The pandemic has created monumental shifts in daily life, making all of us re-evaluate almost every aspect of our work and home lives.

Communication Service Providers (CSPs) are in the middle of a data-driven transformation. The current scale and pace of change in the Telecommunications sector is being driven by the rapid evolution of new technologies like the Internet of Things (IoT), 5G, advanced data analytics, and edge computing. This is opening up new revenue opportunities, use cases, and even the possibility for different types of business models within the sector, changing the way that CSPs operate.

In the final installment in the series, Vijay Raja, Director of Industry & Solutions Marketing at Cloudera shares his views on how the telecom sector is changing and where it goes next. Hi Vijay, thank you so much for joining us again. To continue where we left off, how are ML and IoT influencing the Telecom sector, and how is Cloudera supporting this industry evolution?

Comprehensive, comparative analysis of our data can be a highly time-consuming manual task for your users. But new-wave automation and machine learning (ML) tools makes monitoring, alerting and gleaning new insights a lot faster. One such example is Yellowfin Signals and the latest addition to its extensive algorithm library, called step change.

Recently, Cloudera announced the release of Cloudera CDP Private Cloud, delivering the final component of our hybrid cloud strategy. There’s nothing comparable to it in the industry. CDP Private Cloud offers benefits of a public cloud architecture—autoscaling, isolation, agile provisioning, etc.—in an on-premise environment.

Companies from every industry vertical, including finance, retail, logistics, and others, all share a common horizontal analytics challenge: How do they best understand the market for their products? Solving this problem requires companies to conduct a detailed marketing, sales, and finance analysis to understand their place within the larger market. These analyses are designed to unlock insights in a company's data that can help businesses run more efficiently.

Google BigQuery was released to general availability in 2011 and has since been positioned as a unique analytics data warehousing service. Its serverless architecture allows it to operate at scale and speed to provide incredibly fast SQL analytics over large datasets. Since its inception, numerous features and improvements have been made to improve performance, security, reliability, and making it easier for users to discover insights.

The way that people do business in Japan has radically changed as a result of COVID-19. Historically, the Japanese have been very much about face to face relationship selling where you establish relationships and build trust but now everything has to be done remotely or online. Like everyone else, the Japanese are really keen to keep doing business so they've actually embraced the remote way of doing things which has been quite interesting to watch and had some unexpected benefits.

It is hard to believe if you have had previous experience with setting up, sizing, and deploying a distributed search engine service that this is possible. Imagine how many times IT has lost valuable time spending hours trying to understand Apache Solr application requirements and map them into how to best size and deploy the Solr service. Time that is lost to Line of Business as well.