Guest blog: Why shifting baseline impacts big data analysis

By Carsten Schmidt, Fellow, Henry Corporation

Carsten Schmidt, Fellow, Henry Corporations

Carsten Schmidt

In a 1969 manifesto titled “Design with Nature," a landscape architect named Ian McHarg introduced a concept called the shifting baseline.

The concept essentially describes a form of “generational blindness,” which means that our experiences and immediate views limit our perception of reality. Otherwise stated, our reference point for any given analysis or perspective determines our initial approach and the final result.

Let’s imagine for a moment that data in itself is merely a mirage of wisdom. Whether we apply our knowledge of history and engage our ability to think and consider are ways we can be more wise.

To think beyond our immediate knowledge; to change reference points; to include unstructured data; and to consider for a moment what we may not know…That is to imagine! And that is what fosters great ideas.

From a business perspective, accumulated big data (i.e. measurable, historic data) until recently has been comprised of structured digital trails—proliferation of so-called unstructured data generated by all our digital social interactions—and of analogue workflows.

However, in order to extract knowledge from big data, we may also need to emphasize what has not happened: we should find ways to query the absence and to think beyond our baseline.

Combining what we know with what we do not know can lead to disruptive business models. Let me give you two examples:

1.
When Apple launched the iPhone 3 in 2007, this represented an entirely new approach to mobile phone design. Around the same time, companies such as Motorola, Ericsson and Nokia were spending billions of dollars and euros on mobile phone analysis and design. During this process, none of these companies envisioned the idea—which was then introduced by Apple—of the user interface, the soon to be the de-facto standard of mobile phones. At this point in time, the baseline for analysis simply could not anticipate the concept of a touch screen.

2.
My local butcher excels at hanging meat, but he also sells an assortment of red wine. Lately, he has expanded his collection of red wine so that this supply fills approximately 1/3 of the display area where customers wait in line to be served. Perhaps an ethnographic field study would indicate the sensibility of selling wine in a butcher’s shop, but the linkage to consumer demand otherwise seems obvious. But butchers normally do not study ethnography.

Primarily it is useful to collect and to study both manmade and computer generated (IoT) digital trails, but I believe that big data analysis should include something more. Beyond digital trails, big data analysis should include a comprehensive knowledge of human attitudes and behavior. Information about social and cultural anthropology is ripe for in-depth analysis if we use the appropriate analytics software.

In the pursuit of innovative progress, this kind of knowledge complements digital trails. If we engage information from a variety of sources, then we enable ourselves to see beyond our own baseline of knowledge while we register such historical data. And that, to me, is big data analysis.

Recent studies by Henry Corporation indicates that employment of business analysts are on the rise. This profession, however, is only beginning to find its own feet because the industry is changing and adapting to a new world order. Rather than question whether something can be measured, we should contemplate the purpose or reason that we measure and gauge which historical parameter is relevant to apply.

I have no doubt that technology firms can keep up with the digitalisation of processes to provide increasingly advanced tools for analysing and managing our businesses. However, I wonder whether we possess sufficient experience and wisdom, not only to technically operate such analytical software but to something more basic—to ask the right questions and to extract the real value from historic data? And do we educate our college and university students to be able to incorporate historical perspectives, softer behavior, and attitude data into the analysis of our businesses?

 

tags: analytics, Big Data

2 Comments

  1. Morten Bekholm
    Posted 27/03/2014 at 1:25 pm | Permalink

    An excellent contribution to the discussion of what big data and analytics can be used for. However, many innovations are the results of incremental improvements and inspiration e.g. from parallel industries and first movers. The most important source for creating paradigm changing innovation are the users, the customers and buyers - observing needs and collecting requirements as qualitative and quantitative insights. That is where you will find the business and market analysts asking (the right) questions.

  2. Posted 15/08/2014 at 11:53 pm | Permalink

    Rebound relationships most of the times fail for several
    reasons however there are always two sides to a story.
    Imagine the possibility behind the power of location based marketing on world’s largest social network.
    There are numerous social networking sites which offer some
    great features which are unique to them so that the people can really enjoy themselves and come back
    to them again and again.

Post a Comment

Your email is never published nor shared. Required fields are marked *

*
*

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <p> <pre lang="" line="" escaped="" highlight=""> <q cite=""> <strike> <strong>