The terms “analytics” and “artificial intelligence” get bandied about in the media as harbingers of the future. But until recently, it’s been rare to see them discussed in a common context.
We know that analytics drives many aspects of business strategy and decision-making nowadays, but only recently have researchers, computer scientists and data scientists begun to move in earnest on the notion that AI systems, with their ability to process disparate bits of information quickly and learn while they’re doing so, can have a real impact on challenges faced in areas like healthcare, finance and engineering.
Probably the most high-profile example of this was the creation of IBM’s Cognitive Business Solutions Group earlier this year. The unit advises organizations on how to use IBM’s AI solution Watson in ways that go beyond what was previously possible in research and analytics. According to The Wall Street Journal, IBM sees Watson “as the core of a new generation of ultrasmart digital assistants in data-heavy industries such as medicine, financial management and oil-and-gas exploration.”
The combination of AI and analytics can produce remarkable results. It’s a trend data scientists should pay attention to.
But IBM’s not the only one betting that the combination of AI and analytics can produce remarkable results. It’s a trend data scientists should pay attention to. And here are just four reasons why.
- Smarter analytics engines are making the mining of digitized information more powerful. In the Harvard Business Review, IBM Research’s Dario Gil described how AI speeds the organization of user-created data on the Web, without requiring machine-learning experts to manually index it all. “Thanks to computers with massive parallelism, we can use the equivalent of crowdsourcing to learn which algorithms create better answers,” he said.
- According to researcher IDC, 44 zettabytes of data will be created by 2020. For perspective’s sake, one zettabyte equals roughly 1 billion terabytes. Even if there wasn’t a shortage of skilled analytics talent, that’s an impossible amount of data to process, organize and understand without the aid of some powerful artificial intelligence solutions.
We’ll soon contend with an amount of data that will be impossible to process, organize and understand without the aid of some powerful AI solutions.
- As less-technical companies begin to make use of data, they’ll look for user-friendly ways to make analytics available to end users. For instance, today the marketing departments of large companies can afford to hire the expertise necessary to make sense of their customers’ buying patterns. But as technology improves, similar capabilities are sure to find their way into the hands of small businesses seeking to analyze common business situations and develop strategies to take advantage of them.
- At the same time, businesses of all sizes want to get the most value from their data, as quickly as possible. Very often, that means they want to analyze it in real time. “Narrow AI” – meaning artificial intelligence that’s focused on a single, narrow task – is able to sift through large amounts of data to report on predefined information in needed reports. “Although information has to be narrowly defined within any search, the ability to carry out multiple related searches at the same time means it can provide accurate modeling,” notes David Senior, CEO of Lowdownapp Ltd., which uses narrow AI to create information-based mobile apps.
In short, as the amount of data grows, and more businesses look for ways to take advantage of it, artificial intelligence will become a critical tool for the data scientist. AI won’t make the need for analytics talent obsolete, but it will challenge professionals to think more creatively about how they can harness data to solve business problems.