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Provenance is an increasingly popular topic in the food industry as quality scandals and ethical sourcing of produce push their way to their way to the front pages of the press but have you considered how tracking the provenance of your data can add value to your data analytics?
Anybody who's had contact with software development will be familiar with at least some of the methodologies explored over the years, the spiral model, agile development, the waterfall. However useful as these are, applying their principles to Data Analytics is somewhat of a tricky endeavour.
When working with data, one thing gets me over and over again - granularity. Settling on the right level of granularity means striking a balance between detailed reporting and faster queries all while trying to maintain the elusive quality of simplicity and it's not an easy task.
Re-con-cil-iation, the word alone sends a shudder down my spine. Tying numbers together across months of work, huge volumes of data and complex processing is no simple task, however it’s such a robust method of QA that you ignore it at your peril.
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