As I discussed in my keynote presentation at Strata + Hadoop World in San Jose last week, companies need to do more than merely report on business results. There are substantial advantages to being able to make decisions at the speed required to respond to events in the moment. In fact, real time is at the foundation of many transformational applications. Let’s take a closer look at what real time really means, and why real time is required across the entire process.
The reality is that real time is required across the entire process: it begins at the time data is collected, and continues until the business action is taken. If we can compress that time-to-action timeframe, it can form the foundation for some truly foundational applications.
Let’s take a look at some examples. These companies use high-frequency decisioning applications to make small, automated adjustments to:
is one of the fastest-growing advertising platforms in the industry. With nearly seven billion transactions per day, Altitude Digital is able to select, in real time, the best video advertisement to play at the right time for the right person.
is a $2 billion hospital chain that’s moving to an event-based platform that handles real-time patient data, medical histories, and other data to improve patient care.
is improving yield management by leveraging real-time machine sensors for vibrations, heat, etc. The company can determine quality problems and correct issues must faster with this real-time analysis.
is a $23B multinational company based in Houston, and is a leading worldwide provider of oil equipment, components, and services. NOV is using the MapR Platform to perform real-time analysis to optimize oil and gas drilling and production.
leverages big data to identify potential fraud when an American Express Card is used anywhere in the world. Their platform protects $1 trillion in charge volume every year - determining in less than 2 milliseconds if the charge is fraudulent or not.
These applications are just a few examples of the real-time applications across industries that are transformational. Whether you refer to them as high frequency decisioning or operational agility is not important; the key question is, how do you enable these applications? First of all, it requires a new approach.
Traditionally, we’ve taken an “application first” approach: you start with the application and determine the data requirements. You then prepare the data into specialized schemas to serve the application. Each of these applications has their own dedicated silo, and the result is that you have a proliferation of silos. In fact, the average company has hundreds of data silos throughout their organization. Gartner refers to this as the biggest challenge for data management in organizations. The promise of big data is to centralize this into a data lake and bring the processing to the data.
Hadoop enables organizations to collect data into a centralized data lake.;
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