Technologies that not long ago were science fiction are now mainstream—and to many people, the possibilities look mighty fine. But jumping into that future isn’t easy.
We offer this piece of advice: Before getting sucked into the big data vortex, take stock of what your company could benefit from most. To do that, let’s deconstruct some of the buzz words and get clear about what’s what.
Before tech-ing up, break it down.
Artificial intelligence. Machine learning. Automated insights. Advanced analytics. Heard of them? Sure. But can you tell them apart? (Click to Tweet)
Some of these terms have been used interchangeably, eclipsing the new technology with confusion. So, whether you’re already strapping on your flight suit, or are just beginning to explore, let’s clear things up. Because in technology, as in space exploration, it’s smart to first see clearly what you’re getting into.
Artificial Intelligence or AI
Artificial means that it’s done by machines or devices, not a human. Humans have what’s called natural intelligence, meaning that organic material collects and interacts with data. Artificial means that a non-organic thing, such as a computer, has collected and interacted with the data. Intelligence means acquiring and applying knowledge, whether by an organic being or an inorganic machine/computer.
Here’s the simplest explanation of AI ever. Artificial Intelligence is a machine or computer’s ability to acquire and apply knowledge.
That’s knowledge. Not data. Here’s another distinction: Data is numbers, facts, lists, and statistics. Knowledge is data in context, and it’s gained through analysis and experience. Knowledge is data made usable. That’s what AI does; it makes data usable and applies it, sometimes in a new context.
Acquiring knowledge and using it
AI is a broad term, a discipline in science that covers many topics. All of the areas in AI involve acquiring knowledge and applying it. For example:
Natural language understanding. Tools like Dragon Dictate, Siri, and Cortana recognize speech, turning audio sounds into information they can acquire and use. Siri or Cortana, for example, can learn to adapt to a regional accent or a mispronounced word in order to follow simple instructions, like playing music, turning on lights, or searching for something on a computer.
Digital vision. Tools like DeepFace and self-driving cars acquire information from digital images and use it. For example, Facebook’s DeepFace recognizes which part of a photo is a human face and identifies it based on a database of faces.
Machine learning (ML). ML is when a computer or device acquires and interprets knowledge from a large amount of data and uses it in a way that improves its processes, with or without the aid of humans. ML uses the information it acquires to get faster, smarter, and more accurate over time, based on statistics and other mathematical applications. For example, if you show the ML computer that 2 + 2 = 4, it can figure out that 1 +3 = 4 and that 2 + 3 = 5. You feed it stock market history, weather, political situations, and business data, and it comes up with intelligent math-based trade suggestions.
There are other areas of AI and applications that bring several of these areas together. There are knowledge bots (like Siri and Cortana), chat bots (simple versions, usually used in customer service chats), and inference engines that apply logical rules to data, connect the dots, and deduce new information.
The sophistication of the AI of a computer or device depends on how well it can logically apply information in new ways, with increasing effectivity, and even, occasionally, independently. That means that the future of this technology is as wide open as imagination can take us.
Where does advanced analytics fit into all of this?
Actually, all of this—and more—fits into advanced analytics. Traditional analytics or business intelligence is a review of historical data; it’s a reporting of past results. Advanced analytics uses AI and other techniques, including statistical and predictive forecasting, to look ahead. Put simply, traditional analytics ask, “What happened?” and advanced analytics ask, “What’s possible?”
How does all this affect planning?
When it comes to business planning abilities, AI and ML are about to deliver the sonic boom of mic drops. Instead of basing this year’s forecast solely on last year’s sales, you could have a plan that takes projections for significant events into account—social or local events like festivals or sports championships, business events like mergers and other changes along the supply chain, regulatory changes, weather predictions and effects, seasonal demand, fads and trends, staffing issues—the possibilities are endless. The tool acquires the necessary information and applies it all by itself, leaving you free to review the results and come up with an intelligent response that keeps your company ahead of the competition.
Instead of plodding away trying to make a plan that foresees everything (or doesn’t), you could choose the most desirable result from an array of options prepared for you, using criteria that you select. But it’s more than that—you won’t spend your time compiling and verifying data and trying to keep up with what’s happening. You and your team can get back to the work you really want to do. The status of the business is constantly updated and accurate, and that information is available on your nearest screen.
When it comes to making the most of your data, there’s a lot to wrap your head around. Before you launch any new technology, whether you’re developing a time machine, a space program, or a better planning tool, you need to do some adequate preparation and study. You’ve just taken the first small step to a high return on your technology investment and a streamlined flight into the future, with no possibility of flaming out on re-entry.
To explore more about AI, and in particular, Anaplan’s breakthroughs in marrying machine learning and planning—and whether your business is ready to benefit—read this article.