Poonam Singh
Poonam Singh 9 January 2018

Big Data Analytics: Real-world Use Cases

Data isn’t just for data scientists or data engineers. To continue evolving as a data-savvy organization, integrate analytics into every part of the organization. Here are some real-world examples of data analytics across a range of industries.

In aerospace: Lowering maintenance cost with big data
Splitting from its automobile manufacturer parent in 1971, this old-age aerospace giant is today best known for manufacturing powerful engines that propel some of the world’s biggest aeroplanes and ships. The industrial giant is leveraging big data to digitize its products to redefine customer value. Today, its engines are fitted with hundreds of sensors which collect tens of terabytes of data. Powerful algorithms crunch massive streams of data to generate a real-time analysis of engine performance and guide engine design, manufacture, and after-sales support. Data analysis is also helping in detecting engine problems at an early stage and lowering maintenance cost through predictive maintenance.

In retail: Brewing up a personalized customer experience
This Fortune 500 coffeehouse giant brews up a great customer experience by grinding petabytes of consumer data coming from transactions across its 27,000 stores in 75 countries and from online channels and social listening to serve up personalized experiences to its customers. It has helped the company to improve its marketing efficiency through data-driven up-sell, cross-sell, and contextual offers. For example: combining consumers' purchase history, customer habits, mobile app behavior, device location, social data and time of day helps them serve up personalized suggestions as customers arrive at their stores. The organization even uses data analytics to pick store locations, taking information such as traffic, consumer demographics, population density, income levels, auto traffic patterns, public transport stops and the types of stores/businesses in the location into account. This data-driven approach has put them on the path to revenue growth. 

In agriculture: Making farming smarter
Even the most traditional of companies, such as farm machinery manufacturers, are turning to data for their digital transformation. A US-based 180-year old machinery manufacturer embraced big data to transform itself into a high-tech data-driven digital business. By launching several big data-enabled services, the company has enabled farmers benefit from real-time monitoring of data collected from sensors attached to their own farm machinery as well as vast amount of valuable data crowdsourced from other users around the world. Accessible via an online portal as well as on its iPad and iPhone app, the services help farmers to increase production and profitability. For example, analyzing real-time data on soil types, weather, air quality, fertilizer requirements, and pest infestations, collected from sensors on fields and farm equipment, has enabled the company to deliver services that are helping farmers make better decisions such as determining the best crops to plant or the best time to spray fertilizer and increase crop yields and productivity.

With big data, asking the right questions is the key

To extract superior business value from the four Vs of big data (volume, variety, velocity and veracity), it is important that we ask meaningful questions. 

  • Ask what helpful data can we access but are not capturing? Can we drive additional value to increase profitability and efficiency from the data pools that we have?
  • Can we leverage data to make risk management and compliance reporting smarter?
  • What can we do to enable customer-facing professionals to identify opportunities to improve outcomes for clients and for the business?
  • How do we apply analytics to accelerate prudent, fact-based decision-making?
  • What must we do to deliver hyper-personalized omnichannel experiences to users?
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