AI’s biggest risk factor: Data gone wrong

Bad data is big issue for artificial intelligence, and as businesses increasingly embrace AI, the stakes will only get higher. Here’s how not to get burned.

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"The search technology is largely becoming commoditized," Wagner says. “Lexical parsing, text matching, all that technology has been codified and polished and open source algorithms are just as effective as any proprietary package.”

And it didn't take PhD-level data scientists to do it.

"With some talented engineers, you can figure out how to wire it into your data stream," he says.

Wagner is a big fan of Apache Spark, a big data engine that can pull in data from many different sources and slice and dice it, and Apache Solr, an open source search engine. Bluestem not only uses it on the customer-facing side, but also internally, to help with editorial workflows.

The company also uses commercial products, such as Lucidworks Fusion, which allows business users to customize the search experience with additional business logic — say, to funnel queries related to Valentine's Day to a curated set of recommendations without requiring IT to get involved.

With the right data management strategy, tools, and personnel, you can greatly enhance your organization’s likelihood of AI success.

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