In the age where our day-to-day lives revolve around gadgets like smartphones, smart cameras, smart-tabs, smartwatches, and smart speakers, a mountain of data gets accumulated from these varied digital sources.
Yet how is one supposed to analyze and examine this massive amount of perplexing data? This is where big data works its magic. Big data technologies and tools have emerged to help battle these challenges, making the world realize the extensive applications offered by the technology, with businesses reaping its benefits for expansion purposes.
In layman’s terms, Big data describes a large volume of structured and unstructured data that engulfs a business on a daily basis. It is primarily defined as the three Vs - volume, velocity, and variety.
You can also take a look at our blog on Big Data Analytics tools here.
What had once been a trendy notion is now a technology that considerably influences how one lives and works. Big Data gives a measure to concepts like what the consumers require and facilitates inductive reasoning. Interestingly, almost every sector leverages this technology for future planning by predicting how people will live and what they’ll buy.
We have highlighted 11 industry verticals that are adopting Big Data, how they are applying the technology, along with effective examples.
Be it in the case of cash collection or financial management, big data has been making banks more effective for each industry. The technology’s application has reduced the struggle of the customers, generating the bank more revenue and making their ultimate insights more clear and comprehensible.
Ranging from identifying fraud, simplifying and streamlining transaction processing, enhancing understanding of customers, optimizing trade execution, and facilitating an advanced customer experience, Big Data offers a range of applications.
An interesting instance of a company utilizing Big Data in this sector is that of Western Union. The organization facilitates an omnichannel approach that customizes consumer experiences by processing over 29 transactions per second and compiling all the data onto a common platform for statistical modeling and predictive analysis.
JPMorgan Chase and Co, being a large bank, generates a massive amount of data and has applied Big Data technologies, primarily Hadoop, for dealing with this data. Big Data Analytics allows the bank to generate insights for customer trends and offer those reports to their clients as well as to conduct individual examinations and generate swift reports.
When it comes to the Education industry, the data gathered from the students, faculty, courses, and results is humongous, the analysis of which can generate insights effective for enhancing the operations and working of educational institutes.
From boosting effective learning, enhancing International recruiting for universities, helping students in setting career goals, reducing university dropouts, allowing for precise student evaluation, improving the decision-making process, and enhancing student results, Big Data plays an integral role in this sector.
An excellent example here would be that of the University of Florida. The university adopts IBM InfoSphere for extracting, loading and transferring data via multiple resources, IBM SPSS Modeler in case of predictive analytics and data modeling, and IBM Cognos Analytics for analyzing and predicting student performances.
Different variables ranging from the student’s grades, background, demographics, as well as economic background help measure the assess dropout chances for the students. This aids the university in setting its policies and facilitating timely intervention for students on the brink of dropping out.
We also have companies facilitating big data services to educational institutes. An example of one such company would be Panorama Education. This is a management platform for school districts and administrators + the learning skills of students, stay updated on their progress, and enhance interaction among teachers, students, families, and the staff.
The platform’s data facilitates a holistic perspective of every student, from their attendance to their behavior in classrooms, their academic performance along with their social-emotional learning. It facilitates insights that aid in detecting at-risk students at the initial stage and aid educators in supporting students in required areas.
The hype for the traditional approaches of consuming media are slowly fizzling out as the modern approaches of consuming content online via gadgets becomes the new trend. With the enormous amount of data being generated as a result, big data has successfully paved its way into this industry.
Be it in helping to predict what the audience wants, in terms of the genre, music, and the content as per their age group, offering them insights regarding customer churn, optimizing the media streaming schedule of customers, making product updates more effective in terms of time and cost, and in contributing towards effective advertisement targeting.
An excellent example of how big data has played a hand in revolutionizing media platforms would be Netflix. The technology not only influences the series invested in by the platform but also how the series is bestowed to their subscribers. The viewing history of the user, even including the points where they have paused the video for any particular show, impacts everything from the customized thumbnails to the contents we observe on the “Popular on Netflix” section.
