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Data Drift: Models Break when Data Context Changes

By Dick Weisinger

Data may seem like something that’s immutable and constant. But data is only relevant to the time and context in which it was collected. Time goes on. Contexts change. This presents a problem for applications like big data and data

Data Science: The Importance of Data Engineers

By Dick Weisinger

With the rise in the importance of data in businesses, for use in analytics, AI and Big Data processing, the role of the data engineer has only recently appeared, but it has already become an important role for many businesses.

Data Resiliency: Protecting Against Data Loss

By Dick Weisinger

Resiliency in IT refers to systems that can survive failures while still remaining functional for the end user. Data resiliency means that despite system failures, the data itself remains intact and unaffected. The Global Data Resiliency Market is expected to

Data Management: Businesses Look to Data to Improve Efficiency and Reduce Costs

By Dick Weisinger

How are businesses targeting data to promote growth? A survey by CompTIA identified three categories of trends that they see across businesses: Analyze – Identify the sources of data and processes that use the data Organize – Businesses are creating

Data Quality: Poor Quality Impacts AI and Analytics Projects

By Dick Weisinger

Data prep is essential to effectively using data-based tools like Data Analytics, Big Data, Machine Learning and AI, as discussed in yesterday’s post. A recent study by O’Reilly backs up the importance of data quality. The O’Reilly report finds few