THANK YOU FOR SUBSCRIBING
Growth Of Machine Learning In Asia And Its Drivers
By Sanjna Parasrampuria, Head – Applied Innovation, Asia, Refinitiv
It depends what you use as an indicator. If benchmarked against North America, Asia of course still has ground to cover. That said, Asian institutions are more advanced in some areas than those in Europe, such as in machine learning (ML) being core to business strategy and the projected growth in numbers of data scientists.At an operational level, the reasons for ML adoption are quite telling. Unlike popular belief that saving money is the primary motivation, Asia (like its counterparts) is driven by the desire to extract better quality information (69 percent), generating trading investment ideas (65 percent), and the desire to increase speed and productivity (65 percent). Although at a macro level, we see that the high regional growth rate, the availability of open source ML technologies and importantly, the growing population with rapid mobile/digital adoption are leading factors making ML become core to strategy in the region. Maturity Drivers of AI and ML Initiatives A push to maturity is determined by the business’ sense of urgency, data availability, and scientific expertise. Given Asia’s lagging technology adoption, as compared to North America, only when the business environment’s competition can be won using an AI/ML strategy, will the technology gain industry-wide adoption. This was seen in the case of Grab vs. Uber, and can be seen in the battle between ecommerce leaders every day. Though even if the business sees the value in implementing ML, there are a few barriers to adoption that are often missed: Data availability, data quality and data analysis capabilities. Asia has a wealth of yet untapped data. For instance, only 53 percent of the Asian respondents are using alternative data in their ML work as compared to 93 percent in the Americas. In our lab, we developed a Data Science Accelerator for financial institutions to increase the speed at which data can be turned into usable input for a data scientist. Unsurprisingly, the traction from Asia was unprecedented as it provides data science ready data saving stress on the existing talent pool. Lastly, the gap between the C-level leaders and the data scientists needs bridging. A common vocabulary around what AI/ML can and cannot do needs to be understood. Business leaders don’t have true visibility on how AI/ML are used to solve their business problems. Therefore, they lack clear articulation of problem statements in a way that can be readily turned into a useful AI/ML application by a scientist, an area that will have to mature over time. AI and ML as Part of Core Business Strategy in Asia Knowing that 76 percent of the parties interviewed in our machine learning survey are implementing ML in their core business strategy, 8 percent ahead of Europe, is encouraging. Though if you dig deeper, only 29 percent have actually deployed ML in their core business. This is likely based on the fact that we are yet to mature in ML adoption and while ML has been accepted at a strategy level it still needs to percolate down to the ground for adoption at scale. Some of this is also on account of wanting to appear as an innovation company in the perception battle. The hedge funds where innovation and investment has been traditionally focused are certainly ahead although the greater availability of tools is likely to materially level the playing field—on both the buy and sell sides. Another observation is that senior leadership need to appropriately incentivize their business managers to support ML adoption with their business lines. Real value from ML efforts needs the right ingredients and real understanding of what is possible and what is needed to make it happen. Educating this powerful segment of employees, identifying their communication gaps, and strengthening their ML/AI understanding over time is one approach. Elevating the data science team to the C-Suite level, creating a cross functional body responsible for the execution of the organization’s AI and ML projects, is another.
Real Value From Ml Efforts Need The Right Ingredients And Real Understanding Of What Is Possible And What Is Needed To Make It Happen