Big data analytics and the cloud: a smarter, more connected future ahead
By leveraging the burgeoning infrastructure of the cloud, big data analytics can deliver big benefits to business and society alike.
An estimated 2.5 quintillion bytes of data are now produced every day.¹ The pace at which data is gathered and stored has grown so fast that the best estimates say 90% of the data in the world today has been created in the past two years. But much of this data lies dormant: only 0.5% of it has ever been analysed,² which means that big data analytics retain huge potential to deliver a wide range of benefits to specific industries and society at large.
Comprising networks of data centres that use the internet to supply services from email and social networks to data storage and analysis,³ the cloud is increasingly accessible. Amazon Web Services, for example, has cut its prices 55 times since 2006.⁴ Thanks to cloud-based computing technology, organizations can now hire massive number-crunching capacity to analyse data whenever they need it, putting ‘big data analytics as a service’ among the fastest-growing cloud-based services.⁵
As the Internet of Things grows exponentially,⁶ cloud platforms will be essential in connecting devices and hosting related applications and data. Among respondents to a 2016 survey by Right Scale,⁷ private cloud adoption rose from 63% in 2015 to 77%. Major players from a range of industries – Bosch, Ericsson, Cisco, Intel and Amazon among them – are entering this growing space and vying to develop its most complete proposition.⁸
Applications in industry
The benefits to industry of cloud-based big data analytics include:
- New products and services
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
- Better utilization of existing assets and inventory monitoring
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
- Improved diagnostics and predictions
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
- Greater customization of products and services
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.
- Improved workforce productivity
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
The cloud has some wider applications beyond big data analytics that can:
- Improve operational efficiency
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%.
- Protect data privacy
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.
Unlocking value to society
The cloud and big data analytics are already delivering positive societal benefits – sometimes in unexpected ways. A vivid example occurred in 2009, when Google had some success in identifying in almost real time where the H1N1 flu outbreak in the United States had spread by using a complex algorithm that looked at correlations between Google searches made by people when a flu outbreak had occurred in their district in previous years.¹⁴
They can help with some of the biggest issues facing society today, including rising healthcare costs, crime and environmental conservation. Often, these benefits will be the straightforward result of industry initiatives that have a commensurate societal benefit, e.g. improved asset utilization reducing costs as well as CO2 emissions. Elsewhere, these benefits will need innovative private or public bodies to find ways to use data to address societal problems.
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.
As the threat of cybercrime rises and the importance of the cloud grows, the importance of safeguarding it increases. For providers, cybersecurity will be crucial to maintaining customer trust that sensitive and valuable data – kept on servers the customers cannot access themselves – is stored correctly, securely and privately. It will be crucial, too, for policy-makers charged with ensuring public safety.
Sometimes, government may also need to step in to ensure potential benefits are not left behind as ‘trapped value’, as our final example illustrates.
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.
From our DTI research, we have identified several technologies (3D printing, artificial intelligence, autonomous vehicles, big data analytics and the cloud, the Internet of Things and connected devices, and robots and drones) that are having major impacts across the 13 industries analysed to date. This article is one of a series looking at how each of these technologies is transforming business and wider society.
1 IBM, “Bringing big data to the enterprise”.
2 Regalado, Antonio, “The data made me do it”, MIT Technology Review, 3 May 2013.
3 The Economist, “The sky’s limit”, 17 October 2015.
4 Migliorini, Paul, “Amazon’s tech predictions for 2017”, The Australian Business Review, 23 December 2016.
5 Forrester Research, TechRadar Cloud Computing, Q4, 2015.
6 Gartner, Gartner says 6.4 billion connected “things” will be in use in 2016, up 30 percent from 2015 [Press release], 10 November 2015.
7 RightScale, State of the cloud report, 2016.
8 Davies, Jamie, “Bosch boosts enterprise IT credentials with IoT cloud launch”, Business Cloud News, 10 March 2016.
9 Rolls-Royce, Engine Health Management, 2016.
10 Ola, “Rolls-Royce: Internet of Things in Aviation,” Harvard Business School, 22 November 2015.
11 GE, GE announces Predix Cloud – the world’s first cloud service built for industrial data and analytics [Press release], 5 August 2015.
12 Wheatly, Malcolm, “Underground analytics: the value in predicting when an oil pump fails”, Data Informed, 29 May 2013.
13 Popper, Nathaniel, “The Robots are Coming for Wall Street”, The New York Times Magazine, 25 February 2016.
14 World Economic Forum and Accenture, Digital Transformation of Industries: Healthcare, 2016.
15 Ginger.io website.