking those up into even smaller problems. solving the small problems by programming, then stepping back up. For those of you with high school math: It really isn’t that complicated. Just complicated in layers.

And it is good to know that all this advanced AI stuff really is rather basic math. Just applied in an increasingly complex way, really fast.

Analytics projects

asm_topTogether with Chandler Johnson and Alessandra Luzzi, I currently teach a course called Analytics for Strategic Management. In this course (now in its second iteration), executive students work on real projects for real companies, applying various forms of machine learning (big data, analytics, whatever you want to call it) to business problems. We have just finished the second of five modules, and the projects are now defined.

Here is a (mostly anonymised) list:

  • The Agency for Public Management and eGovernment (Difi) wants to understand and predict which citizens are likely to reserve themselves against electronic communications from the government. The presumption is that these people may be mostly old, not on electronic media, or in other ways digitally unsophisticated – but that may not be true, so they want to find out.
  • An electric power distribution company wants to investigate power imbalances in the electric grid: In the electric grid, production has to match consumption at all times, or you will get (sometimes rather large) price fluctuations. Can they predict when imbalances (more consumption that production, for instance) will occur, so that they can adjust accordingly?
  • A company in the food and beverage industry want to offer recommendations to their (business) customers: When you order products from them, how can they suggest other products that may either sell well or differentiate the customer from the competition?
  • A petroleum producing company wants to predict unintended shutdowns and slowdowns in their production infrastructure. Such problems are costly and risky, but predictions are difficult because they are rather rare – and that creates difficulties with unbalanced data sets.
  • A major bank wants to look into the security profiles of their online customers and investigate whether some customers are less likely to be exposed to security risks (and therefore may be able to use less cumbersome security procedures than others).
  • An insurance company wants to investigate which of their new customers are likely to leave them (churn analysis) – and why. They want to find them early, while there is still time to do something to make them stay.
  • A ship management company wants to investigate the use of certain types of oil and optimise the delivery and use of it. (Though the oil is rather specialised, the ships are large and the expense significant.)
  • Norsk Tipping runs a service helping people who are in danger of becoming addicted to gaming, an important part of their societal responsibility which they take very seriously. They want to identify which of their customers are most likely to benefit from intervention. This is a rather tricky and interesting problem – you need to identify not only those who are likely to become addicted, but also make a judgement as to whether the intervention (of which there is limited capacity) is likely to help.
  • A major health club chain wants to identify customers who are not happy with their services, and they want to find them early, so they can make offers to activate them and make them stay.
  • A regional bank wants to identify customers who are about to leave them, particularly those who want to move their mortgage somewhere else. (This is also a problem of unbalanced data sets, since most customers stay.)
  • A major electronic goods retailer wants to do market basket analysis to be able to recommend and stock products that customers are likely to buy together with others.

All in all, a fairly typical set of examples of the use of machine learning and analytics in business – and I certainly like to work with practical examples with very clearly defined benefits. Now – a small matter of implementation!

Concorde moment

british_airways_concorde_g-boac_03I recently searched for the term “Concorde moment” and did not find it. The term has appeared on Top Gear some years ago (though I can’t find the clip), probably mentioned by James May (who knows something about technology evolution) or Jeremy Clarkson (who certainly lamented the passing of the Concorde many times.) What “Concorde moment” means, essentially, is (as Clarkson says in the video below) “a giant step backward for mankind”.

The Concorde is still the fastest passenger jet ever made (3.5 hours from London to New York) and still the most beautiful one. In the end, it turned out to be too noisy, too polluting, and too expensive, never really making money. But it sure looked impressive. I never got to go on one, despite working in an international consulting company and jetting back and forth across the pond quite a bit. But my boss once bamboozled someone into bleeding for the ticket, and lived off the experience for a long time.

palm_graffiti_gesturesA Concorde moment, in other words, is a situation where a groundbreaking technology ceases to be, despite clearly being (and remaining) best in class, for reasons that seem hard to understand. Other examples may include

  • the Palm Pilot with its Graffiti shorthand system, once used by businesspeople all over the world (and by my wife to take impressive notes in all her studies)
  • the Apollo space program – we last went to the moon in 1972, with Apollo 17, and have not been back since. 45 years without going back has resulted in some impressive conspiracy theories, but again, the lack of any scientific or economic reason for going there is probably why it hasn’t happened.
  • the Bugatti Veyron, at least according to Top Gear. Personally, I find the announced new Tesla Roadster much more exciting, but, well, everyone is entitled to an opinion.
  • and, well, suggestions?

Spam on LinkedIn

Got this LinkedIn invite this morning. I don’t know, but somehow I think this is not legit. If not, I am sure the real Erling Persson Family Foundation will be in touch…

Inbox__48 093_messages_

So far, spammy invites on LinkedIn has largely been young women with very short CVs. This is new, and something LinkedIn should pay attention to, as the network is one where the signal-to-noise ratio is unusually high.

The nastiness of immigrant fear

This piece (http://crookedtimber.org/2017/10/23/working-to-rule/) by Maria (Farrell?) is a long and very insightful read about the emotional impact on immigrants from the Brexit debacle – and more generally, about the nastiness of reducing immigrants (or, for that matter, any foreigner) to a number and a category.

