Schlagwort-Archive: Channels

The digital on the Business Model Canvas

With the Z1 of Konrad Zuse in the early forties of the last century, the first electronic computer was created. However, the fifth Kondratiev that announced the information technology as a disruptive change in the economy and society started not before the 1970s. The access to the global network is possible anytime, anywhere with mobile devices of all kinds. Together with the gigantic computing power of today’s computers, a big wave to implement well-known concepts is being piled up: the automation of processes, embedded systems in all moving and unmoved objects, data management in the cloud as well as the processing of unimaginably huge amounts of data with Big Data. But what do all these approaches mean for the commercial Canvas ?

Every business can be depicted on the Business Model Canvas. In the following bullet points, the momentum of the digital transformation is considered.

  • Customer
    Already in the course of e-business more than twenty years ago, three customer areas were identified: business, consumer and government. Business describes the commercial enterprises, consumer the private customers and government the state/public institutions. In the beginning ventures should explore the digital reality of their own clientele. How digital are the customer areas? Where and when are the customers active? What do the customers want?
  • Customer relationships
    E-business already elaborated the possible customer relationships at an early stage: namely, all possible combinations of the axes business, consumer and government (i.e. B2B, B2C, B2G, C2B, C2C, C2G, G2B, G2C, and G2G). For most fields famous examples are available, like Amazon (B2C), eBay (C2C). A look at one’s own relationship structures and the degree of its digitization provides initial starting points for the digital transformation. What relationships exist or are possible? How, when and where does the customer wants to get in contact with the provider? What kind of digitization is needed?
  • Channels
    The path through which the participants get in contact with each other is determined by the previous routine. These channels range from personal visits, telephone calls, publications, trade shows, to the Internet. It is an advantage to use all possible channels. Which media is already used? Which channels should be developed?
  • Propositions
    The proposals are split into two groups. 1) Physical products and on-site services as well as 2) digitizable products and remote services. While the second group comprises purely digital propositions, the first group can be extended with digital building blocks, e.g. the remote maintenance of a machine, the 24-hour hotline, online training. The digital opportunities of the propositions are often not clear. Which parts of the assortment can be digitized? Which new digital services fit into the product range?
  • Revenue streams
    In addition to the core business, the accumulated knowledge and contacts provide additional sources of revenue. The digital transformation opens up these information-rich opportunities. Which digital sources of income are available in the field of the propositions? What else can one earn with the existing knowledge? What can you do with the contacts?
  • Activities
    It is always surprising how far or how little the internal possibilities of the IT are used. Thus, value creation continues to take place with traditional, paper-based practices. At the latest when the customers are no longer satisfied with the slow, manual processes and if a closer involvement is desired, nothing else remains but adapting digitally. The affected activities can be derived from the digitizable proposals. Which processes are partly or fully automatable? How does the transformation take place?
  • Resources
    Digital companies have virtual resources, i.e. the IT with its networks. A look at the degree of internal digitization, the already automated processes, the data landscape and the applications quickly shows the need for action. Which processes are already IT-based? What data is available? Which applications have a digital future?
  • Partners
    The participants in the provision of deliverables are the internal and external co-workers. They need new skills in the digital world, such as strong customer focus, lifelong learning, teamwork, change management as well as IT-specific knowledge of computer literacy, data security, data analysis, the Internet, etc. Who are the internal and external partners? Which skill profiles are there or are required? Which skills are missing?
  • Cost structure
    The digital transformation is not free of charge. The savings in expenditure and the increases in sales can not be realized overnight. Looking at Amazon, sales are growing steadily, but profits are not developing in the same way. Before you start with the digital transformation, you need an honest commitment concerning the costs. Where do the expenses arise? How long may the digital transformation take? What is the cost of not digitizing?

Bottom line: The complete Business Model Canvas is affected by the digital transformation. The already achieved digital penetration and readiness of all components determine the expected effort. The first step into the digital future is the conscious decision of all involved people for the necessary efforts. The Business Model Canvas provides the required overview.

