Data Scientists Will Help You Overcome Your AI Challenges

Posted 2017-03-27T Posted by Tom


By Jason Muller, COO



Corporate interest and investment in AI is booming. In a recent study by Forrester, the year ahead for AI was marked for a 300% surge in funding across industries. At the root of this is the most valuable business asset of all – data, and the commercial insight and direction that can be gained from it by good data scientists.

Businesses that use artificial intelligence (AI), big data and the Internet of Things (IoT) technologies to uncover new business insights will steal $1.2 trillion per annum from their less informed peers by 2020. Artificial intelligence will drive the insights revolution. Forrester


The promise and problems of AI



 67% of telecommunications decision-makers are considering or prioritizing developing IoT or M2M initiatives in 2017 and customer data management projects will increase by 75%. Forrester
The rush of investment soon to be seen is down to the simple fact that it has only been recently that AI has become tangible for business. Previous to this, the promise of AI has seemingly been little more than a super shiny pipedream, and it is only now, that AI has really been workable in any commercial sense. Alongside the formidable partners of Big Data, and IoT technologies, divides may be bridged where there used to be a lack of access to data and only rudimentary insights.

Yet this level of investment isn’t before time, 2016 suffered from 50% of commercial data collected by edge devices (such as phones, wearables, and cars) simply being disregarded and left unused. This left analytics-makers unable to craft any meaningful location analysis. 50% of location data simply fell by the wayside (data which could and should have helped businesses break down the barrier between board room and the physical world).

And yet despite the promise of this investment, there are problems ahead…

The same study that outlines such promise, also outlines many problems - businesses have significant stumbling blocks when it comes to harnessing AI: 39% of those studied are unclear as to why and how AI can be used; while 42% report that they have no defined business case. And even if they did have both business case and knowledge, many corporations lack the technical ability to implement AI solutions in any event, with 33% reporting this being the case. Still with us through all those stats? Good! In short, the promise of AI is rivalled only by the problems that corporations must grapple with if they are to harness it for the powerful data insight it can provide.

 But there is a singular solution – and it lies in data science talent

For corporations, accessing top talent will prove critical, and for average data scientists, the increasing adoption of AI will demand their own evolution and adaptation. But adapt they will!

The Pivigo S2DS programme, designed for statisticians and data academics, is already helping such talent take to the corporate world they now find themselves in such demand for. They are being primed for a future driven by AI tech, and nurtured onward to tackle the most challenging of strategic projects. Such programmes may prove as important for businesses, as they will be for the average data scientist. Here’s why…



Change is afoot for the role of data scientists



As the AI wave becomes transformational across end-markets... the demand for data scientists is growing exponentially. The role of data scientists in this technology will assume a new level of importance. Rudina Seseri - Founder and managing partner at Glasswing Ventures
AI and its talent pool has rapidly evolved of late - Google’s breakthrough in a system that could identify cat videos (I trust it works for dog videos too, Google?) and Facebook’s Deepface, which can now automatically recognise and tag people in photos, have spearheaded this change. And whilst the basic notions of deep learning had been floating around in academia for decades, today’s processing power and capable technology has made a concept, a reality.

Both basic machine learning and deep learning have and continue to struggle with a talent gap - skills are being sourced and shipped around the world as competition hots up to harness AI. Veterans of the industry are being leap frogged by their younger counterparts - those who’ve been privy to advanced deep learning approaches and training.

The nature of AI and data science is evolving at a rapid rate; make no mistake - the data scientist of today faces a challenge of staying relevant as their industry transforms into an AI driven world. As businesses adapt to new practices to ensure skills match business requirements, there'll be rich pickings of well-paid positions for data scientists who are ready, willing and qualified.

Safe to say that revolution awaits, but Forrester’s research could be considered relatively tame as compared to their longer term predictions, such as how Intelligence-based services and apps are set to transform the world and its workforce over the next five years, or how 7% of US workers will be replaced and made redundant by robots by 2025. (There will be no respite in the UK, for any of you hoping to avoid the fallout!) Ultimately, businesses must catch on, and they must catch on quickly, if they’re going to even cling to the coat tails of all that AI is about to deliver.

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