Businesses these days are exploring innovative ways to transform their operations effectively. They are increasingly tapping into real-time analytics to respond quickly to customers and deliver a variety of services to them. Unlike conventional data analysis, real-time analytics uses and analyzes the data as soon as it enters in the computer system. This helps organizations to gain a competitive edge and improve agility. Real-time analytics often takes place at the edge of the network ensuring that data analysis is done as close to the origin of data as possible.
Real-time analytics plays a crucial role in the acceptance rates and life-cycle development of Big Data. As it dramatically transforms the ways systems use data to envisage outcomes and suggest alternatives, companies are widely turning to this to drive innovation.
Here are 5 companies using Real-Time Analytics to enhance business efficiency.
E-commerce giant Amazon is one of the companies enabling data-driven culture within the organization. The company gleans over 2,000 historical and real-time data points on every order and leverages machine learning algorithms to find transactions with an elevated likelihood of being fraudulent. By doing so, the company’s system stops millions of dollars worth of fraudulent transactions each year. Amazon uses Big Data to automatically customize the browsing experience for its customers based on their past purchases and optimize sales.
Penn Medicine, a multi-hospital health system based in Philadelphia, Pennsylvania, developed a dashboard that leverages its electronic health record (EHR) vendor’s real-time data streams. This intended to alert respiratory and nursing staff when interventions are needed and patients may be ready to be weaned from ventilators. Leveraging real-time data streams, the data science team at the health system is devoted to improving patient outcomes through analytics. Penn Medicine’s ABC, an application dubbed as Awakening and Breathing Coordination, had minimized the time ICU patients spent on a mechanical ventilator by more than 24 hours.
Automaker giant Nissan uses Google analytics e-commerce tracking to amass detailed information about product preferences such as car category, model, and color. By assessing this information, the company’s Global Marketing Strategy division understands which vehicles are in demand, thus they can make decisions tailored for each local market. The auto company has a host of localized websites aimed at assisting consumers to determine which Nissan product is best for them. Nissan has also deployed the Hortonworks Data Platform (HDP) to power its data lake. The company developed its data lake infrastructure using Apache Hadoop powered HDP to gather all data from across the business, including driving data and quality data.
Shell, a Netherlands-based oil and gas company, developed an analytics platform based on software from several vendors to run predictive models to foresee when its different oil drilling machine parts might fail. The company used Databricks that captures streaming data through Apache Spark, to better plan when to purchase machine parts, how long to keep them, and where to place inventory items. Hosted in Microsoft Azure’s cloud, the tool helped Shell by reducing inventory analysis from more than 48 hours to less than 45 minutes, cutting off millions of dollars a year of moving and reallocating inventory.
Land O’ Lakes
Land O’ Lakes, a Minnesota, US-based food company, is relentlessly looking to optimize its pricing, better target sales, and predict future demand. To accomplish this, the company turned to data analytics and AI and brought Data to Value program. This leverages data analytics tools and different data sources to gain insights into the company’s profitability, sales call success factors, and commodity markets. The program helped Land O’ Lakes to improve its success rate by delivering the right product at the right price at the right time. The company gained US$14.9 billion in revenue in 2018.