The Future of Data Science

From the Need for Increased Deliverables to Perfecting the Balance Between Human and Machine Interaction

It’s difficult to predict the future of an industry, particularly one as fast-paced and ever-changing as data science. However, we asked a number of data science leaders to do exactly that by asking them what they believed the biggest changes in the industry would be over the next few years – for better or worse.

Provide Key Deliverables to Build Trust

One common answer was to highlight the importance of data science to the general public. AVP data scientist for Barclays Corporate Banking, Gavin Allan, stressed this, “I see it as a key part of our role in the data science industry to build trust with businesspeople and work with them to better understand what we do. Building this trust and understanding means business stakeholders are far more likely to be supportive of data science projects and crucially, act on their outputs. We need this action to deliver commercial value from data science.”

This theme has been explored before by MBN Solutions when we discussed how technology, such as Artificial Intelligence (AI) and Machine Learning (ML), can gain the trust of the general public. Achieving this on a professional level however is equally as important for continuing the commercial success of data science. Martin Thorn however notes that trust should be achieved through key deliverables.

Thorn, who is Head of Data Science at Abrdn, believes that one of the major changes we'll see within the industry is for those investing in projects to start demanding tangible results. He continued,I think the industry needs to start making good on the promises made by the vendors and consultancies. Too many people have spent millions on data projects and have yet to see a return. That needs to change very quickly or we will find that the investment starts to dry up.”

Thorn also has an idea on how this can be achieved, “In order to get there I think we need, as an industry, to be much clearer that a revolution powered by data, more often than not, involves seismic changes in the organisation. That might be job losses, or it might be repurposing people to other roles but either way there will be a big emotional cost to this revolution. That’s the hard bit and it’s the bit you don’t see in the sales literature.”

Clearly, industry experts foresee that better communication needs to occur between industry experts and business professionals. As highlighted by Allan and Thorn’s testimonies, this can be achieved by ensuring organisations are gaining specific deliverables for their investments, whilst being clear about what data science can and cannot achieve.

Find the Balance Between Human and Machine Interaction

Christina Boididou, Principle Data Scientist at the BBC, is optimistic that the world of data science will change for the better over the coming years. She says, “over-relying on algorithms and automated decisions in every case won’t be the way forward. The industry started realising and will realise even more the importance of including the human in the loop when implementing solutions for problems. Human expertise is something irreplaceable, so sooner or later businesses will start using peoples’ and machines’ unique strengths for their benefit.”

“Machines are better in making sense out of complex data and spotting patterns in places where the human eye can’t, while humans are able to handle more abstract concepts where machines struggle. Combining those tactfully will lead to more successful and ethical solutions.”

The Future is Serverless

Finally Davide Anastasia, Head of Data at Audigent, believes that serverless computing will impact data science in a big way, “a few years ago cloud providers triggered the migration of software platforms from on-premise hardware to cloud resources. More recently, cloud vendors have pushed this boundary even further with serverless computing. I see the data science industry slowly picking up on the same trend, moving from models being developed in-house to training performed on the cloud. In the last couple of years many vendors have started offering Auto ML/AI features, essentially serverless platforms capable of ‘understanding’ your data and offering the best solution for your problem, and I can see these kinds of platforms becoming more and more widespread, due to the very low barrier to entry.”

Overall, data science experts predict a range of changes will occur to the industry, from the way we work, to the technology we’ll have at our disposal. After having our world turned upside down so drastically in the last 18 months however, who knows what will really be in store in the next year or two. Hopefully these predictions from leaders will allow you to stay on top of industry changes and ensure your company stays one step ahead at all times.