Cloud Data Platform: Why It Is So Powerful. Case Studies from Cloudera

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What is Cloudera?

Cloudera is a software platform that provides enterprises with data management run on cloud or on premises. That is, a single place to store, process, and analyze all their data. This enables them to increase the value of their existing investments while opening up fundamentally new ways to create value from their data.

Cloudera products can be used to achieve better performance and prevention of proactive issues. These include data hub, data engineering, data warehouse, machine learning, and CDP private cloud.

In this article, we review three case studies by Cloudera to better understand what it is, and the kind of bottom line results it could drive for organizations. A full list of Cloudera’s customers ranging across industries can be found HERE.

  1. Deutsche Telekom
  2. Novantas
  3. Octo Telematics

The first case study is from Deutsche Telekom, the largest telecommunications provider in Europe. Deutsche Telekom supplies services to over 150 million customers worldwide. Followed by Novantas which is a leading provider of analytical, advisory services and technology solutions for financial institutions. Lastly, Octo Telematics is the world’s leading provider of telematics and data analytics solutions for the auto insurance industry.

Features of Cloudera, Cloud Data Platform
Image Source: Cloudera

1. Deutsche Telekom: Revenue Loss From Fraud Reduced By Up To 20%

Deutsche Telekom’s Challenges

The prevention of network fraud is a major challenge for telecommunications companies like Deutsche Telekom. The amount of network data that needs to be collected and analyzed is enormous. The inability to react to questionable events in near-real-time can be disastrous.

To better recognize fraud patterns, the fraud analysts at Deutsche Telekom required the ability to collect and analyze a large amount of network data. The data they amassed was stored in huge bulk. Meaning that it made access and viewing difficult, and deemed large-scale machine learning impossible.

Deutsche Telekom’s Solutions

Using the Cloudera platform, Deutsche Telekom was able to apply artificial intelligence and machine learning. This enabled the company to detect network hitches before the clients notice them and spot fraud sequence and real-time threats; putting a stop to fraud before any business impact. Apache Impala enables analysts to look into the data quickly so they can react instantly on comprehension.

Deutsche Telekom’s Results

1. Reduced fraud and potential revenue loss:

Deutsche Telekom uses the extensive high-speed electronic data processing and interactive queries within Cloudera to enhance network quality and uncover fraudulent activities. This capability alone is estimated to reduce revenue loss from fraud by about 10 to 20 percent.

2. Greater customer satisfaction and retention in revenue:

Telekom could obtain deeper insights into the needs and wants of the customer. This allows them to effectively build campaigns that delivered results. Customer turnover is reduced by five to 10 percent.

3. Improved operational efficiencies:

With the amount and quality of rich data from Cloudera’s platform, the enterprise could move faster with automations and decision making. As a result, general operational efficiency in departments had improved by 50 percent.

Image Source: Cloudera

2. Novantas: Identified Opportunities For Banks Worth More Than $15 Million For Every $1 Billion In Deposits

Novantas’ Challenge

With the market evolving to greater demands and standards, Novantas realized that they needed to catch up with technology in order to continually deliver objectives for their financial institutions’ clients.

Similar to Deutsche Telekom, they needed a platform capable of analysing huge data sets and within a speedy timeframe. In fact, they needed real-time or near real-time. On top of, they needed a platform to analyse their call center recordings to capture insights into customer’s thoughts about their products and campaigns.

Novantas’ Solution

On Cloudera’s modern data platform, Novantas developed a self-service solution for analyzing customer journey called MetricScape. The platform merges transaction data and customer accounts from over 30 institutions with data from third parties. It also applies models of machine learning to put customer scores into use. Some examples include the customer potential value (CPV), customer retention targeting, cross-sell and upsell activities. Most importantly, the platform was able to offer optimization suggestions allowing it to constantly shape itself for maximum results.

Novantas’ Results

With Cloudera powering MetricScape, Novantas had helped its clienteles implement various initiatives more critically and profitably; namely sales, marketing, pricing, and retention. In particular, Novantas was able to help a large US bank reduce its promotional spending by fifty percent. This results in higher profit margin. With a platform capable of analytics for their mass data, Novantas were also able to derive opportunities in optimizing product prices against products. Hence achieving greater value to their clients.

Image Source: Cloudera

3. Octo Telematics: Achieved 2x Business Growth Through Service Transformation Made Possible by Next-Gen Data Management

Octo Telematics’s Challenges

The company collects and analyzes data from connected cars to provide insurers with various insights. This allows them to constructively evaluate driver risk, offer accident and claim services, as well as oversee customer relationships.

Octo Telematics is able to utilize every type of data collected including contextual data, driving data, behavioral data, and crash data. This helps them forecast driving habits, improve crash notifications and response, evaluate crash dynamics, and detect fraud among others. Nonetheless, the growth of the company meant that they needed to upgrade themselves in order to stay at the forefront of their game. The need is for a data management platform that is scalable, and offers next generation capabilities in the Internet of Things (IoT), machine learning, and Cloud among others.

Octo Telematics’s Solutions

Octo Telematics worked with Cloudera Enterprise to operate its IoT solution. The powerful platform could now store, process, and analyze data from over 170 billion kilometers of driving. As well, about 400,000 serious accidents involving five million connected cars.

The talking point in this partnership being the insertion of more than 11 billion new data points to the platform from connected cars everyday. With data such as weather and traffic included, the Machine Learning powered Octo Telematics to understand context from each data point. This helps it derive greater accuracy in predictions and the resultant risk models. 

Octo Telematics’s Results

New insights backed by accurate and reliable data, transformed the way insurers cater for their customers and created new customer experiences. Some examples include the insurers contacting the authorities in a prompt manner by being able to assess the severity of accidents, reduction in time for claim processing due to faster analyzing of liability, damage assessment etc.

A Look at a Smart Factory Powered by Cloudera (Video Source: Cloudera)

Final Comments

Cloudera’s next generation data management platform represent lots of potential for businesses in different industries to uncover. Whether be it in uncovering business opportunities, for data analytics, or in optimizing services and products, Cloudera had proved to be a sure-fire. Just check out their full list of case studies conducted by them HERE.

If you would like to find out about Cloud just for Storage, read our article Here.

Unfortunately, Cloudera’s solutions are customized solutions catered for different companies. We are unable to advise on any prices or consult further in this.

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Also Read, Why NoSQL Databases are Revolutionary: Usage Cases From MongoDB

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