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SmartData Collective > Big Data > Data Mining > 7 Ways Businesses are Leveraging Hadoop
AnalyticsBig DataBusiness IntelligenceData MiningData VisualizationData WarehousingHadoopITMapReduceOpen SourcePredictive AnalyticsSentiment AnalyticsSocial DataSoftwareSQLWorkforce Data

7 Ways Businesses are Leveraging Hadoop

MicheleNemschoff
MicheleNemschoff
5 Min Read
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ImageWhen you hear about Hadoop or Big Data, you probably think of large Internet companies, such as Google or F

ImageWhen you hear about Hadoop or Big Data, you probably think of large Internet companies, such as Google or Facebook. However, the use of Hadoop is not limited to large search engines or even Internet marketing. Take a look at seven striking examples of how businesses across many industries are leveraging Hadoop to reach multiple business goals.


1. Mobile Commerce

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Big Data has had a significant effect on mobile commerce. With information on a consumer’s location, the weather, demographic information and social media comments, businesses can create highly tailored offers and landing pages on their apps. Apps can also be made to enhance the customer experience in-store, such as providing a map of the store or providing incentives to make purchases within the store, rather than having to visit the company’s website.


2. Electricity


A smart power grid relies on the operator’s ability to make data-driven decisions about the regulation, cost and usage of power. Sensors are frequently used to deliver high quantities of end-user data. The anticipation of both maintenance and fluctuation of energy demands can all be made possible with Big Data processing via Hadoop.


3. Agricultural Planning


With a job as large as feeding the world, agricultural companies depend on a certain level of stability in their crop yield and quality that only Hadoop can provide. Planting and harvesting decisions are made based on large scale data analysis pulled from informational sources such as aerial surveillance, in-field sensory networks, meteorological trends and experimental data sets. Using Hadoop in agriculture allows for computations of these sorts of data, making crop behavior far more dependable and scalable for agricultural planners.


4. Fuel Conservation


For companies whose business is transportation, fuel efficiency is a primary concern. Several factors throughout these companies’ processes contribute to their fuel usage and many of them are using Hadoop to go farther with less fuel. Tracking key factors for fuel consumption, such as driving or flying style, weather conditions, type of vehicle, route, etc., can help identify points of inefficiency to minimize both fuel usage and costs.


5. National Intelligence


The defense of national interests makes Hadoop a necessity in Government. Collecting and computing enough data in order to not only resolve existing security threats, but to also anticipate them is a task of monstrous proportions. Data sets from both classified and public origins have overwhelmed earlier technologies, making Hadoop an essential adoption for those responsible for national intelligence.


6. Drug Development and Personalization


The development of market-ready drugs has always been a long and arduous process. Pharmaceutical companies, in cooperation with healthcare providers, work to supply consumers with effective medications in a timely manner. This means eliminating drugs that perform with a low success rate and shortening the time between development and public release. Using Hadoop for data computing in the healthcare industry also increases the safety and effectiveness of medication by personalizing it to the consumer. Genetic testing, blood work, radiation screenings and more provide complex data sets that can diagnose patients more accurately and make their treatment powerful, healing them faster.


7. Financial Service Security


Financial services firms like Zions Bank depend on Hadoop to process data that can optimize fraud detection and prevention. Zions Bank uses Hadoop to analyze fraudulent financial threats in real time to minimize their impact. With the ability to compute large amounts of structured and unstructured data, financial institutions can create more comprehensive consumer profiles; and by knowing their customer’s behavior, security teams are more capable of detecting harmful activity.

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