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3 Key Infrastructure Considerations for Your Big Data Operation

In this special guest feature, AJ Byers, President and CEO, ROOT Data Center, suggests that if your organization is launching or expanding a Big Data initiative, it would be wise to keep the needs of real estate, power and up-time top-of-mind. Whether your Big Data operations ultimately reside on-premises, at a colocation data center, or in the cloud, infrastructure that is flexible, scalable, sustainable and reliable is ground zero for ensuring its success.

3 Ways Marketers Should Use Data Science to Skyrocket Marketing Results

In this contributed article, journalist Olivia Ryan provides 3 ways marketers should use data science to skyrocket marketing results. The purpose of “data science” is to solve complex problems that organizations face by means of analytics. In digital marketing, data science becomes critically important.

Data Capture Challenges No One Wants to Talk About

In this contributed article, technology writer and blogger Kayla Matthews discusses 3 data capture challenges familiar to IT professionals and data scientists. There are numerous challenges IT professionals and data scientists encounter when striving for accurate and efficient data capture. But unfortunately, many of these challenges don’t always get the level of attention and discussion they deserve, despite having a significant impact on data capture.

3 Reasons Why Machine Learning Should Matter to B2C Businesses Too

In this contributing article, Pratik Dholakiya, Co-Founder, CMO of E2M, provides 3 reasons why machine learning should matter to business-to-consumer (B2C) businesses too. There is a lot of talk about how machine learning, AI, and big data can be used to help B2B companies improve their efforts. While there’s no disputing this, there is a large misconception that B2C businesses cannot be helped in the same way. This is, of course, quite false.

Developing a Deeper Understanding of Apache Kafka Architecture Part 2: Write and Read Scalability

In this contributed article, Paul Brebner, Tech Evangelist at Instaclustr provides Part 2 of his two-part series on Apache Kafka. In Par 1 we gained an understanding of the main Kafka components and how Kafka consumers work. Now, we’ll see how these contribute to the ability of Kafka to provide extreme scalability for streaming write and read workloads.

5 Business Intelligence Mistakes That Can Cost You Dearly

In this contributed article, Nilam Oswal, Software Analyst at SoftwareSuggest, discusses 5 important areas where mistakes that hinder the successful implementation of BI solutions. This estimated $14 billion industry is making inroads in various organizations, but there are still challenges that go with it. According to Gartner, “There still remains a 70% likelihood that a BI project will fail to meet expectations.”

How We Implemented (and Secured) a Big Data Microservices Infrastructure

In this contributed article, Tobias Gurtzick, Security Architect, and Dr. Sayf Al-Sefou, CTO at Arvato Infoscore, discuss the pretty lofty big data challenge stemming from their company’s monolothic architecture, and how they got through it (with particularly data sensitivity being in the financial services space and thus subject to tighter regulations).

2018 Will Bring Convergence of AI and Process Automation

In this special guest feature, Alain Gentilhomme, Chief Technology Officer at Nintex, explores the top AI and process automation trends we can expect to see throughout the rest of the year. One of the most useful developments in artificial intelligence this year will be the convergence of artificial intelligence and process automation.

Developing a Deeper Understanding of Apache Kafka Architecture

In this contributed article, Paul Brebner, Tech Evangelist at Instaclustr provides an understanding of the main Kafka components and how Kafka consumers work. The Apache Kafka distributed streaming platform features an architecture that – ironically, given the name – provides application messaging that is markedly clearer and less Kafkaesque when compared with alternatives.

How Big Data and Behavior Prediction are Shaping Automotive Dealerships

In this special guest feature, Johannes Gnauck, CEO and Co-Founder of automotiveMastermind, discusses how big data and behavior prediction are shaping automotive dealerships. Through the use of big data and behavioral analytics, it’s possible to precisely target who is ready to buy and create personalized, micro-marketing campaigns designed to increase the probability of completing a sale.