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Data Science en Masse

By Steve Jones,

GE is an amazing company. They seem to have so many divisions and produce a wide variety of products. Their financial investment success amazed the world and made Jack Welch an icon to many businesspeople. I remember reading about revolutionary management techniques at one of their jet engine plants that dramatically increased efficiency and performance of that location, similar to the ways that DevOps can produce better software. They have transformed the company from light bulbs to televisions to nuclear power and more across the decades.

And they're not done yet. GE is building a workforce for the 21st century, teaching many of their employees about data science and machine learning. The company is working to retrain scientists and help them explore new ways of using AI techniques to build better software. There are machine learning and data analytics courses available to employees, with the aim of creating hybrid employees that add digital skills to the knowledge they already have in other areas.

Why is GE looking to transform it's workforce with data science skills? They are creating AI software for their products and hoping to expand this further into more areas. With the competition from many other vendors, the ability to generate better results for clients, even just slightly better, might be enough of a differentiator to allow them to continue to grow as a leading industrial company.

Would this work for your company? Your import organization, service company, retail business? Perhaps. Machine Learning isn't perfect and doesn't produce the best decisions, but if it can slightly improve the performance of your organization, perhaps it's worthwhile. Microsoft is certainly making some of practical elements cheaper with the easy to use with new products, such as Azure Machine Learning.

The challenge is that this is just the final element. Your company still needs someone that has built some knowledge of the deep mathematical concepts behind machine learning and has spent quite a bit of time experimenting with your data, building models and determining the relevant features needed. Preparing and loading data is also a challenge, which is why I think one of the core skills future data professionals need is the ability to quickly and effectively build ETL pipelines. If you can get those things done, a tool like Azure ML might be just the thing to add a few efficiency (or profitability) points to your bottom line.

 
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