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Big Data needs a human network

By David Weinberger, senior researcher, Berkman Center for Internet & Society, Harvard University.

Big Data certainly makes smarter the fields of science and scholarship that use it. But does it make society smarter? For that we need to look at the network that connects our giant computing machines to data, to other machines, and to society.

If we had today’s stupendously powerful computers but no network connecting them, we could still do some forms of Big Data analysis. But real-time analysis of data would be severely hampered, and the vital work being done on data coming from the worldwide arrays of sensors would be all but impossible.

« For data and its analysis to have an effect — to make society smarter — it must be absorbed by humans in very human ways »

Once the data is collected and analyzed, the network enables a computer modeling one aspect of the world to connect with computers modeling other aspects. For example, what might be the effects of climate change on the spread of infectious disease? The networking of Big Data holds open the hope of creating a de facto commons in which models can affect one another and learn from one another. (At its best, this commons will always be messy and contentious. Or so we can hope.)

The network also connects people to the data, analysis, models, and hypotheses that emerge. Remarkably, that very same network also connects us to one another. For data and its analysis to have an effect — to make society smarter — it must be absorbed by humans in very human ways: by talking, arguing, getting things wrong, trying ideas out, laughing.

There is no direct path from data to societal smartness. But because what connects data, our machines, and us humans is all one network, there is a path.

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About the Question
Are Big Data & Analytics shaping a smarter society?

Every day we generate a huge amount of big data, but we need to resort to analytics to make abstract information meaningful and get valuable knowledge from it. In education, learning platforms let us easily gather an immense quantity of data regarding students’ behaviour, interactions, preferences and opinions. When properly analysed — through learning analytics — all these data might provide useful insight on how to make learning processes more adaptive, attractive and efficient.

Are these techniques allowing us to provide better support to our students? Are we taking advantage of big data and analytics to help shape the citizens of the future?

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