Guide to using advanced analytics and AI in business applications
A comprehensive collection of articles, videos and more, hand-picked by our editors
GRANTS PASS, Ore. -- Business uses of AI analytics applications will grow over the next 12 months, but so will...
By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.
the need to better manage and orchestrate the algorithms that drive them.
Those were among the predictions made at the 2017 Pacific Northwest BI & Analytics Summit, which brought together a group of IT consultants and vendor executives here this month to discuss -- and forecast -- business intelligence, advanced analytics and data management trends. Other topics they eyed included edge analytics and new data privacy rules taking effect in the European Union next year. But artificial intelligence (AI) was top of mind during the predictions part of the proceedings.
Organizations increasingly will find that the diverse pools of big data they're collecting can't be effectively analyzed with traditional tools and human brainpower, said Yves de Montcheuil, a France-based consultant who works with technology startups on marketing and strategy. As a result, he thinks AI software will become more and more crucial to getting real business value from big data applications.
"AI is becoming the new black," de Montcheuil said -- a statement amended shortly thereafter by IBM executive Harriet Fryman, who proclaimed AI to be "the new bacon."
More to think about on AI management
But, as AI analytics becomes more common in corporate enterprises, managing the process is expected to get more important -- and more complex.
Analytics teams will have to pay more attention to "the composition of AI systems," said Donald Farmer, principal of consultancy TreeHive Strategy in Woodinville, Wash. They'll also need to implement detailed governance and oversight procedures "as companies start to put hundreds and thousands of algorithms in place," chimed in Shawn Rogers, senior director of analytic strategy at vendor Tibco Software Inc.
Donald Farmerprincipal, TreeHive Strategy
Gartner analyst Merv Adrian foresees networks of AI-powered tools and devices that can communicate with one another and have the ability to ingest data on their own -- developments Farmer said would make it more clear that data scientists and other analysts are "participants in AI systems" as opposed to users of the technology in a traditional sense.
Another issue to contend with is the level of uncertainty in what AI algorithms predict. Farmer said AI-based analytical models tend to be accurate if they're well designed, but there's almost never a 100% probability that their findings are correct -- something that needs to be made clear to business executives so they don't expect infallibility from the technology. "If we're going to live in a world where things are going to be driven by algorithms, we have to be able to convey their ambiguity," he said.
Edge analytics set to take flight?
In addition to increased use of AI analytics, big data environments are likely to push deployments of "in-flight" analytics applications at the edge of corporate networks, said Mike Ferguson, managing director of U.K.-based consultancy Intelligent Business Strategies Ltd.
As data continuously streams from devices on the internet of things (IoT), mobile apps, stock-trading systems and the like, trying to funnel it all into a centralized data repository for processing and analysis becomes a tall order, Ferguson said. He envisions wider development of event-driven data architectures with edge analytics systems that can trigger automated actions on the fly. "This is a world where the data never stops, and it's completely challenging the way we've done things in the past," he noted.
Companies face a different kind of challenge in complying with the EU's General Data Protection Regulation (GDPR), which establishes stricter rules on data privacy and security for companies that operate in Europe or do business with organizations that handle the personal data of EU residents.
Due to become law in May 2018, the GDPR will require new data governance processes in many companies -- a step that Farmer said could end up contributing to the law's undoing because of the potential for added business costs. "The GDPR could collapse," he said. "The way it forces you to change how you do business will be unacceptable [to a lot of organizations]."
If the law doesn't fall apart, EU regulators likely will find it hard to broadly enforce the new rules because of the large number of companies that are affected, Adrian said. But IT and business executives shouldn't get complacent about their GDPR compliance efforts, he cautioned, saying that he expects EU officials to try to "make an example of someone" to scare other organizations into adhering to the rules.
Check analytics and data management predictions from the 2016 event
Essential Guide: AI technology comes of age for business uses, at long last
AI applications must be free from data bias to produce accurate results