XOps Archives | Page 3 of 4

Boosting customer experience with real-time streaming analytics in the travel industry

Posted on August 17, 2019March 6, 2024 by Saurabh Singhal

A large US-based airline use case
A recent study by Harvard Business Review revealed that 60% of enterprise business leaders believe real-time customer analytics is crucial to provide personalization at scale. According to the study, the number is expected to increase to 79% by 2020.

As mobile-first becomes the driving force for customer experiences, airlines are battling it out to make every customer journey personalized and customized in real-time. With every customer choosing different channels to interact at different points in their engagement journey, the challenge for airlines is to tap all these points of interaction to create an experience that’s personalized and relevant.

As passenger data becomes more readily available, airlines can have a granular insight into individual travelers to create tailored services for specific groups. Real-time use of this data can boost revenues, improve customer satisfaction, and enable proactive resolution of issues raised with the contact center.

Did you know?

36% of consumers are willing to pay more for personalized experiences
Nearly 60% of consumers believe that their travel experience should deploy the use of AI and base their search results on past behaviors and personal preferences
50% of global travelers say that personalized suggestions for destinations and things to do encourage them to book a trip

Real-time analysis of weather impact on New York City taxi trips in minutes

Posted on March 18, 2019November 25, 2024 by Saurabh Singhal

In this post, we will see how easy it is read data from a streaming source, apply data transformations, enrich data with external data sources and create real-time alerts in minutes with Gathr.

We will use the drag and drop interface and self-service features of Gathr to build a streaming pipeline (image 1) to analyze the impact of weather conditions on New York City taxi trips. This pipeline can be accessed and run on Gathr.

We will analyze two aspects; impact of weather conditions on the taxi trip (time taken to pick-up and drop-off the rider in co-relation to distance traveled), and the mode used to make payments (cash or card) to create alerts for cash payments beyond a set threshold.

Why Apache Spark is the right way to get a real-time customer 360 view for your business

Posted on January 6, 2019November 25, 2024 by Gathr

A survey by Bain & Co. reveals that more than 89% of organizations believe that customer service plays a critical role in staying ahead of the competition. The key to transforming customer experience is having a consistent, complete, and real-time view of customers across systems and channels.

As customers interact with businesses from multiple devices and platforms, companies have huge data available from various sources like website analysis, search results, engagement applications, CRM systems, etc. Customers expect immediate responses to their needs with real-time relevance at every point of engagement.

One of the biggest challenges that any organization faces is having a unified view of their customers to understand what they want, at the right moment. While huge amounts of data flow into the system from multiple sources, often, the data is in silos, making it difficult to stitch it all together to create a complete picture of the customer.

How modern data science is transforming anomaly detection

Posted on December 13, 2018November 25, 2024 by Gathr

Real-time anomaly detection has applications across industries. From network traffic management to predictive healthcare and energy monitoring, detecting anomalous patterns in real-time is helping businesses derive actionable insights in multiple sectors.

However, as data complexity increases, modern data science is simplifying and streamlining traditional approaches to anomaly detection.

How can today’s enterprises ride the modern data science wave to effectively address the evolving challenges of real-time anomaly detection? And what are the key differentiators businesses must look for, to identify a platform that meets their needs?

Let’s explore how modern data science is transforming anomaly detection as we know it.

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Why Apache Spark is the Antidote to Multi-Vendor Data Processing

Posted on August 29, 2018November 25, 2024 by Gathr

The big data open source landscape has evolved.

Organizations today have access to a whole gamut of tools for processing massive amounts of data quickly and efficiently. Among multiple open source technologies that provide unmatched data processing capabilities, there’s one that stands out as the frontrunner − Apache Spark^TM.

Apache Spark is gaining acceptance across enterprises due to its speed, iterative computing, and better data access. But for organizations grappling with multiple vendors for their data processing needs, the challenge is bigger. They’re not just looking for a highly capable data processing tool, they’re also looking for an antidote to multi-vendor data processing.

Spark provides several advantages over its competitors that include other leading big data technologies like Hadoop and Storm. Enterprises have successfully tested Apache Spark for its versatility and strengths as a distributed computing framework that can handle end-to-end needs for data processing, analytics, and machine learning workloads.

Let’s find out what makes Apache Spark the enterprise backbone for all types of data processing workloads.

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New Approaches to Real-time Anomaly Detection for Streaming Data

Posted on August 16, 2018November 25, 2024 by Gathr

Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains. Be it detecting roaming abuse and service disruptions in the telecom industry, identifying anomalous employee behavior that signals a security breach, or preventing out-of-pattern medical spends in incoming health insurance claims; big data anomaly detection has innumerable possibilities.

Anomaly detection has traditionally been driven using rule-based techniques applied to static data processed in batches, which makes it difficult to scale out as the number of scenarios grow. Modern data science techniques are far more efficient. Complex machine learning models can now be built using large amounts of unstructured and semi-structured data from disparate sources including business applications, emails, social media, chat messages, voice, text, and more. Moreover, the massive increase in streaming time-series data is leading to a shift to real-time anomaly detection, creating a need for techniques such as unsupervised learning and continuous models.

Following are some examples of how leading enterprises are using real-time anomaly detection to gain deeper insights and to swiftly respond to a dynamic environment:

Real-time Anomaly Detection Use Cases Across Verticals

Explore the new paradigm of anomaly detection in streaming data: Download Guide to Real-time Anomaly Detection for Enterprise Data

Big Data Trends for 2018

Posted on April 6, 2018November 25, 2024 by Varun Shakya

Data is a powerful corporate asset that enterprises are now beginning to fully harness. Enterprises are looking to derive breakthrough value through investments in cloud-migration, data lakes, in-memory computing, modern business intelligence, and data science technologies.

Following predicts represent views of multiple Fortune 500 companies that are actively investing to transition into future-ready data-driven real-time enterprises in 2018.