This week, we’ve gathered articles that provide practical advice on topics like how to protect your database from marketing automation fatigue and how to operationalize your machine learning projects. We also share how CMOs should take on innovation this year and why machine learning and artificial intelligence implementations are taking longer than expected. And finally, we learn about the must-have qualities of big data visualization tools, and which tools have them all.
Marketing Automation Fatigue Might be Destroying Your Database. Here’s How to Stop It.
By Jonathon Pavoni, guest post for Lityx.com
Marketing automation is a necessary tool for every industry. It is an extremely powerful approach that helps teams of all sizes foster personalized and highly relevant conversations with thousands of people simultaneously. But if it is not managed carefully, it can lead to list fatigue and message saturation. Here are some practical and actionable ways to safeguard your marketing database as you embark on marketing automation tactics.
How to Operationalize Your Machine Learning Projects
by Jessica Davis, contributing writer for InformationWeek.com
Operationalizing machine learning projects is one of the top concerns of IT leaders. There still can be a big gap between knowing that you need to do it and figuring out how to do it in a way that is meaningful for your business. But the same tried-and-true best practices you’ve used for other IT projects can guide you on these new technologies. Erick Brethenoux, a research director at Gartner, breaks down the process.
How CMOs Should Take on Innovation in 2019
by Matthew Mobley, guest writer for MarTechAdvisor.com
There’s a marketing myth that says innovation equals technology. Over the past several years, CMOs have felt seismic pressures to continually innovate in order to compete for new consumers across emerging channels and mediums. And while technology has a role, it is not the answer to innovation in the marketing organization. Innovation does not equal technology. And a platform does not equal strategy. When it comes to innovation, the CMO should first focus on data-driven consumer strategy, then outcomes and finally, technology.
AI, Machine Learning, Data Science: What Enterprises Are Doing
by Jessica Davis, contributing writer for InformationWeek.com
In a recent session titled “The Future of Data Science, Machine Learning, and AI,” Gartner VP Svetlana Sicular provided a perspective on where the industry is in terms of implementation, where things are going, and how soon they might get there. Things are taking longer than IT leaders may have expected. But they’re not giving up. AI and machine learning are the top technologies that will drive game-changing transformation. Learn about some of the reasons why things are taking longer than expected and what Sicular believes is the right path forward.
7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them
by Hiral Atha, contributor for KDNuggets.com
53 percent of employees say that organizing data for easy viewing takes manual effort. A good data visualization tool should help you take care of that. High-quality visualization tools are crucial for your data analytics strategy. A tool that can present the clearest, interactive and accurate visual reports can help you make better decisions, make better plans, and track your KPI’s. Here are seven qualities to look for in a data visualization tool, along with ten tools that possess these crucial qualities.
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