Like many AI technologies, the difference between machine learning and predictive analytics lies in applications and use cases. Machine learning's ability to learn from previous data sets and stay nimble lends itself to diverse applications like neural networks or image detection, while predictive analytics' narrow focus is on forecasting specific target variables.
Instead of implementing one type of AI or choosing between the two strategies, companies that want to get the most out of their data should combine the processing power of predictive analytics and machine learning.
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How to Use Data and Analytics to Make Business Decisions - My TechDecisions
Data has been described as the next gold rush for organizations in various industries, markets, and verticals. That sentiment can be interpreted in many different ways.
Externally, data can be used to better market to customers, keep track of their spending habits, understand their pain points, and gain insight into their purchasing decisions. Internally, data can be used to improve processes, cut through overhead, spur productivity, and make decisions.
Tech Is Transforming People Analytics. Is That a Good Thing?
New tools can help organizations manage stress and performance — but only if they’re deployed ethically.
There is no question that new technologies, coupled with the near-ubiquitous digitization of work and work-related behaviors, has the potential to help organizations monitor, predict, and understand employee behaviors (and thoughts) at scale, like it has never been done before.
A century ago, Frederick Taylor's Scientific Management laid the foundations for modern HR. His central premise was that organizations should turn their workplaces into real-world psychology labs, measuring and monitoring employees' every move in order to boost their performance and reduce their stress levels.
Choosing the Right Business Analytics Tools for Your Business or Org - My TechDecisions
There are numerous aspects of your business or organization which could be tracked, analyzed, and extrapolated on to provide valuable insights for the future. But if you’re reading this article, you likely already know that business intelligence and analytics platforms can do that. Now, your real question: how do you choose the right platform to adopt?
Just like any technology decision, the answer will require a decent amount of research. But we’ve put together this guide to help.
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ReImagine Nation ELC 2020: Data Access and Analytics in a Time of Crisis | FedTech Magazine
Federal agencies' use and analysis of data is more important than ever, with vital government services dependent on the sharing and interpreting of data. The coronavirus pandemic has spurred government to enhance how workers access and share data within and among agencies. We spoke virtually with IT leaders during the ACT-IAC ReImagine Nation ELC 2020 conference about how data access and analysis have evolved during the pandemic.
5 famous analytics and AI disasters | CIO
In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side.
Understanding your data and what it's telling you is important, but it's also important to understand your tools, know your data, and keep your organization's values firmly in mind.
Introducing the new Google Analytics | MarTech Cube
Millions of businesses, large and small, rely on Google Analytics to understand customer preferences and create better experiences for them. With more commerce moving online and businesses under increased pressure to make every marketing dollar count, insights from digital analytics tools are even more critical.
By applying Google's advanced machine learning models, the new Analytics can automatically alert you to significant trends in your data – like products seeing rising demand because of new customer needs. It even helps you anticipate future actions your customers may take. For example, it calculates churn probability so you can more efficiently invest in retaining customers at a time when marketing budgets are under pressure.
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