Business leaders have traditionally had a somewhat complicated relationship with technology. Many of them instinctively know that its deployment could be transformative for their business although they lack the deep knowledge required to fully understand how and when to invest. Recent research from Fujitsu suggests that levels of uncertainty around the way businesses should plan for imminent technologically-driven change are so high that business leaders around the world favour a co-ordinated, global approach led by intergovernmental bodies and governments.
Whereas I do not think these levels of uncertainty and doubt are going to disappear, I do believe that when it comes to the use of AI, data analytics and data science, 2019 will be the year when we see a sharp increase in its use by organisations of all sizes.
What’s driving this trend?
Central to the rise of data analytics are open source tools, which I believe are doing more to democratise the field of data science than anything else. As someone working in the field, I believe that open source is not just an interesting side note to the data science revolution; it is absolutely integral.
In the past, to become a valuable and effective member of a research team one needed special training to master proprietary systems along with years of experience. This is now no longer the case. Thanks to a wider choice of open source tools, more people can now begin contributing valuable insight and analysis from the day they begin work at an organisation.
Changes driven by speed
The benefits of data science and open source programming are exactly what today’s fast-moving businesses need and, quite rightly, demand. There was a time when market trends could be more easily identified and predicted due to the slower pace of global markets. This seems like a world away now, of course, when a single tweet can change a company’s fortunes or upend a corporate strategy. Before these pressures reared their heads, however, proprietary (closed source) software worked for most companies as they sought to analyse their data. This software can be incredibly slow, however. Until recently, it was common for traditional vendors to release updated versions of critical proprietary software quarterly or even annually. Clearly, this will simply not do in a climate where political, social and environmental turmoil seem relentless and business leaders need to be more agile and responsive than ever. Open source software, however, can be modified or rewritten in days or even hours, which means it’s a no-brainer for real-time analytics. Some will also generate tools and statistical packages that can be downloaded free of charge, giving data scientists an almost limitless supply of fresh programming resources.
Why does this matter?
The implications of this increase in the use of data science is far-reaching. Organisations that have hitherto only used data science around the fringes of their operations or which have considered themselves too small to be data-driven can now reconsider their position and begin to deploy this in ways which will transform the way they operate. AI suddenly becomes something which smaller businesses can start to plan for and benefit from.
Business leaders have always struggled with how best to leverage and integrate the talent pool within their organisations. By choosing open source tools, from a skills perspective, they are also opening themselves up to the widest possible pool of talent and ways of working which adapt to constant business change. This will also foster a more collaborative and creative relationship between the business and technology teams.
One of AI’s biggest obstacles has always been the disconnect between data science teams and an organisations’ subject matter experts. The complexity of the underlying tech behind AI demands huge amounts of data science expertise which subject matter experts, still critical to an organisations’ success, will rarely have. Closing the gap between data science teams and other areas of the business is central to deriving the maximum amount of value from an enterprise’s AI initiatives. 2019 is shaping up to be the year when this will actually happen, driven by a more equitably distributed amount of technology expertise throughout an organisation and the smart application of AI.
The end of data scientists? I don’t think so…
The democratising impact of open source software does not mean that data scientists will be surplus to requirements, quite the reverse! Open source tools mean organisations will benefit from fresh programming resources and this will expand AI’s potential – but using these tools requires skill, judgement and experience.
Data scientists will be more in demand than ever but the way in which their expertise is deployed will change in line with the wider changes we see coming into play. Rather than have teams of data scientists working on a full-time basis to address all the company’s challenges, I believe we will see a greater use of data scientists on a project-by-project basis, working in close collaboration with other areas of the business to deliver real value.
None of us really know what the future holds, but the future for AI will be bright in 2019 and beyond. I am looking forward to watching its bright future take shape.