Yet another effective example would be that of Viacom18. The company’s big data platform is built on Microsoft Azure where it experiments with multiple upcoming technologies.
The company has been employing big data analytics for ensuring viewer retention amidst break slots between program segments by pinpointing the appropriate times to hold commercial breaks, owing to which the platform has been successful in retaining viewership even amidst commercial breaks for driving substantial revenue for themselves and the advertisers.
Big Data plays an integral role in enhancing modern healthcare operations. From reducing treatment costs, predicting epidemic outbreaks, avoiding preventable diseases, enhancing overall life quality, predicting the income gained by daily patients to arrange staffing, using Electronic Health Records (EHRs), adopting real-time alerts to facilitate instant care, adopting health data for more effective strategic planning, to reducing frauds and errors, the technology has fully revolutionized the healthcare sector.
A credible example of Big Data in healthcare is that of Mayo Clinic. The platform adopts big-data analytics for aiding in detecting multiple condition patients and improving their life quality. This analytics can also detect at-risk patients and offer them greater health control and basic medical intervention.
Yet another example is MedAware. This is an Israeli startup that is attempting to battle the disturbing trend of detecting errors in advance, which would in turn help in saving money, goodwill, and of course the lives of patients.
In a field like Agriculture, big data analytics propels smart farming and precision agriculture operations which in turn saves costs and unleashes fresh business opportunities.
Some vital areas where big data is put to work include meeting the food demand by supplying farmers with updates regarding any alterations in rainfall, weather, and factors impacting crop yield, playing a role in propelling smart and accurate use of pesticides to aid farmers in accurate decision making in relation to pesticides, management of farm equipment, ensuring supply chain efficiency, in planning when, where and how to plant seeds and apply chemicals and also in ensuring food safety by gathering data on humidity, temperature, and chemicals for examining a growing plant’s health.
Bayer Digital Farming, a Bayer Group unit, set up an application that adopts machine learning and AI for weed identification.
Farmers share captures of weeds in the app and then match the picture against a comprehensive Bayer database (having around 100,000 photos) for detecting the species. This app intercedes at the appropriate time, protecting the crops and enhancing yields.
Big Data has played a key role in making transportation more flawless and effective.
Be it in helping manage the revenue gained, managing the reputation earned, carrying out more strategic marketing, holding advanced market research, and conducting targeted marketing, Big Data has considerably influenced this sector.
Big data also plays a role in planning out the route as per the needs of the user, helping in effectively cutting down their wait time, in managing congestion and traffic control through tools like Google maps which detects the least traffic prone routes, and even in detecting accident-prone areas to boost the safety level of traffic.
An effective example of Big Data’s use in this industry would be that of Uber. The platform generates and adopts a massive degree of data of drivers, locations, vehicles, the trip from each vehicle, etc which is then examined and adopted for predicting the demand, supply, location of drivers, and established trip fares.
Yet another example can be Hipmunk, a travel booking startup that examines data of airlines, profiles of customers, social graphs, and reviews for catering search results based on the requirements of every shopper and helping in accelerating the process of booking flights. The platform provides the customers with what they require while taking care of travel accommodations, by examining all the supplied data, instead of the customers being compelled to discover these on their own.
Where are Big Data applications used?
Manufacturing is no longer the strenuous manual process it once used to be. Data analytics and technology have managed to completely revolutionize the manufacturing procedure. Big Data has managed to play a role in enhancing manufacturing, customizing product design, ensuring proper quality maintenance, managing the supply chain, and also assessing to keep track of potential risks.
Irrespective of the nation, governments face an extensive level of data on a day-to-day basis. This is largely owing to the in-depth updates they have to keep of the different records and databases of their citizens, their growth, geographical surveys, energy resources, and so on. This data is required to be examined and studied, becoming an ally for the government in its operations.
The government uses this data in primarily two areas, in its welfare schemes and in the case of cybersecurity.
In the case of Welfare Schemes, the data is used for making swifter and updated decisions in case of political programs, in detecting areas that require attention, in keeping track of agriculture fields of prevailing land and livestock as well as in overcoming national challenges like terrorism, unemployment or poverty.
