Machine learning is poised to pave the way for many exciting opportunities for businesses, but there are many hurdles to be crossed before getting to the finishing line. Many organisations are still struggling with legacy systems and are slow to invest in more advanced technologies. But the more pressing issue at hand, one that has been an ongoing problem for the technology sector, is the short supply of qualified talent to match what is a fast-moving and demanding industry.

Unexplored territory

By design, machine learning is experimental and often unpredictable – a lot of exploration is required before organisations can even begin to make sense of the data and which machine learning algorithms will work best. While the unpredictable nature of machine learning is understandably daunting, many organisations have yet to fully grasp what is required to effectively deploy and manage it.

Access to data

Access to data is crucial to doing any form of machine learning, but virtually all businesses have policies that limit access to certain information and data. What they need to understand is that machine learning is, ultimately, fuelled by all sorts of data. Therefore, providing consistent and ongoing access to that data, while still adhering to security and other data governance mandates, is essential for the algorithms to be effective. Even if organisations have policies that allow easy access to real-time data, they need to consider how to bring the many data sets together to ensure a steady flow of data. Machine learning models should not be trained on a fixed one-time set of data, as the efficacy of models will decay, but continuously trained on the changing data, so that the models can evolve with the changing behaviour of the data and systems.

Outdated technology

Another barrier to machine learning adoption is outdated technology. Many organisations still rely on legacy technologies and on-premises data systems, making it incredibly challenging to effectively process and manage the increasing volume and variety of data generated. As a result, IT professionals are spending too much time manually coding to resolve the disarray of data and building the data pipelines, and not enough on higher value tasks.

Out with the old, in with the new

Businesses face two options when it comes to the technology question – either staying ahead of their competition or risk becoming obsolete. Organisations not only need to improve user experience and ensure effective use of their resources, but also be able to anticipate and predict their users’ actions and expectations in order to deliver accurate recommendations or actionable insights. To address the technical issues, businesses should invest not only in emerging cloud technologies, but those that can also provide a level of intelligent automation and require minimal coding to lift the burden on IT professionals.

Investment doesn’t just apply to technology, but to employee training and upskilling as well. Organisations should ensure that their employees, both in IT and across various lines of businesses, are well equipped to fully utilise these new technologies, and are regularly being trained to update and expand their skill sets.

Closing the skills gap

Another major issue that organisations have been facing for a long time is the lack of skilled talent. With the adoption of machine learning and cloud technologies becoming more widespread, it has led to a corresponding rise in demand for qualified talent who are trained to utilise these tools effectively. The number of data scientists and machine learning specialists are growing, but the reality is that these specialist skills take time to develop. As a result, the current supply is not keeping up with demand.

It shouldn’t just fall on businesses to address the talent shortage issue – universities have a role to play too. It is encouraging to see that universities are adding more machine learning and data science courses every day, with some making these new disciplines part of core curricula for certain degrees. But it shouldn’t stop there – while academia provides students with theoretical training, enterprises can provide insight and experience based on real-world business problems. Businesses should step in by working with universities to help students gain practical, on-the-job experience. One way of doing this is to make work experience a course requirement. Progressive universities make it compulsory for students taking these courses to spend a semester working for a company in a relevant field. This not only gives students the opportunity to work with a variety of different companies, but it shortens the recruitment process considerably for businesses when it comes time to hire the next wave of full-time employees.

Additionally, businesses also need to look beyond recruiting university graduates for machine learning and data science positions. In an industry where skilled talent is in short supply, training existing employees who have other relevant skills will help bridge the skills gap. It is amazing to see how many valuable and low-cost resources and materials are out there to help people self-train so they can learn how to code and get fully up to speed on new technologies.

An interesting trend we’re starting to see is that professionals, such as senior business analysts and software engineers, are expanding their knowledge of data science and machine learning. Many software engineers, for example, will have some familiarity with machine learning as it is effectively software. While IT business analysts may not have software programming experience, their domain knowledge, their experience handling data and analytics and an emerging set of intuitive self-service technology tools enable them to take advantage of machine learning. Looking beyond data scientists and investing in people with other useful skills in this way will not only help alleviate the talent shortage, but also allow a wider range of users to engage with machine learning and deliver value for the business.

How to make the most out of machine learning

Machine learning and AI will eventually permeate all aspects of applications and system, not just predictive analytics. But we’ve still got a way to go before that becomes a reality. Businesses need to be firm in ridding themselves of legacy systems and invest in technology and resources that will keep them ahead of the game. But it will be a lost cause if enterprises don’t ensure their employees are equipped to make the most out of these technologies. Collaboration with universities and vocational training schools is essential in not only bringing in fresh talent, but ensuring employees are constantly expanding their skills and knowledge.

All of these developments point to a bright future ahead, one that will see the widespread adoption of machine learning to solve some of tomorrow’s pressing business challenges.

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Notes:

The post gives the views of its author, not the position of LSE Business Review or the London School of Economics.

Featured image by Mike MacKenzie via www.vpnsrus.com, under a CC-BY-2.0 licence

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Craig Stewart is the senior vice president of product management and product marketing at SnapLogic. Craig is an experienced manager of technical teams with many years of experience guiding the evolution of data and application integration technologies. Over his career, he has been a significant contributor to the development of several fast-growing technology companies, including Oracle, Cognos, Powersoft, Sybase, iMediation, and Sunopsis.