If you have had the chance to participate in a Kaggle competition, then you will have experienced the thrill of aiming to be the best of the best in predictive modelling, seeking fame, glory and prize money. Like observing super heavyweight boxers in slow motion, we marvel at the data modelling muscle brought to bear by each competitor, trying to knock each other from the top of the leaderboard. Towards the end of each competition, Herculean effort is summoned to supplant one top submission with another as small, incremental improvements are wrestled by the competitors.
Can a follower of Analyst First indulge in such guilty pleasures if it is the case that “Analytics is an Intelligence Activity”? Does not all this flexing of muscles overlook the thinking, careful consideration and peaceful contemplation of data?
Setting aside the ‘brain’ versus ‘brawn’ metaphor for a moment, there are a couple of relevant issues to explore regarding the Kaggle competition and the role it plays in spreading the message about “analytics”. In the Kaggle forum post The Good and The Bad of Kaggle (deliberately provocative to get your attention) , I have discussed a few of these issues and I would be interested to know the opinion of the A1 readership. To summarise:
The change I am advocating is in the perception of what activities need to take place before Kaggle modelling (and after, but that is worthy of its own discussion!). There are many non-technical and novice technical observers of Kaggle, and for some this competition is where they are forming their conceptions about analytics. Kaggle is a great platform for furthering the mainstream accessibility of data analytics, but it is also the perfect moment to educate newcomers on the wider analytics process.
We all have a role to ensure that it is understood by newcomers to analytics that it is both brawn AND brains that produces successful results from analytics.
Secretly though, it is thrilling to see a contender knock out their opponents with an unexpected “left-hook” model.
About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
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