As I write this DataCon Africa 2018 is 5 days away from kicking off in Cape Town and I wanted to do some basic data analytics of my own on the audience data we’ve collected.
Click here to download the full PDF version with graphs
What Question Am I Trying to Answer?
I recently completed “A Crash Course in Data Science” through Coursera and Johns Hopkins University which taught me to first define the question I’m trying to answer.
In this case: What do I know about the DataCon audience? This question is probably too broad. But I think I gained some insight into the audience.
Through our registration process we ask a battery of questions to collect data on the attendees – speakers, paying delegates, VIPs and sponsors.
It’s important, obviously, for us to know more about our customers so we can develop more targeted products with more accurate and relevant content. The data analytics community in South Africa is developing at such a rapid pace that it’s not easy to define it or categorise it.
DataCon Africa was designed to attract data analytics and data science professionals from a wider range of industries and departments through the departure from our “Chief Data & Analytics Officer” brand. I wanted to appeal to a larger audience. And its worked.
How Large is the Dataset?
At the time of writing we have 208 people registered for the event with a forecast final figure of 215. In my mind that’s a relatively good sample size of the Southern African data analytics community. I say Southern African because we have representation from South Africa, Namibia, Botswana and Tanzania.
The Nitty Gritty
I’ve selected 6 profiling questions to run analysis on. I think they’re the most important and the most descriptive of our sample.
Which Industry is Represented Most?
Unsurprisingly the Banking, Financial Services & Insurance (BFSI) sector is the most highly represented making up more than 50%.
Data analytics is the most mature in these organisations, sure, but another contributing factor is the size of these organisations – mostly the banks. Data analytics is used in retail banking, corporate & investment banking, internal audit, credit…the list goes on. Companies in other industries often only have 1 or 2 departments using data analytics.
