‘Big data’ is an expression used to describe complex data sets that result from the digital footprints of technology-enabled human-to-human, human-to-machine, or machine-to-machine interactions. One of the main reasons big data is gaining in popularity is that it promises to create value – either through efficiency or business model innovation.
On one hand, businesses use big data for incrementally improving and optimising current practices and services. On the other hand, new products and business models based on big data can prove to be disruptive innovations. Our research suggests that many businesses are developing new business models – data-driven business models – designed to create additional value by extracting, refining and capitalising on data. Such innovation is notoriously difficult – particularly for large firms that have to contend with ingrained structure, culture and traditional revenue streams.
Big data is already creating value for some very large companies and some very small ones. Established companies in a number of sectors are using big data to improve their business practices and services and, at the other end of the spectrum, start-ups are using it to create a whole raft of innovative products and business models. Therefore, manufacturers that lack competitive big data technology will increasingly find themselves outpaced by the better-informed, quicker businesses that excel in analytics. Empirical evidence suggests that companies relying more on data-driven decision-making are performing better in productivity and profitability.
Asset-heavy manufacturing makes, sells and leases its products and also provides maintenance and repair services. Its products contain sensors which collect vast amounts of data, allowing the companies to monitor assets remotely and diagnose any problems. If this data is combined with existing operational data, advanced engineering analytics and forward-looking business intelligence, the company can offer a ‘condition-based monitoring service’, able to analyse and predict equipment faults.
For the customer, unexpected downtime becomes a thing of the past, repair costs are reduced and the intervals between services increased. Intelligent analytics can even show them how to use the equipment at optimum efficiency. OEMs and dealers see this as a way of growing their parts and repairs business and increasing the sales of spare parts. It also strengthens relationships with customers and attracts new ones looking for service maintenance contracts.
In a completely different sector, consumer goods (CG) organisations were – and still are – driven by mass manufacturing and globally dispersed supply chains. This creates a gap between the manufacturer and the end user, causing limited opportunities for product and service personalisation, up-scaling of local businesses and the development of user-driven products for local markets. In particular, the industry is experiencing a great change towards the use of digital services, internet of things, combined with big data and realtime data analytics.
Capitalising on this data explosion is increasingly becoming a necessity in order for CG businesses to remain competitive, and is a modern twist to the old adage ‘knowledge is power’. For today’s CG businesses, effective data utilisation is concerned with not only competitiveness but also survival itself. CG organisations that fail to align themselves with data-driven practices risk losing a critical competitive advantage and market share. Those that use data effectively could transform the scale, location and nature of manufacturing, improve the interaction with end users and build an inclusive model of production and consumption.