Data makes the world go round
Artificial intelligence has a trust problem. At the root of this trust problem is the data that makes AI run. Nearly six in 10 AI users say it’s difficult to get what they want out of AI, with more than half, 54%, claiming they don’t trust the data used to train today’s AI systems, a Salesforce survey of 6,000 global knowledge workers suggests. Three in four of those who don’t trust the data that trains AI also believe that AI lacks the information needed to be useful.
Innovation in AI doesn’t just spring out on its own, or by business or technology teams working the models and algorithms. A solid and well-vetted data foundation is a necessity.
Industry leaders are increasingly voicing concerns about the viability and reliability that is being fed into today’s growing complex of AI systems and applications. "AI is only as good as the data that’s backing it," says Sean Knapp, founder and CEO of Ascend.io. Business leaders and professionals “need to understand that just because AI will give them an answer, that doesn’t mean it will be accurate.”
Along these lines, "you can’t realize AI’s immense innovation potential by simply pressing harder,” Knapp adds. “Data development is often siloed and time-consuming, fraught with delays, disconnects, and disillusionment.”
The bottom line is that a data-driven business is an AI-driven business — there is no longer any daylight between the two definitions. “Without a data-driven focus, businesses can't compete,” says Sharad Varshney, CEO of OvalEdge.
At issue is the fact that "many organizations are still just getting a handle on their data for basic business intelligence tasks, let alone AI,” Knapp says. Needed is “clean data from advanced data pipelines. Achieving operational efficiency, improving customer experience, and creating innovative products hinges on how quickly you can identify the required datasets and create systems to produce them reliably.”
Adopting AI-enabled “data management, analytics, and governance technologies from day one will put you in an enviable position,” Varshney points out.
First and foremost, organizations need to grasp with without the right data, their AI initiatives won’t even get past the starting gate. "Many business professionals want to skip right to the analytics and exploitation of AI models without thinking about building a solid foundation of data," says Jonathan Bruce, vice president at Alation.
"You need to slow down to go fast,” Bruce continues. “While there are benefits to the rapid adoption of AI, the organizations that come out of the AI revolution in the strongest position will be those that invested in a solid foundation of trusted and governed datasets that underpin their AI initiatives. Packaging trust allows users to understand the provenance and lineage of the supporting data, empowering them to apply those models at the speed of business.”
To keep up at the speed of business, and push innovation forward, “businesses need data to train AI solutions, as data and AI are inextricably linked," says Ram Chakravarti, CTO of BMC. “AI can even make new data available for better analytics and identify patterns and anomalies. Additionally, AI can automate routine tasks, freeing up time for employees to focus on new business ideas and structures."
High-quality data is a necessity, says Chakravarti. "For AI to have value, it requires being trained on high quality data sets – and data quality is just as important as data volume.” At the same time, he adds, “without AI, organization will have a hard time deriving meaning from their large volumes of data."
