Away with M.E. Porter! Data is revolutionizing Business Strategy.

6

October

2016

4.67/5 (6)

Michael Porter, professor at Havard Business School, is of course a widely acknowledged business strategist. Not rarely did we come across his philosophies in our books, is he cited in studies or are whole businesses based on his theories. In this blog I am going to argue that the assumptions on which these theories are based are no longer valid and that a new way of thinking about business is in place.

Let us start with a little history about business strategy. One of the first accepted business strategy theories came from Bruce Henderson, the founder of BCG. He stated that by concentrating a large mass against a weakness a competitive advantage could be achieved. Basically by overwhelming the ‘enemy’ while making use of economies of scale. It was the introduction of incorporating military strategies into doing business. Porter partially agreed to this theory and improved it by adding the idea of the value chain. Companies could not be seen as one entity but as a sequence of steps, getting from a raw material to a finished product. So while trying to create a large mass, the efficiency and costs of these steps where critical in achieving a competitive advantage.

One of the biggest aspects of a value chain are the transaction costs, consisting mainly of the costs of processing information and the costs of communication. As we all know, the costs of both aspects have tremendously decreased since the increase in computing power and the rise of the internet. Because of this change it is more difficult to achieve a competitive advantage within a companies’ value chain. Companies started to break up the value chain or started to ‘attack’ other companies at certain steps within their value chain. For example, the way encyclopedias were sold. Most of the costs went to the salesman, but when the CD-ROM and the internet entered our world it became much cheaper to sell and distribute them. An even more interesting change in the value chain of encyclopedias is actually the way they are produced. When we entered the Web. 2.0 era, it turned out that thousands of people could make the whole production layer of the value chain of conventional encyclopedia’s obsolete. Obviously, I am referring to Wikipedia.  Screenshot 2016-10-06 16.44.11

Figure 1: Value chains become dynamic.

Screenshot 2016-10-06 16.45.33

Figure 2: Phases of the ‘digital revolution’

Right now, as we are entering the third information era Web 3.0, another revision of business strategy is needed. More and more devices are connected to the internet and all these devices gather data. This results in an enormous growth of the amount of data that is available now. When studied in combination with data from different devices and places, new patterns and discoveries are waiting for us. To get an idea of the impact this could have, you can think of for example the mapping of a human genome. In the year 2000 the first human genome was mapped and it took researchers 200 million dollar and ten years to do. The cost of doing this in 2017 is expected to drop below $100, – in a fraction of the time, which opens up the possibility for this technology to go commercial. Every doctor will map your genome to take lessons from it. When all this genome data is combined with the information from devices like medical sensors in hospitals or our phones, opportunities open up to do discoveries and find patterns that are unprecedented.Screenshot 2016-10-06 16.47.00

Figure 3. World’s stock of data

But, there is a problem here. Different institutions, corporations and organizations need to access each other’s information in order to leverage the potential of all this data and create new businesses from it. However, for many organizations today, data is where companies are still getting their competitive advantage from. It looks like this unstoppable improvement of technology is driving the way we think about doing business away, and with that the conventional way in which business strategy is formulated.

Transaction costs are diminishing that essentially held businesses together, value chains are changing drastically like in the Wikipedia example and it looks like leveraging the potential of sharing information across companies and institutions is becoming the new way of getting a competitive advantage. I have no clue to what form of doing business we are heading, but I do know that we live in a different time then when Porter formulated his theory and we are going to have a very exciting transformation.

 

Joep Beliën

 

This blog is a summary and own interpretation of a TED talk of Philip Evans: How data will transform business.

National Human Genome Research Institute (NHGRI). (2016). DNA Sequencing Costs: Data. [online] Available at: https://www.genome.gov/sequencingcostsdata/ [Accessed 6 Oct. 2016].

SEIER CAPITAL. (2016). How Big Data Could Change Your Business Strategy – SEIER CAPITAL. [online] Available at: http://www.seiercapital.com/disruptive-business-how-big-data-could-change-your-business-strategy/ [Accessed 6 Oct. 2016].

McGuire, T., Manyika, J. and Chui, M. (2016). Why Big Data is the new competitive advantage •. [online] Iveybusinessjournal.com. Available at: http://iveybusinessjournal.com/publication/why-big-data-is-the-new-competitive-advantage/ [Accessed 6 Oct. 2016].

