A new recipe for enterprise data, 'too many cooks' is over

The adage 'too many cooks' might still apply in the soup kitchen, but in cloud-centric data analytics, there is an argument for more ingredients (data sources), more cooks (data scientists) and more servings all round.

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The old adage 'too many cooks' might still apply to soup, sandwiches and sausages, but in the cloud-centric world of enterprise software and the data analytics it now supports, there are compelling arguments for more ingredients (data sources), more cooks (citizen data scientists) and more serving plates (application endpoints and analytics payloads)... but to serve up this new dish, we need a method to ensure we prepare, blend and manage our digital mix in the right way.

At the risk of tabling (pun not intended) too many cooking analogies, this above truism is intended to suggest that, as in food, ingredients are everything. Method will always be fundamental too, but without a good source and knowledge of provenance, we cannot hope to serve up the right kind of dish, be it data or delicious delicacies.

If data analytics were a pasta course, then it would probably be spaghetti alla puttanesca i.e. a dish with all manner of ingredients in various shapes and sizes and from the widest variety of sources possible.

Garbage in, garbage out

Leaving the kitchen for a moment then, the lesson for data analysts, data scientists and (increasingly now) citizen data protagonists is an important one when it comes to predictive modeling tools now being used to inform business decisions.

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