Water – the ideal metaphor for data

Heraclitus created with Panta rhei (Greek: Everything flows) the bases for a new world view more than two and a half thousand years ago. You cannot step twice into the same stream. The simple insight that results is that everything is in permanent coming and going. It is at first sight always water that flows in the river – however always different one. The same happens with our current, virtual flows that are filled with data. That makes water to the ideal metaphor for data.

Let’s look at some characteristics of water and data.

  • Physical state
    Water can be found in three conditions: solid, liquid and gas. The melting point is the transition from solid to liquid and the boiling point between liquid and gas. Data takes shape the same way. As long as nothing can be expressed as zeros and ones, because they were not yet determined or expressed, there is nothing that can flow – like ice. Data reaches its melting point, as soon as someone expresses its thoughts in the form of language, pictures or sound, or after a sensor provided measured data. Now the data can flow – disseminated, exchanged or received. If the temperature rises further, then it reaches sometime the boiling point. The data becomes fuzzy – like steam. The sensors produce in this case an indefinite noise that cannot be captured in the virtual space.
    In order to receive useful data, it, as water, has to be converted into liquid condition. Either you have to heat it, so that it becomes visible – by measuring or questioning. Or you must cool it in order to consolidate it into to processible data.
  • Quality
    Let us limit ourselves to the simple distinction between pure, in the sense of potable, versus contaminated, in the sense of poisonous, water. We differentiate between objective and false data. In general we believe in pure data, if it originates from trusted sources. Obviously contaminated data is supplied by sources that are suspicious – research results, because they were created by a biased source; news, because they were published by a politically depended press.
    Unfortunately the quality is often based on an evaluation by third parties that is doubtful – if for example one press agency accuses another to be politically manipulated. On the other hand, contaminated data is used – even more than you can drink water that is poisoned to a certain degree.
  • Channels
    The water is continually running in a circular flow – water evaporates above the sea, rains down on the mountains, and produces creeks, rivers and streams, which sometimes flow over water falls back into the sea. The sea of data exists in the public and the protected virtual area. Clouds are formed in the world Wide Web with the Internet of Things that are collected in programs, are mixed up with new data in the processes, and are getting back through interfaces, like water falls, into the cyberspace.
    With Cloud computing, we are approaching more and more this state – even, if still many do not entrust their data to the cloud, due to strong concerns about the security. These internal dams offer no more protection on a long-term basis, since all data has to flow from time to time through the cyberspace.
  • Dangers
    An obvious danger comes from very strong contaminated water that poisons the users within shortest time. In the last years also the unimaginable power is shown to us by water floods, tsunamis, or dam failures after strong rainfalls. We quickly forget that no water represents a still worse threat – above all, if countries mutually cut water resources, as you can see at the distribution problems in the West Bank, at the dams of the Mekong in China or at the Aral Sea. The spreading of false information contaminates likewise the attitudes and insights of the audience. At the same time more and more data is flooded through the Internet. The attempt to sort this data leads on the one hand to filter bubbles that fade out a large part of the data. On the other hand the filters provide the possibility to manipulate the public opinion by subtly filtering critical contents, like for example the censorship of the embedded journalism. And eventually the lack of data results in dangerous misjudgments.

Different thinkers have taken care of the question “How real is the reality?” But nevertheless many people still argue with categories like “Right” and “Wrong”. The truth lies as always in the eye of the beholder. With the new conflagration of propaganda, we probably have to live with the fact that new terms, as “post factual” or „alternative facts” are trying to hide this dilemma. We should not forget that some people are not shy to sell good and bad data and unsuspecting people are consuming them without questioning – like water.

Bottom line: Data behaves obviously like water – there are similar physical states, qualities, channels and dangers. Data flows, data streams and data overloads can be controlled by particularly created riverbeds and dams as well as by filters. Thus, water is the ideal metaphor for data.