TED, (2015). How Data Will Transform Business. [ video] Available at: http://www.ted.com/talks/philip_evans_how_data_will_transform_business [Accessed 5 Oct. 2016].

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Is Artificial Intelligence Making Art?!

5

October

2016

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So, you’ve decided to read a blog about artificial intelligence making art, one of the activities that is considered impossible for computers to do due to the necessity of certain human cognitive traits that we have yet to understand ourselves? Well, without discussing the definition of art too much, I would like to tell you about the rapidly succeeding developments in the world of AI and how its applications are surprising scientists as they are becoming less dependent on human input and doing unprecedented complex tasks.

funny-animals (1)

To understand how pictures like the above are created with AI, we need to understand how artificial neural networks work.

Artificial neural networks make use of ‘nodes’ and are based on biological neural networks like you and I have. It is a hierarchy of these nodes and each node completes a very specific simple task, i.e. recognizing patterns and ‘firing’ a signal to a node higher up in the hierarchy when it does. For example, one node is specialized in recognizing the slash ( / ), for example in the letter A ( /-\ ). Another node is specialized in recognizing the backslash ( \ ), and when a node higher up in the hierarchy receives a signal of the slash,  ( / ), backslash ( \ ), and dash ( – ) nodes, it recognizes the letter A. In the same way, other letters are recognized and a few levels up in the hierarchy the nodes “Apple” or “cAr” are activated, depending on the other signals. The higher in the hierarchy, the more abstract these nodes become as the combination of more complex patterns increase.

Neuron3

The above is called deep learning and is part of the family of machine learning methods. These neural networks start ‘empty’ and are fed with incredible amounts of data, for example the whole google images catalog of cat pictures. Without supervision of you or me the program learns itself to distinguish a cat from a picture with a cat and a dog in it. Recognizing if a cat is a cat and not a dog is an example of a task that is effortless for humans, but has been extremely difficult for a piece of software to do.

Artificial intelligence is getting smarter. Not only are they telling us which movie to watch or what music to listen, recently there were AI programs that compiled a song, made a movie trailer, wrote a book, defeated the world champion in the Chinese game GO and won the TV show Jeopardy (the last two need a story of their own).

This brings us to the art that AI has been creating since a year. Researchers at Google realized that, after letting a artificial neural network learn, they could reverse the process. So instead of giving the program an image and asking what was on the image, they gave the program so called ‘white noise’, i.e. no object, and asked the program to create a picture of what it saw. As a result, the program started to look for patterns and created images of objects it ‘thought’ it saw, ending in images like these (there is a link behind the image with more of these).

Iterative_Places205-GoogLeNet_3 Iterative_Places205-GoogLeNet_4 Iterative_Places205-GoogLeNet_18iterative-lowlevel-feature-layer

Some people took it further and programmed the program to zoom into the picture it made, resulting in an infinite source of new patterns and new objects to create.

Deep_Dreaming_into_noise_with_inceptionism

AI is getting smarter as not only computing power but also techniques are improving. Researchers are getting unexpected output like the animated gif above and are surprised by the effectiveness of neural networks.

Although I think this is art, there are discussions on whether AI will ever succeed in human tasks like creating art. What do you think? Share your thoughts!

 

Joep Beliën

 

 

 

Wikipedia. (2016). Artificial neural network. [online] Available at: https://en.wikipedia.org/wiki/Artificial_neural_network [Accessed 4 Oct. 2016].

Newsweek. (2016). Can an artificially intelligent computer make art?. [online] Available at: http://europe.newsweek.com/can-artificially-intelligent-computer-make-art-462847?rm=eu [Accessed 4 Oct. 2016].

 Casey, M. and Rockmore, D. (2016). Looking for art in artificial intelligence. [online] Phys.org. Available at: http://phys.org/news/2016-05-art-artificial-intelligence.html [Accessed 4 Oct. 2016].

Wikipedia. (2016). Deep learning. [online] Available at: https://en.wikipedia.org/wiki/Deep_learning#Deep_neural_network_architectures [Accessed 4 Oct. 2016].