Anyone who thinks being an immigrant, even a deluxe EU three million-type immigrant, is easy, should try it. We compete on equal terms with all comers, but with no social or economic safety net and, for many, hustling like mad in second and third languages. No dole, no network of couches to sleep on, no contacts and no introductions; qualifications from institutions you’ve never heard of, references from employers you aren’t sure are real but can’t be bothered to check, acting as daily fodder for stereotypical jokes we laugh off to show we’re one of you. You don’t hear us complaining about it because it’s just part of the deal. But when the terms of the deal change, and you tell us we’re social welfare parasites who are also, somehow, taking all the jobs and are the reason the country is failing, then the deal is probably dead.

How anyone can think shutting yourself off from the world and fantasise about going back to a nonexistent 1960s idyll is in any way beneficial is beyond me. And this nastiness is not limited to Britain or Trump’s USA, far from it, Norway has its share of little people with big fears as well.

To get new ideas, increase the variety of sources, expose yourself to new experiences, and embrace that which you cannot understand.

Assuming you want new ideas, of course.

A tour de Fry of technology evolution

There are many things to say about Stephen Fry, but enough is to show this video, filmed at Nokia Bell Labs, explaining, amongst other things, the origin of microchips, the power of exponential growth, the adventure and consequences of performance and functionality evolution. I am beginning to think that “the apogee, the acme, the summit of human intelligence” might actually be Stephen himself:

(Of course, the most impressive feat is his easy banter on hard questions after the talk itself. Quotes like: “[and] who is to program any kind of moral [into computers ]… If [the computer] dives into the data lake and learns to swim, which is essentially what machine learning is, it’s just diving in and learning to swim, it may pick up some very unpleasant sewage.”)

Science fiction and the future

I am on the editorial board of ACM Ubiquity – and we are in the middle of a discussion of whether science fiction authors get things right or not, and whether science fiction is a useful predictor of the future. I must admit I am not a huge fan of science fiction – definitely not films, which tend to contain way too many scenes of people in tights staring at screens. But I do have some affinity for the more intellectual variety which tries to say something about our time by taking a single element of it and magnifying it.

So herewith, a list of technology-based science fiction short stories available on the web, a bit of fantasy in a world where worrying about the future impact of technology is becoming a public sport:

  • The machine stops by E. M. Forster is a classic about what happens when we make ourselves completely dependent on a (largely invisible) technology. Something to think about when you sit surfing and video conferencing  in your home office. First published in 1909, which is more than impressive.
  • The second variety by Philip K. Dick is about what happens when we develop self-organizing weapons systems – a future where warrior drones take over. Written as an extension of the cold war, but in a time where you can deliver a hand grenade with a drone bought for almost nothing at Amazon and remote-controlled wars initially may seem bloodless it behooves us to think ahead.
  • Jipi and the paranoid chip is a brilliant short story by Neal Stephenson – the only science-fiction author I read regularly (though much of what he writes is more historic/technothrillers than science fiction per se). The Jipi story is about what happens when technologies develop a sense of self and self preservation.
  • Captive audience by Ann Warren Griffith is perhaps not as well written as the others, but still: It is a about a society where we are not allowed not to watch commercials. And that should be scary enough for anyone bone tired of alle the intrusive ads popping up everywhere we go.

There is another one I would have liked to have on the list, but I can’t remember the title or the author. It is about a man living in a world where durable products are not allowed – everything breaks down after a certain time so that the economy is maintained because everyone has to buy new things all the time. The man is trying to smuggle home a wooden garden bench made for his wife out of materials that won’t degrade, but has trouble with a crumbling infrastructure and the wrapping paper dissolving unless he gets home soon enough…

Big Data and analytics – briefly

DFDDODData and data analytics is becoming more and more important for companies and organizations. Are you wondering what data and data science might do for your company? Welcome to a three-day ESP (Executive Short Program) called Decisions from Data: Driving an Organization with Analytics. It will take place at BI Norwegian Business School from December 5-7 this year. The short course is an offshoot from our very popular executive programs Analytics for Strategic Management, which are fully booked. (Check this list (Norwegian) for a sense of what those students are doing.)

Decisions from Data is aimed at managers who are curious about Big Data and data science and wants an introduction and an overview, without having to take a full course. We will talk about and show various forms of data analysis, discuss the most important obstacles to becoming a data driven organization and how to deal with data scientists, and, of course, give lots of examples of how to compete with analytics. The course will not be tech heavy, but we will look at and touch a few tools, just to get an idea of what we are asking those data scientists to do.

The whole thing will be in English, because, well, the (in my humble opinion) best people we have on this (Chandler Johnson og Alessandra Luzzi) are from the USA and Italy, respectively. As for myself, I tag along as best I can…

Welcome to the data revolution – it start’s here!

Recommended: appear.in

Premium_–_appear_in_–_one_click_video_conversationsTelenor has made a video conferencing service called appear.in (Twitter: @appear_in) – and it is fantastic! All you need to do is open a browser window and type

appear.in/something

where “something” is a word you choose. The other participants do the same, and you are in conference. Camera, screen sharing, everything work great, the whole thing is free (at least with up to four participants, have not tested with more). If you want your own room with your own design there is a premium version for $12 per month. No app installation, no weird settings, no drivers, no updates. Just works. Excellent!

Recommended!

(No, I am not sponsored. Just like the service.)