Data, data everywhere

I came across this quote from David Rowan, editor of Wired UK on a website I visited today:

Each day, according to IBM, we collectively generate 2.5 quintillion bytes — a tsunami of structured and unstructured data that’s growing, in IDC’s reckoning, at 60 per cent a year. Walmart drags a million hourly retail transactions into a database that long ago passed 2.5 petabytes; Facebook processes 2.5 billion pieces of content and 500 terabytes of data each day; and Google, whose YouTube division alone gains 72 hours of new video every minute, accumulates 24 petabytes of data in a single day. . . . Certainly there are vast public benefits in the smart processing of these zetta- and yottabytes of previously unconstrained zeroes and ones. . . .

Yet as our lives are swept unstoppably into the data-driven world, such benefits are being denied to a fast-emerging data underclass. Any citizen lacking a basic understanding of, and at least minimal access to, the new algorithmic tools will increasingly be disadvantaged in vast areas of economic, political and social participation. The data disenfranchised will find it harder to establish personal creditworthiness or political influence; they will be discriminated against by stock markets and by social networks. We need to start seeing data literacy as a requisite, fundamental skill in a 21st-century democracy, and to campaign — and perhaps even to legislate — to protect the interests of those being left behind.

David's concern about the growing disconnect between the data-rich and the data-poor is a salient reminder that, as we become increasingly immersed in the digital world, the notion of a digital divide is defined not simply in terms of those who have access and those who don't but those also by those with the knowledge, skills and dispositions to navigate their way through the morass of data, content and applications etc. and those who don't. 

The focus in David's comment is specifically on data. We are awash in it – overwhelmed by it. As he illustrates, we are generating it in quantities unprecedented in human history. Schools aren't immune. We generate large quantities in our SMS systems, our LMSs and our accounting packages to name a few. The introduction of increasingly sophisticated analytics applications helps us yield valuable information from this data – but the ability to conceive of and follow through with the development of these sorts of tools, and the interpretation of what they reveal requires specialist abilities.

Imagine the benefit to our school systems if we had access to the sorts of tools and algorithms used to underpin how Amazon operates for instance, or how Google customises search etc. Our ability to customise personal learning pathways for students, to anticipate learning needs, gaps in resources or target specialist interventions would be greatly enhanced. This sort of activity would certainly differentiate between the haves and the have nots in terms of data and data use.

So what are the implications for schools? How might we close this form of digital divide? To be honest I'm still processing my thoughts on this, but a couple of ideas are emerging…

  1. First – we need to consider the concept of what David calls 'data literacy', as a part of the wider concept of digital literacy, and ensure that we are catering for its development in all areas of the curriculum and across all levels of the school system. The goal of developing digitally literate, data-savvy students should be a priority in our student graduate profiles, along with basic literacy and numeracy skills.
     
  2. Second, we need to agree on some system-wide approaches to data management and use. The e-asTTle tool that is currently being used in schools demonstrates well the benefits that can be achieved when large quantities of data are analysed and represented in ways that allow for informed decisions to be made about student learning. Imagine what more could be achieved if schools became even more collaborative in sharing the data they have to assist in building real-time analytics to help inform key decisions about resourcing, placement of teachers, targetting courses etc. 

I'm sure more will come to mind as I ponder this further. What ideas do others have? Would love  you to share them in the comments box below…

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