Data science, alongside AI, has been a key disruptor across multiple industries.
Heather Dawe, UK head of data at UST UK Data Practice, discusses how data science and artificial intelligence (AI) are driving digital transformation success across sectors
The pandemic accelerated a phenomenon that was already taking place across industry: digital transformation. Lockdowns and similar changes in our behaviours drove a massive increase in demand for online services and this demand is now unlikely to return to pre-pandemic levels.
In reaction to this, businesses of all shapes and sizes are striving to make their existing business models increasingly automated and digital-first in a bid to avoid being disrupted. They are also disrupting themselves, changing their ways of working using data and technology in a bid to improve their products and services, remain competitive and create new markets.
Central to successful digital transformation is the effective use of data. The personalisation of online services is a key example of how data is used to generate AI that achieves this. Such initiatives frequently strive to place the user or customer in greater control, catering to them by predicting their requirements and subsequently personalising the service to them. Data is used to train machine learning models underpinning an AI service. The AI predicts the user requirements and configures the service to these requirements.
The desire to accelerate digital transformation programmes is a large contributor to the increased demand for data scientists and data science skills within industry. In 2019 the Royal Society reported a threefold increase in demand over five years. Subsequent year-on-year increases in demand have been at least 30 per cent.
Data science and AI across industry sectors
So, what are all these data scientists doing and where are they doing it? At UST I work with Clients from a variety of industry sectors. They typically fall within the retail, asset management, banking & financial services and insurance (BFSI), manufacturing and automotive domains.
One of the fascinating things about this from a data perspective is the variation in which these sectors have so far adopted and utilised advanced analytics and AI. Asset managers for example generally use quite different forms of analysis and machine learning models than retailers.
There are also similarities across sectors. Customer personalisation is a common requirement and analytical pattern within a number of sectors including retail, insurance and banking. Supply chain optimisation has significant applications across retail, manufacturing and the automotive industry.
AI in asset management
From our perspective, asset management is among our most advanced spaces for use of analytics, machine learning and AI. In addition, they are increasingly successful in implementing analytics and AI services – processes that have been recognised by Gartner as difficult to achieve.
Asset management as a discipline has used data and analytics to inform investment strategies for a long time. As data scientists within these companies become increasingly adept with programming languages such as Python and R; sophisticated in the data science methodologies they employ; and ambitious about data they use to develop and test strategies, this trend is set to continue.
Retail
The retail sector, unsurprisingly, is relatively advanced when it comes to using machine learning and AI. Data-driven loyalty and customer reward services were introduced back in the early 2000s, and since then — due in a large part to increasing competition — data innovation for customer personalisation, among other use cases, …….
Source: https://www.information-age.com/data-science-ai-drivers-successes-across-industry-123499650/