2024 DATAVERSITY Top 20

As we approach the end of another year, we here at DATAVERSITY have been busy analyzing our data and answering the big questions: What was the most popular content on our website and training center over the past 12 months? Which data management topics did you – our readers and students – seek out again…

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Prescriptive Analytics Use Cases

Optimizing business outcomes through advanced analytics and real-time scenario analysis represents a significant leap forward in decision-making processes. In practice, advanced analytics provides a framework for evaluating an extensive array of potential outcomes derived from different decision paths.  It integrates ML models that simulate various scenarios in real time, thereby offering a dynamic decision-support system….

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Predictive Analytics Techniques

The process of predictive analytics has three main steps: defining the objectives, collecting relevant data, and developing a predictive model using sophisticated algorithms. These models are further tuned for greater accuracy before being applied to real-world situations like risk analysis or fraud detection.  Predictive analytics techniques are at the forefront of modern data science, enabling organizations to…

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Data Storytelling: Be a Data-Driven Organization

With the staggering amount of data in use today, it’s no wonder that business leaders are increasingly harnessing its power to develop meaningful insights and make informed decisions. According to a survey by Deloitte, organizations that make use of data for decision-making are twice as likely to exceed their business goals and outperform their competitors. One method to…

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4 Key Reasons to Build a Data Culture

Understanding the importance of data culture requires recognizing its pivotal role in shaping how organizations operate and innovate. A strong data culture instills a mindset where decisions are driven by data, fostering an environment that values evidence over intuition. This cultural shift enables organizations to harness the full potential of their data assets, leading to…

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The Future of Data Literacy

There was a time when only elite, tech-savvy staff in an organization understood and felt qualified to discuss data-enabled business decisions. These individuals often possessed advanced academic degrees in data science, data engineering, statistics, operations research, and other allied fields and did not speak the language of the ordinary business staff. As a result, there…

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Women in Data: Meet KNIME Head of Data Science Evangelism Rosaria Silipo

The latest installment in our Q&A series with women leaders in data features Rosaria Silipo, head of data science evangelism at KNIME. (Read our previous Q&A here.) Rosaria Silipo, Ph.D., has worked in data analytics and data science for the past 30-plus years. Currently the head of data science evangelism at KNIME (as well as a longtime DATAVERSITY…

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Self-Service Analytics: Pros and Cons

Self-service analytics empowers the non-technical users in an organization. Traditionally, data analysis was the domain of specialized data scientists or IT professionals with skills to manipulate and interpret complex datasets. In self-service analytics, user-friendly tools enable ordinary business users to conduct data analyses without expert knowledge or support.   These tools typically feature user-friendly interfaces, drag-and-drop functionalities, and pre-built templates…

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Fundamentals of Data Preparation

Data is often called the raw material of the information age, and it does share characteristics with the resources that power other industries. For example, imagine trying to make a car out of unrefined iron ore. A lot of processing happens between the mine and the factory. Data is no different. In its “raw” form,…

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Data Product vs. Data as a Product (DaaP): Understanding the Difference

Data quality (DQ), which ensures that data is fit for business and consumer needs, remains a significant challenge and is growing more complex. According to a dbt Labs 2024 report, 57% of survey respondents identified data quality as a challenging aspect to data preparation, up from 41% in 2022.  To address these data quality challenges, companies increasingly…

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