What your Data Life Cycle probably looks like

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From Analyst, to Scientist, to Engineer 

A common problem we see is that different roles are using different methods of preparing and reporting data. What occurs is that there is very little collaboration between key data driven roles within the organisations that we deal with.

For example –  we see that analysts use excel and understand databases, data scientists use script and understand predictive analytics and AI, whilst data engineers use code and understand scale and error handling.

Whilst each role is important and is contributing important information through different methods of data preparation, this means that there is a wall in cooperation due to a lack of understanding – simply because of a discrepancy in the tools they use.

What happens is that the data produced is often never integrated together, meaning that despite doing valuable work; the organisation isn’t able to utilise the full potential of the work being done.

Too Much Wasted Time - A Commonality

We’ve found that a commonality amongst analysts, scientists, and engineers is that up to 80% of their time is spent on data preparation. And, to do add to this a further 10% of time is wasted due to poor data quality. That means, up to 90% of time is spent either preparing or cleaning data – and no time left to work together.

The data that is produced is complex meaning that it is difficult to understand except for the person who created it. And, to make matters worse it is too difficult to transfer data from excel to script to code and vice-versa.

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