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Why Your Business Needs Data Modeling and Business Architecture Integration


In the contemporary business environment, the integration of data modeling and business structure is not only advantageous but crucial. This dynamic pair of documents serves as the foundation for strategic decision-making, providing organizations with a distinct pathway toward success. Data modeling provides organization to your facts, whereas business architecture defines the operational mechanisms of your […]

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Author: Pankaj Zanke

The Analytics Sandwich: Understanding the Business Value of Data and AI
In discussions with data management professionals, conversations often veer toward the technical intricacies of migration to the cloud or algorithm optimization, overshadowing the core business objectives that originally spurred these initiatives. Yet, conversations with chief information officers (CIOs) and chief data officers (CDOs) reveal a relentless pursuit of concrete business value, a metric that determines […]


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Author: Myles Suer

Granularity Is the True Data Advantage


Commerce today runs on data – guiding product development, improving operational efficiency, and personalizing the customer experience. However, many organizations fall into the trap of thinking that more data means more sales, when these two factors aren’t directly correlated. Often, executives will become overzealous in their digital transformations and cut blank checks for data collection, […]

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Author: Fabrizio Fantini

Facing a Big Data Blank Canvas: How CxOs Can Avoid Getting Lost in Data Modeling Concepts


The volume of data now available to businesses continues to grow exponentially. When looking to extract valuable insights into their business’s performance, C-level executives (CxOs) must navigate the big data blank canvas. This requires a strategic approach, in which CxOs should define business objectives, prioritize data quality, leverage technology, build a data-driven culture, collaborate with […]

The post Facing a Big Data Blank Canvas: How CxOs Can Avoid Getting Lost in Data Modeling Concepts appeared first on DATAVERSITY.


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Author: Haroen Vermylen

Handling Data Concerns in 2024 and Onwards


Looking back, then forward, is a traditional exercise by year-end. Which data concerns are important enough to worry about in 2024? Which of those do we stand a chance of doing something good for in 2024? Needless to say, money (budget and costs) is an issue. But even more needless to say, solving real business […]

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Author: Thomas Frisendal

Generative AI and Semantic Compliance


Only CPT and its peers know how many statements have been made based on results from generative AI. But there are loads of them. My background as a data modeler over many years makes me shiver a little bit, because what the friendly AI helpers help us produce is subjected to cognitive processes, where we, the readers, process […]

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Author: Thomas Frisendal

Modeling Modern Knowledge Graphs


In the buzzing world of data architectures, one term seems to unite some previously contending buzzy paradigms. That term is “knowledge graphs.”  In this post, we will dive into the scope of knowledge graphs, which is maturing as we speak. First, let us look back. “Knowledge graph” is not a new term; see for yourself […]

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Author: Thomas Frisendal

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