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Cloud Computing vs. Edge Computing: What’s the Difference?

Much has been said about the mass migration towards cloud-based computing, known by most simply as “the cloud.” Organizations worldwide have shifted to the cloud for better security, scalability, and efficiency. However, much less is known about edge computing, another influential technology gaining traction. Edge computing is increasingly becoming the IT infrastructure of choice for enterprises that […]

What Is Data-Centric AI All About?

Data-centric AI (DCAI) is a new class of AI technology that focuses on understanding, utilizing, and making decisions based on data. Before data-centric AI, AI was largely reliant on rules and heuristics. While these could be useful in some cases, they often led to suboptimal results or even errors when applied to new data sets. […]

How Can Unifying Data Fabric and Data Management Help Enterprises?

Today, one of the most cutting-edge technologies is data fabric. Data fabric and data management can significantly improve business functions by connecting siloed data and making it accessible across divisions and workgroups.  Utilizing a specialized architecture, on-premises data can be shared across multi-cloud and deliver unique insights unavailable with other data management techniques. In this […]

5 Simple Ways to Improve Your Data Literacy Skills

Building and maintaining a data-driven business is the only way to ensure your long-term success in today’s increasingly digital commerce landscape. This involves more than just hiring a team of IT professionals or buying the latest data analytics tools – it requires improving your employees’ Data Literacy skills as well. Data-driven organizations must capture time-bound […]

A Beginner’s Guide to Data Modeling and Analytics

As more and more companies start to use data-related applications to manage their huge assets of data, the concepts of data modeling and analytics are becoming increasingly important. While they typically rely on one each, they are two very distinct concepts.  Companies use data analysis to clean, transform, and model their sets of data, whereas they […]

Data Science: How to Shift Toward More Transparency in Statistical Practice

Data Science and statistics both benefit from transparency, openness to alternative interpretations of data, and acknowledging uncertainty. The adoption of transparency is further supported by important ethical considerations like communalism, universalism, disinterestedness, and organized skepticism.  Promoting transparency is possible through seven statistical procedures:  Data visualization Quantifying inferential uncertainty Assessment of data preprocessing choices Reporting multiple models Involving […]

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