Sentence examples for implementing data analytics from inspiring English sources

Nonetheless, the company has huge ambitions for its future, aiming to be a comprehensive business intelligence platform, implementing data analytics, social publishing and even smart paid-for promotion of social media posts using the various platforms' advertising APIs.

By implementing data analytics into their everyday marketing endeavors, Pizza Hut has created 6,000+ customer behavioral groups, empowering the brand to predict future purchases and execute campaigns at preferred times via customers' preferred engagement channels (direct mail, email, text message and more).

Therefore, new data analytics stacks have emerged as environments that provide the necessary underlying infrastructure for giving access to data collections and implementing data processing workflows to transform them and execute data analytics operations (statistics, data mining, knowledge discovery, computational science processes) on top of them.

This chapter presents innovative systems existing on clouds for designing and implementing scalable data analytics applications.

In the next two years, 6,400 organizations with 100 employees or more plan to implement big data analytics, providing ample opportunities for a new crop of startups that collect, refine and interpret data to populate the HR analytics landscape.

We are also implementing big data analytic tools to empower the processing part through Hadoop and Spark.

Furthermore, it describes the key criterias and resources requirements utilities should examine in order to select the right Big Data tools and implements given data analytics system.

Focusing on manufacturing and maintenance process of the product lifecycle, and the key technologies were developed to implement the big data analytics.

The implementation of data analytics on such datasets is commonly referred to as big data analytics.

Many companies on the current "cutting-edge" of machine learning and data analytics are implementing supervised learning, or black-box modeling.

In this section we illustrate, with a concrete example, the use of analytics as part of a workflow where models are implemented initially as hypotheses and data analytics is used to validate and refine the model.