Data Collection Data Quality Software 
CIO Business Intelligence
NOVEMBER 27, 2024
Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.
DataKitchen
JUNE 21, 2024
Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It’s that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You’ve got yourself a recipe for data disaster.
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O'Reilly on Data
MARCH 24, 2020
Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.
O'Reilly on Data
MARCH 18, 2020
Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.
O'Reilly on Data
DECEMBER 19, 2018
In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Humans are still needed to write software, but that software is of a different type. Developers of Software 1.0
O'Reilly on Data
JUNE 18, 2019
As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.
datapine
APRIL 16, 2019
Collecting, extracting, formatting, and analyzing insights for enhanced data driven decision making in business was once an all-encompassing task, which naturally delayed the entire data decision making process. With more people understanding the data at play, you’ll have an opportunity to receive more credible feedback.
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