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Blog Series Part 5 – Small Deep Data Vs Big Data

Posted on March 12, 2020 by David Staunton, Global Services Director & Ryan McInerney, Technical Consultant

Small deep data Vs big data, part of our: ‘Life Sciences 4.0, Revolutionising Life Sciences Manufacturing Through Connected Systems & Data’ blog series.

Big data draws from sources that have traditionally been disconnected and looks for relationships and trends that were previously undetectable. For example, combining production data with that from sales and dispatch systems can streamline production planning. Enterprise Resource Planning (ERP) tools can already do some of this on a much smaller scale, however they use smaller data sets than a Life Sciences 4.0 plant will generate, so the conclusions and recommendations are comparatively less valid.

The life sciences industry has been doing big data analysis for over 20 years. However, there are inherent dangers that will be exponentially more problematic when interpreting the larger data sets that will be created by Life Sciences 4.0 approaches. Spurious correlations, which are inevitable when dealing with numerous variables and data points, will no doubt seem attractive to operators and engineers but must be ignored to ensure data is used effectively.  If a business acts on this data without confidence, the consequences can be very problematic and even detrimental.

It is vital that businesses adopt the data-driven improvement cycle approach – DMAIC.

> Define
> Measure
> Analyse
> Improve
> Control

The only correlations that should be acted upon must be carefully hypothesised, tested and validated. Process engineers must measure a large amount of data with a small number of variables, wait, monitor and define improvements before implementing change and starting the cycle again. Customers require a narrow, deep focus – not a broad and superficial one. This iterative approach will only be successful if complete, contextualised and accurate data is collected from a fully integrated network of systems and machines.

In summary, today is not about using big data to streamline and improve systems, it is about accessing small deep data to answer the questions you need answering. If companies get this right they can use data to deliver end to end visibility, transparency, operational excellence, predictive insights and in turn transform business processes.

In the next blog in this series we look at what latest thinking and technology is out there to support your Life Sciences 4.0 initiatives and goals.

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