Remove Data Collection Remove Data Quality Remove Risk
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AI adoption in the enterprise 2020

O'Reilly on Data

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.

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The unreasonable importance of data preparation

O'Reilly on Data

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. Data collected for one purpose can have limited use for other questions.

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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. If you can’t walk, you’re unlikely to run.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

Preparing for an artificial intelligence (AI)-fueled future, one where we can enjoy the clear benefits the technology brings while also the mitigating risks, requires more than one article. This first article emphasizes data as the ‘foundation-stone’ of AI-based initiatives. Establishing a Data Foundation. era is upon us.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

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Making the gen AI and data connection work

CIO Business Intelligence

The alternative to synthetic data is to manually anonymize and de-identify data sets, but this requires more time and effort and has a higher error rate. The European AI Act also talks about synthetic data, citing them as a possible measure to mitigate the risks associated with the use of personal data for training AI systems.

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How Data Cleansing Can Make or Break Your Business Analytics

Smart Data Collective

This market is growing as more businesses discover the benefits of investing in big data to grow their businesses. One of the biggest issues pertains to data quality. Even the most sophisticated big data tools can’t make up for this problem. Data cleansing and its purpose. Tips for successful data cleansing.