Tibco dashboards smooths out pandemic information overload

Michael O'Connell, chief analytics officer, at Tibco Software is making access to COVID-19 data more accessible.

One notable sideshow to the COVID-19 news cycle is that we have moved from a world with a shortfall of data scientists to a world where we are all data scientists… or at least we'd like to think we are. A problem though: we use statistics in the same way as the drunk grabs hold of a lamp post while stumbling. That is, more for support than illumination.

Frustrated by the loudmouths, commentators and politicians, I recently called in to an online session by the Silicon Valley software company Tibco Software. Tibco is a veteran of both understanding data and of the knotty issues of integration and that seems to be a good formula for understanding the complexities of our world today. In the session, the company showed off its independent dashboard that makes what is happening more understandable.

But I remained worried that the data being put out by governments and health officers often lacks several of the five Vs that distinguish Big Data: volume, variety, veracity, velocity and value. That is, a lot of the data I have observed was selective, could not be backed up or was offered in isolation. So, in a follow-up call, I spoke to Michael O'Connell, a genial Australian who is Tibco's chief analytics officer, in order to delve a little deeper.

Supersets and smooth lines

O'Connell told me that the Tibco project was the brainchild of Tibco data scientists and a showcase intended to help Tibco customers and others better observe and troubleshoot this most complex phenomenon and its wider impacts.

For me, part of its usefulness lay in the fact that it is really a superset of figures, harnessing multiple external sources and some crowdsourced by Tibco. Another is that it provides the ability to very easily drill down, for example to English counties, towns and locations therein.  Yet another is the ability to see data in the context of key actions (social distancing rules, school closures etc) to help establish cause-and-effect principles. Last, this toolkit also uses Natural Language Generation from a partner company called Arria, making data easier to comprehend by turning it into writing or speech.

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