Wednesday, 20 August 2014

From (inter)objective Big Data to (inter)subjective Deep Data

Big Data can have relevance to economics, and society. Blog Ref

photo of Michel Bauwens
Michel Bauwens/ P2P Foundation Blog

16th August 2014

The real problem of big data is that we are increasingly outsourcing our capacity to sense and think to algorithms programmed into machines. While this seems very convenient and cool at first and offers access to services that many of us want, it also raises a question about who actually owns big data, about the rights of individuals and citizens to own their personal data and to exercise choices regarding its use.

Excerpted from Otto Scharmer:

“While big data has certainly opened up a whole new range of possibilities, I would like to suggest a distinction between surface big data and deep data. Surface data is just data about others: what others do and say. That is what almost all current big data is composed of.
Deep data is used to make people and communities see themselves. Deep data functions like a mirror: it makes you see yourself–both as an individual and as a community. Over the past twenty years of my professional life I have been helping teams and organizations go through processes of profound innovation and transformative change across sectors and cultures. The one thing that I have learned from all these projects is that the key to transformative change is to make the system see itself. That’s why deep data matters. It matters to the future of our institutions, our societies, and our planet.
But what happens today with big data often is the opposite: big data is used to manipulate our behavior, to bombard us with commercials that we never asked for. Surface big data is used to outsource human thinking to algorithms, to reduce our level of awareness inside old patterns of habitual thought. Deep data, if developed and cultivated in the right way, could help us to enhance the level of awareness and consciousness and to change the system by shifting the consciousness of stakeholders in that system from ego-system awareness(awareness of my own silo) to eco-system awareness (awareness of the whole).

On a societal level, what types of deep data infrastructures might facilitate this bending of the beam of observation back onto the observer on the level of entire eco-systems?
For example, today we use GDP to measure economic progress. GDP is an excellent measure of surface data. But what would the equivalent deep data tool be for measuring real economic progress in a community? I believe that it would include a new indicator system that is grounded in real outcomes (like life expectancy), and in the wellbeing of individuals and their communities (like quality of life). Last year we–the Presencing Institute, with the GIZ Global Leadership Academy (German Ministry for Development Cooperation) and the Gross National Happiness Centre in Bhutan– launched the Global Wellbeing Lab, The lab links leaders from government, business, and civil society around the world who are working to pioneer new indicators and deep data tools that help communities and eco-systems begin to see themselves.
Where are you seeing the seeds of such new indicactor systems or deep data tools today? What can be learned from these first living examples? What would deep data mean for your self? What are the sources of well-being and happiness in your own life and work and what metrics could help you to see yourself in a more meaningful way? How can we co-pioneer theshift from big data to deep data in society today?”

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