Beyond the Basics: Advanced Tips for Effective Data Extraction

Data extraction is a cornerstone in data analytics, enabling organizations to extract valuable insights from raw data. While basic extraction techniques are fundamental, understanding advanced strategies is crucial for maximizing efficiency and accuracy. This article will explore advanced tips for effective data extraction, shedding light on automation tools, leveraging APIs and web scraping techniques, enhancing…

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A Brief History of Data Quality

The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact-checking and Data Governance, and, as a consequence, make decisions that give them an advantage….

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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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Data Discovery 101

Data discovery deals with extracting useful information from data and presenting it in a visual format that is easily understood. The types of useful information discovered during the process range from finding patterns in human behavior to gaining insights about data glitches to answering highly specific business questions. Using data taken from a variety of…

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Data Crime: Arizona Is Not Arkansas

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from mistakes and prevent future data crimes. The stories can also be helpful if you have to explain the importance of data management to someone. The Story After a series…

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How Data Analytics Is Transforming Due Diligence

Due diligence is the complex process of assessing a potential investment and ensuring it is a sound one. It involves the gathering, classifying, and analyzing of large volumes of data. Given its very nature, it’s the perfect field for data analytics, which can speed processes up and assess the quality and reliability of data. Due…

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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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Eyes on Data: The Right Foundation for Trusted Data and Analytics

Trust. Trust is defined as the assured reliance or belief on the character, ability, strength, or truth of someone or something (Webster’s Dictionary). It’s a term we use often to describe how we feel about the people, the institutions, and the things around us. But I would argue that the term “trust” was used differently…

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Managing Missing Data in Analytics

Today, corporate boards and executives understand the importance of data and analytics for improved business performance. However, most of the data in enterprises is of poor quality, hence the majority of the data and analytics fail. To improve the quality of data, more than 80% of the work in data analytics projects is on data…

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