Automating data science and machine learning for business insights | Automation - Information Age

The huge explosion of data is causing a headache for organisations: how can we exploit it and gain invaluable business insights?! observes Nick Ismail, editor for Information Age.

Data scientists are crucial in the success of data science projects. But, they can't do it alone. They need help from other skill-sets, as well as automation solutions.

It all comes down to machine learning and data science.

Data, the oil that greases the cogs of the modern machine. But, there’s a problem. Organisations are struggling to gain business insights from this new power.

In short supply 
In the market, many enterprise customers are trying to build very big data science teams. Some, are trying to hire hundreds to deal with the explosion of data; with sources ranging from customer input to IoT devices — this will become the main channel.

But it’s not very easy, there’s a huge shortage of data scientists.

There are, as Gartner coined, citizen data scientists — a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics — but they provide a complementary role to expert data scientists. They do not replace the experts, as they do not have the specific, advanced data science expertise to do so.

Even with this, many enterprises are really struggling to establish a citizen data science team, let alone a data scientist team.

Data science  
Data science is described as a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

Naturally, it has many different components. One of which is machine learning, which is “the most fun part of data science”, according to Ryohei Fujimaki, CEO and founder at dotData...

Data scientists can’t do it all
A good data scientist needs to have a strong mathematical and statistical skill-set, but often, they do not possess business and data engineering skills.
Read more...

Source: Information Age