TDWI Upside: The Source for All Things Data & Business Analytics
TDWI Upside
Upside delivers timely, relevant, actionable information about transforming data into intelligence, insight, and impact in your enterprise.
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Although cybersecurity risk varies by several factors (such as company size and industry), it hasn't declined in the last quarter across all U.S. enterprises measured by FICO.
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By Upside Staff
How quickly self-driving are cars coming, how they can be created more efficiently, and what went wrong for an airplane’s automated systems.
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By Upside Staff
How to be successful with machine learning, choose the right tools, and head off model decay.
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By Upside Staff
How to embrace predictive analytics, how predictive analytics is affecting security, and how five other industries are changing because of real-time data.
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By Kurt Rohloff
Does your data need to be encrypted?
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By Wayne Yaddow
Applying DevOps-style test automation to your projects can guarantee a high level of data quality.
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By Emily Washington
Moving to a cloud-based data platform is a tall task, especially with so much data at stake. These tips can help.
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Early adopters of the data lake have arrived at a maturation stage where they need to modernize their platforms before expanding their implementations.
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By Upside Staff
What’s coming in the U.S. for consumer data protection, what enterprises can do to protect their data, and how new regulation might affect medical devices that depend on machine learning.
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By Upside Staff
What job descriptions say about corporate culture, what millennial bosses really think, and which industries see the most drug and alcohol abuse.
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By Richie Etwaru
Adding property rights to inherent human data could provide a significant opportunity and differentiator for companies seeking to get ahead of the data ethics crisis and adopt good business ethics around consumer data.
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Thinking about three key aspects of the cloud can accelerate your adoption of the technology.
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By Upside Staff
These data visualizations show patterns for childbirth, daycare costs, and high school education in the U.S.
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Data quality issues become even more important as machine learning use grows. DataOps and data wrangling help enterprises address this vital problem.
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By Greg Council
Machine learning applications are dependent on, and sensitive to, the data they train on. These best practices will help you ensure that training data is of high quality.
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With the advent of automated machine learning, data scientists will need to adapt their role in the data science life cycle.