In the case of Cyber Security, the analytics is deployed for tasks like deceit recognition and to ensnare tax evaders.
A useful example of Big Data’s application by the government is in the case of the Department of Homeland Security (DHS). For safeguarding security, the Department of Homeland Security (DHS) makes use of an intrusion identifying system for sensors which holds the ability to analyze internet traffic both in and out of Federal systems apart from identifying attempts of malware and unsanctioned access.
The National Oceanic and Atmospheric Administration (NOAA) is a platform that consistently gathers data through space-based sensors, land, and the sea. A big data approach is used by the platform for gathering and examining extensive data amounts to conclude the correct information.
When it comes to retail, big data plays a part in predicting emerging trends, targeting suitable customers at the appropriate time, cutting down marketing expenses, and enhancing customer service quality.
From maintaining a comprehensive view of each consumer and facilitating personal engagement, optimizing pricing to get maximum value from upcoming trends, streamlining back-office operations, and enhancing customer services, Big Data offers a wide range of applications when it comes to Retail.
Being one of the biggest Canadian retailers, the shoe and accessory company Aldo leverages big data to survive occasions like Black Friday. The platform operates on a service-oriented big data architecture, integrating multiple data sources involved in payment, billing, and fraud detection, allowing it to facilitate a flawless eCommerce experience.
Amazon adopts the Big Data gained through customers to develop their recommendation engine. The more the platform knows about its users, the better they can predict what they wish to purchase, knowing which allows them to simplify the process and persuade the user to purchase it, such as by suggesting certain products rather than making the user go through the entire catalog.
The platform’s recommendation technology is based on collaborative filtering, which implies it determines what it assumes the user wants by creating an image of who the user is and offering products that people having similar profiles have brought.
You can learn in detail about How Amazon uses Big Data through this blog.
Energy and utility platforms adopt a range of Big Data sources which include smart meters, grid equipment, weather data, power system measurements, storm data, and GIS data. This data is adopted by these platforms for the purpose of cutting down costs, fetching operational efficiencies, lesser carbon emissions, and handling energy demand extended by end consumers.
One lucrative example of Big Data’s application in the energy and utilities industry would be Google’s Superstorm Sandy Crisis Map. The map not just gathers, presents and layers weather data from varied sources, but it also incorporates video feeds from various locations, evacuation routes, emergency centers, and traffic conditions.
Yet another effective example is the nonprofit organization, Direct Relief International, which facilitates medical aid for the people impacted by civil arrest, poverty, and disaster, both in the US and across the world.
The platform partnered with Palantir Technologies with the goal of labeling at-risk populations exposed to the storm, assessing possible emergency situations, and detecting medical clinics in flood risk zones to ensure that the medical aid and supplies can be properly distributed to people requiring them and to integrate and examine various datasets like shelter locations as well as almost real-time epidemiological alerts from the Red Cross and government agencies.
You might wonder how food industry uses Big Data? Big data aids food platforms in enhancing their marketing campaigns, setting up creative and highly sought items, and enabling firms to keep track of the growth rate of their competition, control their quality and examine their decisions regarding buying and prices.
This data is also helping the owners in keeping track of factors like the product quality by concluding if the product has been modified in such as if its ingredients have been replaced, or alteration of measurements or less altered in terms of replacement of ingredients, alteration of measurement or more underlying causes such as seasonal aspects or deviations in storage method.
One example is Blue Apron, a fresh ingredient, and recipe delivery service, which adopts analytics through Looker, a big data analytics platform, for making nearly real-time decisions regarding food delivery reducing the decision-making duration by up to one day.
Yet another example is that of Starbucks. Starbucks adopts the data it gains via its mobile payment app users for tracking customer data such as individual likes and preferences, which in turn is adopted for developing pertinent marketing messages, such as an offer devised for luring a customer that hasn’t visited since some time.
These are just a handful of the effective applications of Big Data, with the technology laying its seed into innumerable sectors. Hope the blog has managed to help you understand Big Data’s application in the mentioned sectors.
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