This week, learn how data collaboration solves big data’s biggest problems and the negative impacts of data pollution on marketing. Understand how actionable intelligence will make for a more successful holiday season. Get the real definition of artificial intelligence (AI), its relationship to machine learning (ML), and three trends to watch out for in 2020.
by Tal Golan, contributing writer for InformationWeek.com
Data collaboration is a new approach that is shaking up big data management. It allows multiple parties to share immediate access to their data, providing a holistic view of the project and enables deeper insights. It also means reduced data fragmentation and lower management costs while simplifying the deployment architecture for IT departments. Learn how this approach solves big data’s biggest problems.
by Smriti Kataria, contributing writer for MarTechAdvisor.com
As holiday season dawns upon the marketers, marketers should aim to win the heart of customers and increase the revenue by effectively using data at hand. As a business, you can do much more than just wish for a great holiday. You can proactively position yourself for higher sales, and more revenue, by using data-fueled actionable intelligence. Actionable intelligence can yield great results for more relevant and targeted marketing, measurement, messaging, monetization, and pricing. Here are three ways to tap into actionable intelligence for a better holiday season.
by Rohit Chowdhury, contributing writer for MarTechAdvisor.com
Bigger is not always better. This is especially true when we’re talking about big data when often larger means a lack of control. Today’s marketers are being confronted with an inconvenient truth: data pollution. In fact, 96 percent of marketing organizations have had campaigns negatively impacted by data pollution. But marketers can get back on the path to unlocking the benefits that effective marketing data can offer. This article explains how.
by Dr. Ryohei Fujimaki, contributing writer for TDWI.org
This year, we saw widespread adoption and application of AI and ML. Just about anywhere you look, you can witness these approaches at work. Looking ahead to next year, Fujimaki highlights three trends to take note of as this technology advances. From AutoML 2.0 platforms, to increased privacy and regulations, to citizen data scientists, next year will be interesting to watch.
by Ron Schmelzer, contributing writer for Forbes.com
There isn’t a well-accepted and standard definition of what is artificial intelligence. And this often leads to blurred lines. This is problematic, as organizations label their technologies, products, service offerings, and projects as AI products, projects, or offerings without necessarily being the case. This informative article aims to outline the goals, definitions, differences, and relationships between AI and ML.
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