Enabling a digital lifestyle using data analytics and machine learning
To enhance customer experience for its 60 million customers, Globe Telecom looked into the use of machine learning to deliver real-time targeted marketing and optimised products and services, while maintaining compliance with the latest industry data regulations in the country.
Delivering personalised omni-channel customer experience through real-time analytics
The EDH delivered immediate value to
Western Union by supporting predictive analysis on structured and unstructured
data sets, at the time of transaction. Consequently, the company was able to
impact transactions in real-time and drive customer compliance in a way that
drove better conversions.
Developing a scalable data platform to support evolving IoT-based insurance propositions
The
resulting NGP enables Octo Telematics to store, process, and analyse data
generated by over 5.3 million drivers totaling 175 billion driven miles, and
that increases by over 11 billion additional data points daily. It also allows
for complete flexibility in the selection of sensors, analysis and output of
data for all insurance and automotive services.
Precision-targeted marketing through big data enabled machine learning
Traditional systems could no longer be used with the increased volumes of data coming in. Novantas also needed to process and analyse a greater variety of data, such as audio from call center recordings and unstructured text in payments transactions data.
From genomic data to precision medicine via machine learning
Inova had generated petabytes of genomic and patient data, and needed to provide a way to process that data into a single data infrastructure. It could take weeks and months to pull data together for researchers with its previous data warehouse.
Sharing real-time relevant data with utility consumers – Saving costs while helping the planet
A smart meter may generate 100 readings per day, per household. When that is multiplied by thousands of customer households for just one utility provider, the volumes of data are enormous. Data size is further compounded by related data streams including demographics data, smart appliances and sensors, weather data, consumer behavior information, and social media data
Enhancing customer journeys and improving fraud detection through machine learning
Machine learning applications enable to the
Bank to predict customer needs and determine in real time which offers to give
each customer. For example, staff can deliver real-time, localised, and
personalised interactions to each customer at the right time, with the right
content, and using the right channel.
Saving lives with big data analytics that predict patient outcomes
Cerner’s Enterprise Data Hub allows data to be brought together from an almost unlimited number of sources, and that data can be used to build a far more complete picture of any patient, condition or trend.
Harnessing data for improved customer service and smart urban planning
The EDH was used to combine network topology (GIS) data with terabytes of DSL performance (time series) and electrical line test data to grade the quality of every line in the network. This helped indicate if slow speed was a network issue or a customer issue. Using this network analysis, the probability of a successful outcome of an engineer dispatch could be predicted, reducing wasted in-person engineer visits.
How DBS transformed into a data-driven organisation
The enterprise data hub, built in partnership with Cloudera, enabled DBS to scale out more economically, experiment more, and think about the types of data in terms of billions of events rather than millions of events. It allowed DBS to answer questions before they’re asked to more
effectively engage customers and deliver better service.