Winning the Hardware Software Game Winning the Hardware-Software Game - 2nd Edition

Using Game Theory to Optimize the Pace of New Technology Adoption
  • How do you encourage speedier adoption of your product or service?
  • How do you increase the value your product or service creates for your customers?
  • How do you extract more of the value created by your product or service for yourself?


big data

  • A Data-Generating System: A Framework for Data Assessment

    Suppose I gave you, the Data Analyst, a dataset of information on sales of Ford automobiles, and suppose I told you to use that dataset to predict total national sales of Ford automobiles for next 12 months. What would you want to know about the data you were given?

    If you were given data with information on past sales of Ford automobiles, and if you wanted to use that information to come up with a forecast of future sales, you would want to know, for example,

    • Which regions of the country were included in the data,
    • Which models of vehicles were included,
    • Which time periods of sales were included, and
    • Which regions, models, and time periods were not included in the data for which there were Ford sales. That is, which sales data are missing from your dataset.

    More generally, you would want to know where the data came from, which information the data include, and which information you need to make your forecasts are missing from the data. To do a good job in forecasting future sales, you would also want to know such things as:

    • Was the economy strong or weak when the data were collected? If the economy was strong in the past but it is expected to be weak in the future, then you would expect sales to decrease.
    • You would want to know if Ford automobiles were easily accessible to customers. That is, for people who wanted to buy Ford cars, were there Ford dealerships close by and with vehicles in stock? If Ford cars are expected to become more readily available in the future, then you would expect sales to increase.
    • You would want to know if people who wanted to buy a car had access to other brands of similar cars. If other brands of cars are increasingly available, then that might decrease future demand for sales of Ford’s automobiles.

    In other words, you want to know about the environment in which the data you have were generated and how that environment might differ from the environment you expect in the future, during the time your sales predictions will take place. You must align (your data and your analysis) between (the environment you have and the environment you want to predict).

    A data-generating system is a concept I created to describe the process and environment from which data are generated. Understanding this system tells you, for example, which data were generated, how, when, where, and why the data were generated, and other factors affecting what the data look like

    A typical data-generating system looks like this:

    data system

    There are five main components of a data-generating system

    1. The Content Provider provides content to Customers
    2. The Data Collectorcollects data from Customers. The Data Collector decides which information to collect, how to collect it, and from whom.
    3. The Customer “provides” or “generates” data, for example, by buying products, consuming content, or providing feedback.
    4. The Client or Data Analystuses data collected by the Data Collector from the Customer to perform analyses.
    5. The Context and Environment are all the factors in the background or setting that affect the information that ends up being collected by the Data Collector from the Customer.
  • Understanding the Evolution of IoT and What Will Be Important for Success

    The IoT Ecosystem Contains a Vast Array Of Components

    The Potential Value of Iot Will Increase Exponentially Over Time

    Barriers Are Currently Impeding Adoption of Iot

    How the Evolution of Iot Will Proceed

    Why Be an Early Adopter?

    What Will Be Important for Success in Iot?



    Vasyl Mylko of SoftServe notes that the Internet of Things is emerging at the intersection of Semiconductors, Telecommunications, and Big Data, through the evolution of their respective laws (see Figure 1)

    • Moore’s Law observes that semiconductors have been achieving a 60% increase in computer power every year.
    • Nielsen’s Law observes that Internet bandwidth has been achieving a 50% increase in speed every year.
    • Metcalfe’s Lawobserves that telecommunications networks increase in value with the square of the number of nodes
    • Law of Large Numbersobserves that the average obtained from a set of data approaches the true value as the size of the dataset increases.

    Charles McLellan, in “The internet of things and big data: Unlocking the power,” describes more directly how the confluence of trends inspired by these laws is enabling the rise of IoT:

    A huge number of 'things' could join the IoT, whose recent rise to prominence is the result of several trends conspiring to cause a tipping point: low-cost, low-power sensor technology; widespread wireless connectivity; huge amounts of available and affordable (largely cloud- based) storage and compute power; and plenty of internet addresses to go round, courtesy of the IPv6 protocol…


    Figure 1

    1 iot intersection

  • Why Is So Difficult to Extract Value from Data?

    This is a new idea I'm working on. I'd love to hear any feedback you might have.


    We Collect and Analyze Data

    Why do we collect and analyze data? It informs us about (i) what happened in a given time and place and (ii) why (see Figure 1).