So have we all got it now? When our industry (the information services and content provision businesses, sometimes erroneously known as the data industry) started talking about something called Big Data, it was self-consciously re-inventing something that Big Science and Big Government had known about and practised for years. Known about and practised (especially in Big Secret Service; for SIGINT see the foot of this article) but worked upon in a “finding a needle in a haystack” context. The importance of this only revealed itself when I found myself at a UK Government Science and Technology Facilities Council at the Daresbury Laboratory in he north of England earlier this month. I went because my friends at MarkLogic were one of the sponsors, and spending a day with 70 or so research scientists gives more insight on customer behaviour than going to any great STM conference you may care to name. I went because you cannot see the centre until you get to the edge, and sitting amongst perfectly regular normal folk who spoke of computing in yottaflops (processing per second speeds of 10 to the power of 24) as if they were sitting in a laundromat watching the wash go round is fairly edgy for me.

We (they) spoke of data in terms of Volume, Velocity and Variety, sourced from the full gamut of output from sensor to social. And we (I) learnt a lot about the problems of storage which went well beyond the problems of a Google and a Facebook. The first speaker, from the University of Illinois, at least came from my world: Kalev Leetanu is an expert in text analytics and a member of the Heartbeat of the World Project team. The Great Twitter Heartbeat ingests Twitter traffic, sorts and codes it so that US citizens going to vote, or Hurricane Sandy respondents, can appear as geographical heatmaps trending in seconds across the geography of the USA. The SGI UV which did this work (it can ingest the printed resources of the Library of Congress in 3 seconds) linked him to the last speaker, the luminous Dr Eng Lim Goh, SVP and CTO at SGI, who gave a magnificent tour d’horizon of current computing science. His YouTube videos are as wonderful as the man himself (a good example is his 70th birthday address to Stephen Hawking, his teacher, but also look at ( And he focussed us all on a topic not publicly addressed by the information industry as a whole: the immense distance we have travelled from “needle in a haystack” searching to our current pre-occupation with analysing the differences between two pieces of hay – and mapping the rest of the haystack in terms of those differences. For Dr Goh this resolves to the difference between arranging stored data as a cluster of nodes to working in shared memory (he spoke of 16 terabyte supernodes). As the man with the very big machine, his problems lie in energy consumption as much as anything else. In a process that seems to create a workflow that goes Ingest > Store and Organize > Analytics > Visualize (in text and graphics – like the heatmaps) the information service players seem to me to be involved at every point, not just the front end.

The largest data sourcing project on the planet was represented in the room (The SKA, or Square Kilometre Array, is a remote sensing telemetry experiment with major sites in Australia and South Africa). Of course, NASA is up there with the big players, and so are the major participants in cancer research and human genomics. But I was surprized by how Big the Big Data held by WETA Data (look at all the revolutionary special effects research at in New Zealand was, until I realised that this is a major film archive (and NBA Entertainment is up there too on the data A List) This reflects the intensity of data stored from film frame images and their associated metadata, now multiplied many times over in computer graphics – driven production. But maybe it is time now to stop talking about Big Data, the term which has enabled us to open up this discussion, and begin to reflect that everyone is a potential Big Data player. However small our core data holding may be compared to these mighty ingestors, if we put proprietory data alongside publicly sourced Open Data and customer-supplied third party data, then even very small players can experience the problems that induced the Big Data fad. Credit Benchmark, which I mentioned two weeks ago, has little data of its own: everything will be built from third party data. The great news aggregators face similar data concentration issues as their data has to be matched with third party data.

And I was still thinking this through when news came of an agreement signed by MarkLogic ( with Dow Jones on behalf of News International this week. The story was covered in interesting depth at but the element that interested me and which highlights the theme of this note concerns the requirement not just to find the right article, but to compare articles and demonstrate relevance in a way which only a few years ago would have left us gasping. Improved taxonomic control, better ontologies and more effective search across structured and unstructured data lie at the root of this, of course, but do not forget that good results at Factiva now depend on effective Twitter and blog retrieval, and effective ways of pulling back more and more video content, starting with You Tube. The variety of forms takes us well beyond the good old days of newsprint, and underline the fact that we are all Big Data players now.

Note: Alfred Rolington, formerly CEO at Janes, will publish a long-awaited book with OUP on “Strategic Intelligencein the Twenty First Century” in January which can be pre-ordered on Amazon at And I should declare, as usual, that I do work from time to time with the MarkLogic team, and thank them for all they have done to try to educate me.


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