There is no hotter topic than artificial intelligence (AI). In the new CIO MarketPulse research survey from Foundry, 96% of the participating business and IT decision-makers said their organization is using, testing, or planning to investigate AI technology.
Similarly, there is no hotter profession than data science – developing algorithms to wring valuable and potentially game-changing insights from data. But focusing on highly paid data scientists and what they do can be misleading. To maximize the value of AI, you must first maximize the value of your data.
“Data scientists play a critical role, but they can’t do anything without trustworthy data,” says Rita Jackson, senior vice president of product marketing at OpenText. Generating clean, accurate, and timely data and subjecting it to AI algorithms that elicit actionable insights is fundamental to a successful modern business, Jackson explains.
So, what’s the best path to a successful AI implementation?
“Within an overall data governance strategy, implement enterprise content management,” says Jeff Healey, vice president of analytics and AI product marketing at OpenText. The idea, Healey explains, is to create a “single source of truth” from your data that can be used by multiple AI-powered applications.
Creating data that will produce good results with AI means cleaning, deduplicating, and tagging it with metadata. The data can then be subjected to machine learning (ML) algorithms with OpenText Aviator Intelligence (Magellan), an AI platform for text mining and data discovery.
A great use case is litigation, in which documents must be searched for specific words and references in response to legal discovery demands. “Aviator Intelligence can narrow the search down to just a few documents. It’s a good example of how OpenText is infusing AI into all its products,” says Jackson.
But litigation is only one use case. The proliferation of internet of things (IoT) data is opening new horizons for AI and ML. As IoT sensors have multiplied, raw processing power has increased, enabling manufacturers and healthcare providers alike to perform real-time analytics on IoT data from machines.
In healthcare, sensors on MRI machines monitor performance and relay information about the machines’ health to a management console. Running ML algorithms against sensor data means that organizations can predict when equipment maintenance is needed. This way preventive work can be scheduled to ensure that patients receive uninterrupted high-quality care.
In another example, Jaguar TCS Racing is feeding IoT sensor data from its electric racing vehicles to OpenText for analysis. “By leveraging data from lots of sensors, OpenText enables Jaguar TCS to take in information during a race, analyze it, and make adjustments so the cars can run at peak efficiency,” says Healey. And lest you think electric race cars are a one-off use case, Healey adds, “This might involve IoT, but it applies to every industry. Everybody should be treating their business as a high-performing vehicle.”
The bottom line
Whether from an electric race car, an MRI machine, or legal documents, it’s the data that drives AI. Learn more about how OpenText helps you govern, unify, and protect your data in your quest for transformative AI results.
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