Big Data: Productivity Engineering

Big Data continues into 2016 with improvements in the framework arena, and likewise products.  I’m still on the H2O bandwagon until anyone can suggest an alternative.  I did have a quick look at Deeplearning4j, but I’m not convinced it will offer any ROI over H2O.  Further, H2O with the RStudio integration just makes life so simple to spike ideas, and move them to production leverage the integration of Apache Spark.

Its interesting to see what others are doing in the big data space outside of the usual use cases studies that always include financial fraud detection.  Increase productivity within the workplace though identification of trends, and data analysis is discussed in this short TIME article.

Digitalist magazine discusses the often difficult topic of data collection within the organisation, with some idea of how to message the usage.  In many way, data science on organisation data could be seen as a further aid to retrospectives and such that teams undertake today:

Ideally, data collected on employees will be used to provide constructive feedback to employees so that they can improve their performance.

Business Intelligence provides interesting ideas around employees:

By combining traditional HR data, employee demographics, rewards and performance data, with additional information such as sales and profits, employees and customer surveys and internal communications, HR gains a better picture of employee performance and the factors influencing performance. With this data companies can better reward top performers and help them develop further, as well as have a better profile to look for when recruiting new employees.

The results from Cubist Pharmaceuticals provide an interesting productivity gain for employees:

the data also revealed that employees weren’t interacting during the lunch hour. Instead, they would spend lunch in their office. In response, the company created a better cafeteria to entice workers to eat together.

Finally, we end with an interesting article on calculating Productivity in Employees.

~ by mdavey on April 18, 2016.

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