Five Industries That Are Being Transformed By Real-Time Analytics

In past blogs, I’ve defined real-time analytics and discussed alternative architectural approaches to implementing real-time analytic solutions. I’ve also touched on how some of our customers, such as Lufthansa, Aspen Marketing Services (a division of Epsilon), and the US Coast Guard employ HVR software in the airline, marketing, and aviation logistics industries. At HVR, we are seeing the adoption of analytics grow across a wide range of industries, and in this post, I thought we’d share some additional interesting and transformative industry use cases.

1. Retail — Most major retailers today rely on real time analytics to optimize their supply chains and ensure that their brick and mortar stores and e-commerce channels have the right level of stock on hand. Predictive analytics algorithms are commonly applied to integrated data sets continuously streamed from supplier factories and distribution warehouses, POS systems, GPS-equipped transportation vehicles, RFID chips, traffic feeds, weather tracking systems, and other sources.

Nearly all online retailers also leverage analytics to deliver personalized consumer website experiences, and some are now applying real-time analytics to optimize in-store shopping experiences at brick and mortar sites. By augmenting inventory data with in-store video feeds and mobile device data, retailers can dynamically adjust banner messages and prices on electronic display panels and deliver targeted promotions to shopper’s smart phones.

2. Banking — Large retail banks are integrating banking and brokerage transactions with historic customer data across all channels in near real-time to create what is commonly called “relationship pricing.” By gathering intelligence on all the services a customer uses across the organization, they are able to offer different tiers of prices.

Compliance is another driver for real-time data in banking. The Comprehensive Capital Analysis and Review (CCAR) mandated by the Federal Reserve is one example. It is meant to ensure that banks maintain their capital reserves properly to account for fluctuations in economic and financial stress. Banks must continuously monitor capital adequacy to ensure they can make capital distributions, such as dividend payments or stock repurchases. Because of transaction volumes, the data integration challenge is massive.

3. Utilities — Real-time data is fundamentally changing the way most public utility companies relate to their customers. Integrating usage data from millions of residential and commercial customers, as well as grid suppliers, and weather feeds enables utilities to proactively send customers advisory alerts and offer dynamic pricing promotions that optimize grid efficiency, prevent outages and help consumers save on their electricity bills by shifting usage from periods of peak demand.

Real-time visualization and predictive analytics is also being applied to grid maintenance. Sensor data is integrated with other data sources and analyzed so that utilities can anticipate when maintenance needs to be performed on transmission lines, substations, and other grid assets.

4. Shipping and Port Operations — More than 80% of global trade travels by sea, passing through ports en route to its final destination. Most major ports have hundreds of barges entering and exiting every day. A single barge can carry 1,500 tons — the equivalent towing capacity of 15 rail cars or 60 semi-trailers. With traffic of this magnitude, one delay can upset shipping schedules and jam up supply chains indefinitely with enormous economic costs. Port authorities are increasingly relying on real-time analytics to drive economic efficiency as well as protect homeland security and public safety.

By integrating shipping schedule information, destination and point-of-origin information, real-time asset tracking data, tidal and weather forecasts, and other types of data, port authorities and shipping companies are realizing significant benefits. These include increased shipping lane capacity, optimized mooring space, faster turnaround times, lower accident rates, improved port security, and better cost and revenue forecasting accuracy.

5. Law enforcement — Thirty-eight law enforcement agencies across the US are now employing modern data science techniques to help stop crime before it ever starts. The concept of “Smart Policing” is predicated on analyzing geographic patterns to uncover highly likely crime locales. Hot spot analysis incorporates information from GIS systems, social media feeds, video surveillance systems, neighborhood surveys, and national crime databases in conjunction with predictive analytics to search for trends in criminal activity, and to determine its root causes.

The success of these initiatives is impressive. The Philadelphia Police Department achieved a 38% reduction in property crime in one year. The Los Angeles Police Department (LAPD) reported that property crime rates fell 12% within six months. And in Memphis, serious crime decreased by 30% between 2006 and 2010.

 

Take a look at our product solutions to see how HVR can be used to address some of the new challenges industries are facing in data replication, migrations and real time analytics.

About Mark

Mark Van de Wiel is the CTO for HVR. He has a strong background in data replication as well as real-time Business Intelligence and analytics.

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