Data Science Terminology: 26 Key Concepts Everyone Should Understand (Part I)

Data science is the theory and practice powering the data-driven transformations we are seeing across industry and society today. Artificial intelligence (AI), self-driving cars, and predictive analytics are just a few of the breakthroughs that have been made thanks to our ever-growing ability to collect and analyze data. Just as with big data (see my terminology pieces here: Part I,

Big Data Terminology: 16 Key Concepts Everyone Should Understand (Part I)

These definitions are for anyone who wants to know more about Big Data and of which they should have a general understanding. As-a-Service Infrastructure Data-as-a-service, software-as-a-service, platform-as-a-service, these all refer to the idea that rather than selling data, licences to use data, or platforms for running Big Data technology, it can be provided “as-a-service,” rather than as a distinct product.

Big Data in Sports: Going for the Gold

In the 2015 movie “Concussion” starring Will Smith, the protagonist, a forensic pathologist, investigates multiple cases of severe mental disorders among former football players to arrive at the conclusion that they were all caused by the athletes’ sport careers in the first turn, but also – and to a very considerable extent – by inefficient safety measures taken by the

How Predictive Analytics is Changing the Retail Industry

Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers. However, reports summarizing average behavior don’t provide the useful

5 Common Myths Around Virtualizing Big Data

Big data burst on to the scene a little over a decade ago. Today it is not an obscure term confined to just a handful of bleeding edge companies. It is a mainstream trend that every enterprise undergoing a digital transformation journey has adopted. The technology landscape around big data has broadened dramatically; in the early days it meant Apache

Learning from Your Data: Essential Considerations

For any organization undergoing digital transformation, a primary consideration is how to find, capture, manage and analyze big data. They are looking to big data and data science to facilitate the discovery of analytics that will enable informed decision-making. CIOs have a responsibility to provide expertise in the area of analytics, as well as an understanding of how to provide

7 Ways Big Data Is Changing Manufacturing

Though manufacturing is a somewhat bygone industry, it might surprise you to learn how much it has benefited from the use of big data. Manufacturing is evolving, thanks to its access to new analytical tools and better ways to gather information. How Big Data Is Changing Manufacturing Below are just a few of the ways big data is reshaping manufacturing

The 7 Biggest Data Trends to Watch in Finance for 2017

Big data has already heralded some massive changes in the world of finance, but new tech is pushing new trends for the industry. Anyone in the financial sector—and even common consumers—can benefit from recognizing them. Data Trends for Finance in 2017 Use these trends to direct your investments, your choices as a consumer, and your direction within the finance industry

Top 5 Reasons to Embed Analytics in Your Enterprise Apps

When is embedding analytics into your application the right option? What can it bring to my users? These are all legitimate questions for those who are less familiar with the benefits of embedded analytics and reporting. Here are some of the top reasons to embed analytics in your software applications. Context is King There are times when a traditional, enterprise-wide BI deployment does not