Big Data

Let’s Get Real: The Truth about Artificial Intelligence

OCT 02, 2017

When was your first time? Was it when you read ‘I Robot’ (1950; a collection of short stories by science fiction writer Isaac Asimov)?  Watching 2001 Space Odyssey (1968; film by Stanley Kubrick). Or was it when you purchased a Roomba vacuum cleaner in 2005, played ‘Middle Earth – Shadow of Modor” in 2014, or purchased a “recommended” item from Amazon?  And today, we’ll guess that you’ve already used apps like Spotify, Pandora or Netflix to get your fix of music and movies based on the interests you’ve expressed and judgments you’ve made in the past.  Or perhaps Siri, Google Now, and Cortana guided you through your kid’s homework or helped make weekend plans.

Welcome to the real world of Artificial Intelligence. It’s sexy, it’s cool and its inevitable.  And it’s seemingly everywhere and may prove to be one of the fastest-growing technology segments in 2017. After all, it certainly can assess tons (literally) of data far more quickly and accurately than a human.  And this is exactly why organizations need to figure out how to prepare, foster and grow their AI strategy.  Part of this requires asking if/how will this impact their current business and then figuring out how to integrate AI in their strategic plans.

From our experience, we think Data Intelligence is the backbone of Artificial Intelligence based upon the methodology of how Machine Learning and Deep Learning apply to data.  As you investigate the power of Artificial Intelligence to drive business outcomes, keep in mind the following “truths” as your team moves forward.


  • Clean Data is a Requirement 
    • Think about how Machine Learning works. It’s based on algorithms that require data to learn from. You have data. Lots of data. {According to EMC (with research and analysis by IDC), “by 2020 the digital universe — the data we create and copy annually — will reach 44 zettabytes, or 44 trillion gigabytes.”} In fact, your company is likely producing more data than is usable. The data is probably siloed, unstructured and a headache to analyze. You know how this story ends. The machine learning algorithms are fed bad data and create incorrect patterns, bad models, or just conclude that there’s no value in the data.


  • Data Must be Integrated 
    • Data silos, by their inherent characteristics, breed data distrust. In the consumer product industry for example, one group may own branding information, the other product specs or nutritional info, so if they aren’t talking, the data (product information) will not be optimized. Business leaders need to make sure that all the valuable customer or product data that comes from various systems is centralized and integrated. Integrated data means better organizational analytics, critical in educating machine learning systems and creating more well-rounded AI.


  • Know Your End Game
    • The jury is still out as to whether AI can outlive its hype. In Gartner’s hype cycle, which ranks technologies based on how the market perceives them and how far away they are from mainstream adoption, machine learning is right at the top, estimating AI is 2-5 years away from widespread adoption. If that’s the case, find out how you can best leverage AI. Start with a blank whiteboard and ask yourself where in your industry can you develop a competitive advantage by solving new problems or automating tasks. (See chart below from Harvard Business Review on how companies are using AI.) Determine measurable goals and then get some expert advice (Trace3, of course!) on how to implement them.


How Companies Are Already Using AI

One Response to “Let’s Get Real: The Truth about Artificial Intelligence”

October 06, 2017 at 4:02 pm, Inès said:

Great post! AI is definitely the future.


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