Artificial intelligence, Engineering, and Data Science used to be disparate disciplines with little overlap, but now these specialist domains are rapidly converging. Altair believes that engineers hold the key to capitalising on the potential of AI across the manufacturing sector.
Nowadays product development and simulation engineering teams have access to a wealth of data that should be informing their product design and manufacturing processes. This means that engineers must be able to harness AI, Machine Learning (ML), and data analytics to support and accelerate better decision-making, reduce time to market, and design more efficient products.
The engineering industry has been occupied with the democratisation of simulation technology amongst the design community over the last decade, but we are now seeing the emergence of a new democratisation drive – that of machine learning. If history can teach us anything, it’s that technology democratisation requires a multi-functional team to become successful. What we are seeing is that the optimal approach to scaling data science is matching five domain experts/engineering data scientists with every data scientist.
Who better to come up with the use cases than the people designing these products and who better to verify, scale, and operationalise these use cases than the experienced data scientists? How often have we heard data scientists complaining of spending too much time on data profiling and reporting? Why not give the domain specialists the power and tools to solve these challenges and give your data scientists the freedom to explore niche custom model development? This way you can leverage the advantages of a democratised solution and provide people closer to the business pain with the tools to solve it while ensuring control and lineage.
The best part about the engineering data scientist movement is that companies don’t need to search for them. They’re an untapped analysis resource inside an organisation, that with the right structure, can provide insights that otherwise wouldn’t be found. We have all read the articles and seen the statistics emphasising how revolutionary and era-defining AI can be. At the same time, given their existing capabilities, most engineers will find that embracing it is a small step rather than a giant leap.
By nature, engineers are curious and thrive on solving problems. Ultimately, engineers are motivated by a practical desire to build something better. Instinctively, they will be drawn to tools that can help achieve this goal like they always have done with the principles of established engineering techniques such as experiment design, as well as modern simulation and optimization.
To provide a tangible example: Rolls Royce has led a cultural transformation in their organization. To date, they have logged over 78,000 hours of training on drag and drop, self-service tools. Their suite of courses included introductions to data science, AI, ML, coding, and digital culture and ranged from ‘bitesize’ 20-minute sessions to extended fully certified training programs. This means that they have now successfully trained 20,000 employees in the last two years. This has paved the way for engineers to get started with data science-led projects and see success with those projects.
McKinsey estimates that AI will add $13 trillion to the global economy over the next decade, yet companies are still struggling to scale up their AI efforts. The difference between the winners and losers in this transformation will be determined not by whether you have implemented AI, but by how you have, and who you have involved in the process.
Register for our 3-part webinar series: Data Science and Practical AI for Engineers. This series contains everything you need to know about getting started with data science at scale. It has been designed by engineers for engineers and will be presented by technical experts with case studies so you can see how others have implemented AI successfully.
