Datalicious partner overview

Below is a short extract of our most important best of breed partners. Please read the more detailed announcements further down if you would like to find out more about how our partners help us turn data into actionable insights for our clients or email us.

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WhereScape RED: Building enterprise grade data warehouses in the cloud just got quicker and easier

Our data team have just completed training on WhereScape RED, which is an amazing tool for building data warehouses. Datalicious will be using the tool to help our clients to combine complex data from multiple sources in one single Oracle data warehouse in the cloud in a streamlined way so we can start delivering some actual insights faster! This will make stuff quicker, cheaper and easier. Exciting times!

Well done Kent, Mahesh and Chaoming.

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A data visualisation to divide the nation: Analysing Tweets for SBS's Go Back series #gobacksbs

SBS's remarkable reality TV series on refugees and asylum seekers called Go Back exploded on Twitter with over 36,000 Tweets last week. We thought it would be cool to analyse the Tweets and try distill the sentiment of a nation (or at least the Twitter savvy part of it) on this issue that apparently divides the population.

Partnered with Alterian using their business intelligence product SM2 to extract all the information on each Tweet, a database of all word pairs was created (over a million!). The word pairs, like 'asylum seekers' or 'boat people', were generated from each Tweet independently and then tallied up over all Tweets. Common words like 'the' and 'it' were removed, and a stemming algorithm was used to group words such as 'Australia', 'Australian', or 'Australia's' together. All Tweets were treated equal and all Retweets were included so that the content of the most popular and followed people on Twitter would emerge via Retweets.

Once the top word pairs (based on a tally) were finalised an open source software called Gephi, which is a powerful tool for visualising and analysing large networks, was used to present the data. See below for our first attempt; each word is connected to the words that were paired with it, taken from the the top word pairs. The size of the words is related to how many other words are connected to it (not how mant times the word pair appeared in all Tweets).

The whole network is below and shows how the different words are associated. The word 'Raquel' is at the centre (and is the largest) because it was associated with the most words. Many interesting word associations come out of the data. For example, there is a sub-network with words 'live', 'exports', and 'corners' (top left) most probably comparing the SBS Go Back series to the Four Corners program that exposed the live exports trade.

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One thing that you might notice is that there are pockets of networks that are associated with a particular Tweet that was Retweeted a lot. For example the Tweet below is associated with the sub-network (bottom middle) that contains words such as 'no', 'vote', 'mad', 'point', and 'court'. You can search Google with any combination of associated words along with the word 'gobacksbs' to find the Tweets that made up the data.

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A focus on the main star of the program Raquel shows that the word 'Raquel' was often used in Tweets along with words like 'ignorant', 'racist', 'hate', and 'complain', but also the words 'hope', and 'change'. This surely reflects the change in viewer sentiment for Raquel as she modifies her views on refugees and Africans, and shows compassion, over the three-part series.

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 An interesting set of word pairs ...

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In our next installment we will try, among other things, to see what comes out if Retweets are not used. We will also visualise the number of times each word pair occurred making the line joining words thicker if it occurred a lot. There is a lot of scope for further analysis as Alterian's SM2 provides data on things like the gender of people who Tweeted, where in the world they are from, and when they Tweeted. See below for a screenshot of SM2's interface:

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We hope you like it. Let us know what you think in the comments section below. Stay tuned.
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Data mining and visualisation of raw social media data from BuzzBumbers in Tableau BI platform

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We recently did a bit of custom data mining for one of our clients to showcase the power of analysing raw social media data through business intelligence platforms such as Tableau and given it was only a demo of public data it should be ok to share the results with the rest of you.

The Australian based social data provider BuzzNumbers (great platform, check it out) was so kind to provide the raw data (small sample of consumer generated content mentioning Zara fashion related terms) and we then analysed and visualised the data using Tableau and the free word cloud service Tagxedo, the results of which you can see below.

BuzzNumbers is scanning all social profiles for geographic keywords and based on their frequency they allocate users with a home location which allowed us to visualise not only where social buzz originated from geographically but also what region was generating the most influential buzz. In Zara's example the majority of buzz volume came from the US, UK and Australia but there were a few interesting outliers such as Thailand and Egypt that showed high buzz influence (smaller but darker red dots).

Next we looked at what sites and categories of sites were generating the most buzz value or media value and interestingly while Twitter generated the most buzz value other sites such as Youtube where more influential (darker red bars). Once we broke the sites down by major categories and countries a completely different picture emerged hinting at different site categories performing better in certain countries for example forums seem to be big in Australia but negligible overseas.

And of course we had to visualise what people were actually talking about hence the word cloud to help visualise the different topics, i.e. the larger the font the more mentions that term had. The BuzzNumbers guys don't believe in automatic sentiment analysis (and we kind of agree) so they offer a cost effective manual sentiment analysis but unfortunately we didn't have enough time to set this up in this case.

BuzzNumber metrics definitions

  • BuzzValue: Equivalent online advertising value in AUD (based on banner ad CPMs)
  • BuzzRank: Website rankings based on traffic/visitors/page views (1 being highest)
  • BuzzInfluence: Influence based on search rankings & link popularity (for post not site)

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Click Tale offers free heat maps visualising mouse movements, clicks, scrolls and form analytics

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Have you ever wondered where your website visitors are focusing their attention? What visual clues are they responding to? And wouldn't it be nice to have this data visualised in form of heat maps? 

Last week, Chaoming came across a new service called Click Tale which generates heat maps similar to the one below which shows mouse movements on our home page. Apart from mouse movements you can also report on mouse clicks, attention, scroll reach and forms. 

Register for a free account, copy and paste the generic code into your pages and wait until Click Tale sends you an email announcing that your heat map reports are ready (nice feature by the way), easy as that.

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