The number of ‘just add data’ off-the-shelf analytical and AI solutions is growing in availability, and with AI, intelligent automation and augmentation of power users on the business side, a big question looms – is the role of data scientist even relevant anymore? Absolutely.
The World Economic Forum’s Future of Jobs report puts data analysts and scientists, AI and machine learning specialists, and big data specialists in the top spots in its list of positions of growing job demand. There’s an evolving need for skills in technology combined with critical, analytical thinking and creativity, as well as the values and attributes which the global pandemic has reinforced – active learning, resilience, flexibility and stress tolerance.
As every business inadvertently becomes a technology business, so must every enterprise become data-powered – tapping into the continuous flow of data to learn, improve, and optimise. But while the demand is there, the talent is missing. Almost three in four businesses say they lack the talent to complete AI and data science initiatives. So for those who want in, there’s a huge opportunity to make your mark – here’s how.
You do the math
No doubt about it, AI and smart machines are doing more of the heavy lifting of data science and analytics. But a deep affinity with algorithms and mathematical logic, even when technology evolves into new areas (like deep learning and reinforcement learning), is vital. Data masters need to understand what lies beneath the surface if they’re going to make informed decisions about which approaches and tools will work best for the problem at hand.
Technology is your friend
Ultimately, it’s about creating outcomes by the data-powered enterprise. Technology is only a way of achieving that. But it is technology that brings us a surge in real-time data points from so many more sources. Technology is what gives us the means to collect this data, to store it, integrate it, and analyse it. It enables us to visualise insights at any point of action and take intelligent, automated action – so embrace it.
Open your mind
A data team which can clearly comprehend the complex metrics, math and logic involved in an AI system is a no-brainer. But cold, hard data means nothing if it cannot be clearly articulated. If you don’t understand your problem, you’ll never be able to solve it. It’s not just artificial intelligence which matters – emotional intelligence will create empathy, conversational capabilities and the ability to balance the objectives of being data-powered and being human.
Get down to business
In a technology business, the best use of data is typically made far from central IT and data management. Sector or domain knowledge needs to be nothing less than substantial. Once that’s mastered, successful data scientists need to keep their ears pricked for relevant external and open datasets – and increasingly algorithms – to bring into every new project – that’s the litmus test for their industry insight.
What’s your story?
I hate to admit it, but sometimes the data scientist doesn’t get their way. Even the most imaginative, smart insights and predictions can fail to land. Why? Because beyond the data, attractive storytelling and visualisation skills are needed to tempt clients and colleagues into the data-powered journey. And the best thing? Inspiration can come from anywhere.
Don’t just do well – do good
We’ve all got that friend who thinks being immersed in data means we’re up to something suspicious. We’re not – but they still have a point. It’s up to every practitioner to seriously understand the ethical considerations of data and AI, and then live and breathe them every single day. There is no shortcut. Unsure how your organisation abides by ethical data rules? Not good enough. Get out there, figure out how others do it well, and learn from it.
Be like water
In business, adaptability is crucial. You need to be like water: ultra-agile, ultra-adaptive, and ultra-responsive. How? Through tightly integrated, multi-disciplinary teams that can rapidly bring solutions to operations. There’s no time for silos – or worse, egos. All the skills mentioned here – they’re important for the individual, but the whole team has to play ball – because specialisation is bliss, but fusion is better.