“Data will talk to you if you’re willing to listen”— Jim Bergeson.
Few can dispute that.
However, the challenge comes when data transforms into bundles and stacks of unorganized and unstructured data sets. The challenge comes with listening to big data and making sense of what it says.
With big data, the conversing data becomes loud and noisy. You don’t hear the voice; you hear the cacophony. This is where organizations struggle.
And, amidst a struggle, you look up to the leaders to see how they are rising to the challenge. You observe, you learn, you implement and you adapt.
This is the first article of my “Under the Spotlight” series, where we will look at how leading organizations are leveraging big data and analytics, filtering out white noise from the cacophony in the process—to closely follow and benefit from what data has to say.
These organizations are spaced among different industry verticals, including aerospace industry, sports industry and life sciences industry, along with government agencies.
Airbus Leveraging Big Data and Analytics to Improve Customer Experience
Airbus has been a global leader in the aerospace industry for the last four decades, specializing in designing and manufacturing of aerospace products, services and solution.
Operating in a complex and highly-competitive industry means that Airbus has to be at its best in terms of efficiency, productivity and innovation to deliver an unmatched service experience to their customers. Big data and analytics are helping the company in that respect.
Using the IBM InfoSphere Data Explorer, Airbus integrates data discovery, navigation, analysis and contextually-relevant view of more than 4TB of indexed data, that is spread across different business units. All this data is then centrally accessible for people working in the service department, equipping them with valuable information to execute timely airline maintenance programs.
Leonard Lee, the vice president and head of new business models and services at Airbus Group, said in a recent interview, “We have tons of data. An aircraft is a very talkative machine. It produces petabytes of data. And today, in general, in aerospace industry, only two percent of that data is used in any constructive way. So, our plan is to leverage all of the richness in that data, to help improve our customer experience by driving initiatives like predictive maintenance. This way our customers can get airplanes back in the air as quickly as possible.”
This one application of big data and analytics has accounted for savings of more than $36 million for the company in a single year.
Another way the company has been leveraging big data and analytics is to improve lead time in the production of aerospace units, so that the customers can be facilitated with deliveries in due time.
Each shop floor has been empowered with digital solutions, which allow workers across different production units to update the status of a project in real time. This data can then be communicated and shared between workers, positioned across different shop floors, reducing paperwork inspections and inducing a proactive production approach. The newest Airbus rotorcraft, the H160 helicopter, has been built on this newly erected production model.
Lee further expanded on the company’s strategy, adding, “What we are trying to do with our digital transformation effort, is to build digitally-enabled, data-driven business models. We are working with strategic partners like Palantir, and others, to capture more value across the value chain, by having layers of analytics, machine learning and artificial intelligence, so that we can build solutions that would help us improve our customers’ experiences.”
NFL Teams Can Now Leverage Big Data and Analytics to Improve Performance Levels
In April, the NFL Players Association (NFLPA) entered into a partnership with WHOOP, a company that manufactures wearable devices. The objective of the partnership was to equip the athletes with a technology that could help them track their health and performance levels.
The WHOOP device can be strapped on an athlete’s forearm, wrist or bicep, giving insight into his body while he trains or recovers.
- It allows coaches and players to know how much sleep they receive and compare it to how much sleep they should be getting.
- It measures an athlete’s muscle strain levels, which can then be leveraged to reduce muscular injuries and recovery times.
- It allows coaches to measure the workload of every athlete separately, so that they can design training sessions accordingly.
Bioethicists Katrina Karkazis and Jennifer Fishman, commented on this innovative initiative, in an article, saying that if applied judiciously, responsibly, and ethically, biometric data technologies in professional sport have the potential to reduce injuries, improve performance, and extend athletes’ careers.
Isaiah J. Kacyvenski, a former American football linebacker, welcomed the idea, saying“In the end, playing football is a job for us—athletes. As a football player, I always thought of my body as a business. The ability to create more value for the job you do, should be acknowledged.”
NFLPA has announced that the data and the insight from it would be in the sole ownership of athletes and they could use it or sell it, in any way that they may want. The announcement further entailed the use of the device during a match as prohibitive.
Big Data and Analytics May Speed Up Finding Cure for Cancer
Business, sports, and even the life sciences industry is finding a use for big data. The life sciences industry is all about researching and expanding our understanding of the human body, to keep it healthy and disease-free.
A human body is a complex system of cells, tissues and organs, with various biological molecules forming the fabric of this complex system. This system is then regulated by sets of genes, which are present in our DNA.
To put these details and complexity into a quantitative context:
- There are 37.2 trillion cells in our body.
- Each cell is made up of 7 billion atoms.
- There are around 20,000 genes in a single cell.
Expand on each of these details, and you will come across petabytes of data.
This shows the extensive amount of data, which life scientists have to manage and decipher on regular basis.
But, the industry is up for the challenge. It believes big data and analytics can help to speed up the process of finding cure for various diseases, even something as complex as a cancer.
“By leveraging big data and analytics, we can begin understanding the basic facts about how tumors grow, how heterogenous tumors are and what are the targets, so we can create new drugs that work for particular tumors with particular genomic signatures,” said Robert Grossman, the principal investigator of the project Genomic Data Commons, in an interview with Chicago Inno.
What is Genomic Data Commons all about?
The project, Genomic Data Commons, is about making cancer data available to researchers worldwide, so that they can contribute to the findings and help speed up the search for cancer treatment. The data repository is housed at University of Chicago and is one of the largest open access repositories in the world.
“On the research side, the majority of researchers in cancer, I think, find the amount of data frustrating,” said Grossman. “They want to use all available data but to set up an environment, to manage it, keep it secure and compliant—the process is just overwhelming. Our role is to bring together the large public research data sets to consistently analyze them and make it available in a digestible form to the research community to accelerate the pace of research.”
The project was launched a year ago and the team believes that over the next six to nine months, they would be well resourced to make announcements regarding discoveries made through the use of GDC.
Government Agencies Leveraging Big Data and Analytics to Ensure Safety of Citizens
One of the primary roles of government agencies is to collaborate and communicate with each other to ensure the safety and wellbeing of citizens.
U.S. government agencies, both at federal and state level, have always worked hard to make sure that they deliver on this responsibility. And now, they are leveraging big data and analytics to reinforce their efforts and strategy.
Data and analytics is not a new domain for government executives. However, as the volume of data rises, while budgets get strained, the challenge is to use big data and analytics solutions that give faster and clearer insights for agencies to respond proactively and quickly. An example of one such deployed solution is SPATIOWL by Fujitsu. The platform gathers traffic movement and transportation-related data that comes from sensors installed in urban areas. This data can then be used to identify accident hotspots—areas where there is increased passenger and vehicle movement—so that preventative measures can be taken in advance, to mitigate accident risks.
Another example is the use of big data and analytics to anticipate natural disasters and improve disaster management activities. Government agencies are leveraging the use of technology to acquire high resolution satellite imagery and seismic data. With the help of analysis offered by machine learning and artificial intelligence, this data is then combined with historic information to identify patterns and predict natural disasters. Moreover, platforms are being integrated with disaster predictive algorithms, that allow government agencies to monitor in real time the different delivery channels that make disaster management services accessible. As a result, the standards of delivered services are being improved.
The world of big data and analytics is challenging but insightful. It provides actionable insights to help businesses and organizations automate their process, gain an insight into their target market and optimize their existing operations for improved productivity and efficiency.
But, only if one is willing to embrace its cacophonic nature. And, based on these examples, only few can dispute that.
About the Author
Ronald van Loon is an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He contributes his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.