How to Achieve Self-Service Data Transformation for AI and Analytics

Data transformation is the critical step that bridges the gap between raw data and actionable insights. It lays the foundation for strong decision-making and innovation, and helps organizations gain a competitive edge. Traditionally, data transformation was relegated to specialized engineering teams employing complex extract, transform, and load (ETL) processes using highly complex tooling and code….

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Unveiling the Power of Dark Data in Strategic Decision-Making

If you’ve never heard of dark data, you’re not alone. Setting aside the ominous name, dark data isn’t something that is inherently bad – although, in practice, it usually does end up this way. Dark data is usually unstructured data, though it can also be semi-structured or structured data that a business collects and stores but…

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KNIME Adds New SaaS Features to Automate Analytics Workflows on Community Hub

According to a new press release, KNIME, a software company focusing on user-friendly data handling, has introduced a new Software as a Service (SaaS) offering for its KNIME Community Hub. The KNIME Analytics Platform, an open-source tool, is already available for free, providing full functionality for analytics through an intuitive interface. The KNIME Community Hub…

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AI and Machine Learning Trends in 2024

As we delve deeper into the age of artificial intelligence (AI) and machine learning (ML), it is crucial to stay ahead of the curve by identifying emerging trends that will shape our future. In 2024, several key advancements are set to revolutionize these technologies, paving the way for unprecedented possibilities. One of the top AI…

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ADV Webinar: Data Integration — Newsflash: We Still Just Move Data!

Download the slides here >> This webinar is sponsored by: About the Webinar Data integration is a broad term for data movement. While moving streaming data is increasingly important, and data virtualization and data mesh architectures somewhat lessens the need, straight data movement still comprises the vast majority of cycles for data integration. Are you…

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Building an Effective Data Strategy for Edge Deployments

Data analytics and integration are the key components of building a data strategy. For organizations to have an effective data strategy, it requires the definition of measurable metrics and proper consideration of all data sources. An effective data strategy also needs to define how data can be moved from various sources to a location where…

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Webinar: Beyond the Hype – The Real Impact of AI on Business Intelligence

Download the slides here>> This webinar is sponsored by: About the Webinar Join us to learn how the user journey with BI can change for the better by integrating AI. We’ll take a look at the perspectives of both Analysts and Business Users, to understand where the practical application of AI can help in the…

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The FAAR Framework for Consuming Insights from Data and Analytics 

Faced with overwhelming amounts of data, organizations across the world are looking at leveraging data and analytics (D&A) to derive insights to increase revenue, reduce costs, and mitigate risks. McKinsey found that insight-driven companies report EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25% [1]. According to Forrester, organizations that use data and insights…

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Business Intelligence and Analytics Trends in 2024

As organizations continue to generate massive amounts of data from various sources, the need for effective data analytics and business intelligence (BI) solutions has never been more pressing.  Here are some general trends for data analytics and BI:  Use of Advanced Algorithms in Data Analysis  Advancements in machine learning (ML) and artificial intelligence (AI) are…

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How to Become a Citizen Data Scientist

The job responsibilities of a citizen data scientist include dealing with new data, using automated tools to process big data, and creating additional models to gain additional insights. Their primary job is not to make predictions directly from big data, or develop prescriptive analytics, but to build models and use tools that accomplish those goals….

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