“The 5 Ways In Which Automation Will Change The Big Data Landscape”

(Sarah Facq, Tengu.io, 21 March 2019)

“Highly demanded data scientists search for solutions to automate certain parts of their job”.

Data scientists have to be up to date with the best suited technologies, deliver POC's at a high pace & have business feeling to create right analysis solutions. Data scientists & automation solutions: 1) Infrastructure. A) Automating deployment tasks will make it possible to get rid of operational tasks & start earlier with BI. B) Open-source automation platforms automate the deployment, setup & configuration processes by creating digital work environments. 2) Coding. Data scientists have been forced to write custom code. By automation, empowers them to shift focus from routine coding & data wrangling to tasks that add real value. 3) Data conversion. A) Before data scientists can use the data for BI, they spend 70% of their time to cleaning & structuring data. B) ML & automation will be used to make the process faster. 4) Integration. A) Integrated Big Data as a Service & the cloud, are on the rise to deliver big data teams complete packages. B) Well, integrated stack-solutions should make it possible that clusters can be deployed automatically on the service of choice. C) Automated stacks. The customer plugs in the data, the service performs all the scalable activities & cleaning/preparing the data for applying the algorithm. Whether it is an anomaly detection algorithm, the solution is ready for the customer for that particular domain/vertical. 5) Data Analysis. A) With recent GPUs (graphics processing units) computers are becoming much better in pattern recognition. B) Predictive analytics will be made easier & marketing managers will also be able to predict without a data scientist.

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