Plenty of investment is taking place in machine learning – but the skills gap isn’t going away
If you thought the cloud skills gap was bad, then it’s only going to get worse as more emerging technologies mature.
According to a new report from Cloudera, more than half of the 200 European IT managers surveyed said they were reticent at adopting machine learning technologies because they did not have enough skills and knowledge of the area.
As might be expected, plenty of investment is going to be taking place. Almost nine in 10 (87%) of those polled said they have already implemented machine learning technology, or plan to do so. While a similar number (89%) said they had a ‘basic’ understanding of ML’s benefits, 60% admitted they lacked the skills to implement the solutions fully.
When it came to cost, however, there was an overall positive reaction. Three quarters (74%) of respondents said machine learning would be a cost that would eventually reduce the bottom line, while a third of companies said they were already seeing a return on investment from their initiatives. 84% of those polled said machine learning provided a competitive advantage, with key benefits being the improvement of operational efficiency and greater data insights.
Regular readers of this publication will be more than aware of the difficulties organisations face in terms of closing the skills gap, seemingly regardless of technology. Cloud computing professionals continue to be at a premium; a study earlier this week from OpsRamp found 94% of the more than 100 respondents were having a ‘somewhat difficult’ time finding candidates with the right tech and business skills to drive digital innovation.
Problems can go in both directions too. Let’s put it this way: if organisations are going to hire someone to take charge of their ML initiatives, they are going to receive a driven, talented, intelligent professional. If this is the case, make sure you have enough work for them to get on with.
Writing in Medium in March, Jonny Brooks-Bartlett, data scientist at Deliveroo, noted the disparity between companies who didn’t know what they were doing and young, hungry execs. “The data scientist likely came in to write smart ML algorithms to drive insight, but can’t do this because their first job is to sort out the data infrastructure and/or create analytic report,” he wrote. “In contrast, the company only wanted a chart they could present in their board meeting each day.”
Ultimately, it is this uncertainty which needs to be eradicated if organisations want to get serious about machine learning. “Although most IT buyers understand the benefits of machine learning, many are still unsure about how to implement and how it will impact their businesses,” said Stephen Line, VP EMEA at Cloudera. “These are barriers that can be overcome through upskilling staff, recruiting new data talent, working with the right partners who can complement existing teams, and through leveraging external technology.”
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