Visualising Client Data

Visualising Client Data

An area of interest to me over the last few years has been how to create data-driven insights that assist our clients in achieving their best possible outcome in a matter.  One of the ways that resonates strongly with clients, and that is particularly compelling to me, is employing data visualisation software to introduce perspectives that may otherwise go unnoticed.

I find this technique particularly helpful in large-scale litigation, where documents and related metadata can easily leap into the million+ count, but the idea is valid in any data-intensive context.

I've created a fictional example below to illustrate my point.  In this example, BigCo is a popular company headquartered in Melbourne that has been accused of engaging in conduct which is unfair to a segment of its customer base.  This conduct has been picked up by a national affairs program and will be featured on its 22 June episode.   A promo for the upcoming episode is aired June 20th.  

This visualisation is a hashtag analysis of positive and negative tweets for BigCo in the five day period prior to the episode airing.  (click the image below to explore the data in Tableau)

As we can see, the number of negative tweets rises sharply following the promo, while BigCo's positive tweets die off completely.  A knee-jerk reaction for BigCo in this situation might be to hold a press conference at its headquarters in Melbourne to get in front of the issue right away.  However, visualising the BigCo tweets identifies a critical element that otherwise may not have been picked up and suggests a different course;  the negative social media feedback is region-specific.  Bringing tweet geo-reference metadata into the picture immediately identifies that BigCo's Brisbane office is the best place for it to publicly address the situation. 

This is a greatly simplified example consisting of made up data that exactly suit my purposes.  Having said that, I hope it illustrates the insights that are possible by looking at your data in a different way.

Graeme

(The opinions expressed in this article are mine only, and do not reflect in any way those of Corrs Chambers Westgarth)

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