Furness, D. (2016). Google’s newly launched Magenta Project aims to create art with artificial intelligence. [online] Digital Trends. Available at: http://www.digitaltrends.com/cool-tech/ai-art-google-magenta-project/ [Accessed 4 Oct. 2016].

IFLScience. (2016). Google’s AI Can Dream, and Here’s What it Looks Like. [online] Available at: http://www.iflscience.com/technology/artificial-intelligence-dreams/ [Accessed 4 Oct. 2016].

Wikipedia. (2016). Jeopardy!. [online] Available at: https://en.wikipedia.org/wiki/Jeopardy! [Accessed 4 Oct. 2016].

Mordvintseev, A., Olah, C. and Tyka, M. (2015). Inceptionism: Going Deeper into Neural Networks. [online] Research Blog. Available at: https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html [Accessed 4 Oct. 2016].

PBS Idea Channel, (2016). Can an Artificial Intelligence Create Art?. video] Available at: https://www.youtube.com/watch?v=Sbd4NX95Ysc [Accessed 4 Oct. 2016].

 

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Technology of the Week – The Disruption of E-books

23

September

2016

5/5 (1)

 

This week’s technology is all about Information goods. We have looked into the e-books market, linked it to education, predicted the future and made a video with theories, models, an interview and sweet animations. Check it out here

 

Publishing is the activity of making information available to the general public. In the 15th century, the art of publishing took a huge leap due to the invention of the book press. From that time onwards it became easier and cheaper to print multiple copies of a book and information became more widely available to the public. Today, the printing of books is making way for a disruptive innovation called electronic books. Hardware devices for reading e-books were introduced to the market in 2004 and the sales of e-books are starting to overtake the sales of hardcopy books.

 

To analyse the market of e-books and the actors that are playing a role, we use Porters five forces model. Due to the ease of publishing your own work with for example Amazon’s Kindle Direct Publishing the threat of new entrants is very high. The intensity of rivalry is also high as many big companies compete with each other on prices. The threat of substitutes is high due to hard copy books. The bargaining power of suppliers is low because authors can publish their work directly on a platform (retailer). Lastly, the bargaining power of buyers is high as they can easily switch between the various platforms.

 

A SWOT analysis will now give us an idea of what the future may bring. Strengths for the e-books market lie in innovative features that offer new ways to interact and engage with content. Furthermore, the technology improves the cost-effectiveness, ac
cessibility and disadvantages. However, the technology is not without drawbacks. As a weakness you can think of licensing and copyright issues, as well as implementing the necessary IT-infrastructure. Opportunities for this market include increasing collaboration, enriching distance education or content and the increase in the interconnectedness of people. Threats are the existing culture around hardcopy books, the lack of viable business models, high tax-rates on e-books and the absence of awareness.

 

swot

The market depends on quite a lot of factors in determining how strong the market will grow in the upcoming years, including the willingness of people to adopt to this technology and the VAT on e-books.

 

To conclude, we actually believe that the book market will definitely change and that eventually everyone will read e-books. That does not mean that printed books will totally disappear, but at least everyone will own a device that allows you to read e-books and will be used to reading from screens. We see a lot of old-fashioned thinking in the industry, but with new players knocking on the door it is only a matter of time till the market is disrupted.
The educational system we live in will definitely change, and e-books and online education will play a huge role in that.

We want to thank Farshida Zafar of the EUR and Floor Theunissen of STAR for their cooperation. Group 38.

Daniel Bos
Joep Beliën
Robbert Brouwer
Stijn Oude Elferink

Reference list:

Reference list:
Gardiner, Eileen and Ronald G. Musto., The Electronic Book,  Suarez, Michael Felix, and H. R. Woudhuysen. The Oxford Companion to the Book. Oxford: Oxford University Press, 2010, p. 164.

PwC (2014), Media Trend Outlook: E-books on the Rise

Chalmers, T. (2015), 10 Industry Predicitions for 2016 [online], Available at: http://www.digitalbookworld.com/2015/10-industry-predictions-for-2016/ . (19 September 2016)

The printing press, Wikipedia, 6 September 2016, Available at: https://en.wikipedia.org/wiki/Printing_press (20 September 2016).

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