Big data analytics and the cloud: a smarter, more connected future ahead

Case study: Rolls-Royce – tracking engine health in real time

Rolls-Royce’s Engine Health Management (EHM) system, enabled by on-board sensors and live satellite feeds generating hundreds of terabytes of data, tracks the health of its engines worldwide.^⁹ EHM is a form of predictive monitoring that utilizes real-time or post-flight engine performance metrics to flag potential threats and recommend improvements in engine efficiency. By using EHM, the Rolls-Royce Trent 7000 engine enables the Airbus A330 to be 14% more fuel-efficient.¹⁰

Case study: General Electric – providing advanced analytics and modelling through Predix

In 2015, General Electric (GE) launched Predix, a cloud-based ‘platform as a service’ that supports industrial-scale analytics for asset performance management and optimization. Predix Cloud stores, analyses and manages machine data in real time. It operates seamlessly with applications and services running in a broad spectrum of cloud environments. In the utility sector, it has enabled wind farms to generate 20% more electricity.¹¹

Case study: Apache – anticipating critical equipment failures

Electronic submersible pumps (ESPs) had been causing losses of 10,000 barrels a day for Apache Corporation, a US-based independent oil and gas company. To tackle these issues, it set up a collaborative industry effort around predictive analytics (ESP-RIFTS) to document and quantify the locations and operating conditions of more than 100,000 pumps. Working with Ayata, it looked at this data and identified 40 actionable variables to improve its ESPs. The result: reduced production losses and increased output thanks to higher overall equipment uptime.¹²

Case study: Disney – improving customer experiences

Launched in 2014, after six years of planning and $1 billion of investment, Disney Magic Bands are multicoloured RFID wristbands that allow guests to make payments, manage reservations and access hotel rooms. Linked to Disney’s guest management system, its analytics servers and the My Magic+ app, the system allows staff to deliver a highly personalized service: for example, by creating customized itineraries or having pre-ordered meals ready for collection at restaurants in the theme park. So far, the bands have been used by more than 10 million visitors and received approval ratings beyond 90%. They help Disney access rich user data, allowing it to make efficiency and process improvements that have helped Disney World increase footfall and customer spend.                                                                                                                         

Case study: Kensho – answering complex research queries

Kensho augments human capabilities to think, learn and do by combining big data and machine learning to analyse the impact of real-world events on financial markets and answer complex financial queries automatically. Kensho’s search engine automatically categorizes events according to abstract features – a process that takes just a few minutes. Generating a similar query without automation could take around 40 man-hours – a significant investment for companies whose employees are paid an average salary of $350,000 to $500,000. Goldman Sachs is Kensho’s largest investor and uses it to perform research work.¹³

Case study: Schneider Electric – using the cloud to cut costs

Cloud-based Box provides file-sharing, collaboration and other tools for working with files that are uploaded to its servers. Schneider Electric uses Box for both internal and external sharing of files because it addresses users’ needs for external collaboration and mobile access while giving IT centralized management and enterprise-grade security. Schneider has roughly 67,000 users on Box and adoption continues to increase. Currently, 20TB of Schneider’s data is currently stored on Box – none of which was previously accessible to IT. Schneider has been able to offload its on-premises file servers, cutting costs by 30%.

Case study: Genecloud – supporting medical research

Genecloud is a platform and cloud-based interface that enables researchers to engage with sensitive genomic data in a managed environment that enforces data access policies for third parties. Sensitive health and genetic data is anonymized and made accessible to researchers, who can use the information to advance personalized medicine, while ensuring individual data privacy and legal frameworks are followed.

Case study: Ginger.io – personalization in mental healthcare

Ginger.io safely and securely uses data from a patient’s everyday mobile usage (time spent on calls, text messages sent) and activity (distance travelled, sleep) to map behaviour over time and then track variations from patterns as potential self-harm risk triggers.¹⁵ The app can predict signs of depression up to two days before outward symptoms manifest. By continuously monitoring the user, Ginger.io claims it is more effective at targeting care to when the patient really needs it than regular visits to a clinic. In this way, the Ginger.io service has the potential to improve clinical outcomes while reducing healthcare costs.

Case study: incentivizing the launch of usage-based insurance

The introduction of usage-based insurance for cars clearly illustrates how unaligned incentives can derail societal gains. Our research suggests that usage-based insurance could save 160,000 lives by 2025. However, it is not being widely rolled out in countries such as the United States because the profits and costs from the service are being unevenly distributed. In a low-margin environment, car manufacturers are not mandatorily installing the telematics equipment needed for usage-based insurance because the cost cannot be easily passed onto consumers. Accurately priced insurance means lower costs to consumers, fewer accidents and reduced crash costs for all stakeholders – a win-win-win for customers, industry and society that is not yet in place.