Those monitoring economic megatrends would do well to look at research and development (R&D). We live in an age of unprecedented innovation. Many of the world’s biggest companies and most ubiquitous technologies were unimaginable a decade or two ago. While commodities such as oil – the engine of the economy for the last century – remain important for now, the real driver of modern economies is research and innovation, and the key to successful R&D is the good use of data.
R&D Drives Today’s Most Successful Companies
Of the top ten companies by market capitalization, seven are digital innovators (Alphabet, Apple, Facebook, Amazon, Microsoft, Alibaba and Tencent). Ten years ago only Microsoft made this list, which was then dominated by oil and gas giants. The latest list also includes Johnson & Johnson, a big investor in healthcare R&D, and Berkshire Hathaway, which had built its fortune on long-term investments in innovators.
Most of today’s top global tech companies got where they are very quickly. Their products and services are in a constant cycle of innovation to avoid being outdone by their peers or by disruptive startups. Alphabet, Apple, Facebook, Amazon and Microsoft spent over $58 billion on R&D in 2017 according to PwC. It’s not just their deep pockets that have brought success – these are digital companies built on data – using data to drive their businesses forward is in their DNA.
Data has always been collected in R&D to understand and refine new innovations – the difference now is how much and how diverse this data has become. Everything from chemical pathways to physical properties to user interaction is carefully monitored and captured. Many different sensors, from highly sensitive measurement and analytical devices in labs and testing facilities to identifying tags on products to smartphones in users’ pockets all capture data which feeds back into R&D processes. Within this data lies huge insight into the best route to the optimal outcome. The world’s most successful companies are laser-focused on finding that insight in their R&D data. Others still need to get their house in order before they can start using their R&D data effectively.
Although the digital giants are masters at data-driven innovation, companies in more established industries realize they cannot rest on their laurels and are taking inspiration from these digital leaders. Automotive companies, partly driven by the threat of disruption from upstarts like Tesla and tech companies like Google, are becoming more innovative. Similarly, renewables and energy storage are challenging fossil fuel’s dominance, driving oil and gas companies to innovate furiously.
Spotting The Next Billion-Dollar Product
The most effective way to harness this data is through digital R&D, a coordinated process of taking any data involved in R&D processes – from experimental data to product images and industry standards – and presenting it in a digital format, allowing for visualization of that data and the building of predictive models. Such models help R&D departments understand the likelihood of success of research routes and inform business decisions on which products to focus resources on developing.
This has game-changing potential. Digital R&D promotes a completely new approach to innovation, allowing companies to start with a model of what they want to achieve and to use this model to accurately describe new products and how they can be produced at scale while ensuring quality. Pharmaceuticals already have sophisticated models to identify what properties drug-like compounds require to meet specific disease efficacy, helping them identify new treatments that make billions and make patient lives better.
While it’s exciting to speculate about game-changing tech, clean energy and blockbuster drugs, R&D is iterative and incremental. Seemingly small improvements reduce the cost of business and keep products ahead of the game, such as new techniques for manufacturing faster semiconductors or a new product formulation to make a shampoo’s fragrance suited to a new market or increase its shelf life. Data collected in R&D is already being used by many companies to model changes to existing products.
Data can also make R&D more cost-effective. It reduces research time by guiding research scientists to experiments with the highest chance of success and dismissing unproductive routes. Making R&D data accessible reduces duplication of research and allows new discoveries which may otherwise not have been considered. With companies spending billions on R&D, a 10% efficiency improvement means big savings. Some companies talk about R&D bringing time-to-market down from months and years to weeks.
The R&D Digitalization Dream
While R&D digitalization is a fact of life at the tech giants, many non-digital companies have not yet used R&D data to full effect, but times are changing and companies are starting to recognize that using it well can be the difference between disrupting and being disrupted.
Most are, quite rightly, starting small. It’s a big move. They are collecting data from small parts of their R&D departments and exploring what can be done with it. Then they roll out projects across departments, gradually standardize processes and introduce new technologies to allow data to be captured and shared. Over time, more data sets can be combined and more accurate and sophisticated models developed, building toward true digitalization. Techniques such as machine learning and neural networks can be applied to gradually draw out more and more insight hidden within data.
Ultimately, as companies collect more data and use it more intelligently, they can build towards a fully digital R&D capability. This will cut development times, improve and retarget products and even lead to new breakthroughs. The companies that do this well will be the ones who still make the list of the world’s biggest companies in ten years’ time.
With so much at stake, R&D needs to be done right – and at the heart of digital R&D is data.