By Christ Stuart
In the evolution of analytics, we’ve come quite a long way but to quote Alice Cooper, we still got a long way to go. Imagine the early 1990s, when slow, basic, back-office reporting reigned. Numbers weren’t connected to the actual front-office business and were used mostly to support internal decisions. For argument’s sake, let’s call this the 1.0 phase of analytics.
Next, during phase 2.0 in the early 2000s, companies like Google, PayPal and others started to look at large amounts of unstructured data. Thus began the rise of “data scientists” and under their watch, data management activities took on increasing popularity.
Enter, phase 3.0 when big companies started adopting big data. Conglomerates hired data scientists and acquired smaller analytics shops to harness the power of their analytics.
Finally, we reach phase 4.0, or where we are today. With the onset of predictive analytics and cognitive analytics comes machine learning. Suddenly, artificial intelligence is no longer a plotline in sci-fi movies but a viable decision-making tool for business. Combine the ensuing age of AI with a justifiable obsession for big data and analytics, and business leaders must now confront the reality that when the process of analyzing analytics becomes fully automated, machines may dictate our next move. But are we as a business collective ready to trust what these machines have to say?
If we take a cue from Facebook, the answer is yes. In March, Facebook announced a pilot project that would use AI to proactively identify Facebook posts that signified a potential suicide threat. In its first month of testing, 100 “wellness checks”—when first responders visit affected users—were enacted due to the work of Facebook bots.
The news of AI saving lives comes at an applicable time, just when a discernible shift can be seen from human-analyzed big data to autonomous data analytics, and this transformation means C-Suite leadership members must put their trust in the power of AI.
In advertising, AI has certainly been embraced, though it’s still niche marketing for now. In early November 2017, BMW created a car inside Snapchat that was designed with life-like precision. Unlike Snapchat’s famous dancing hot dog or AI bitmojis, this was the first instance a product was translated into the augmented reality world as an ad, in this case to commemorate the launch of the BMW X2. Users could virtually walk around the car, mobile phone in hand, and see its features with exquisite detail, change its color and interact with the product in an entirely new way.
November 2017 also saw Adidas release a new mobile app game in partnership with Salesforce. The app evolves as a consumer interacts with it, tailoring to the preferences and behavior of the user to create a highly personalized mobile experience. Embedded artificial intelligence within the app learns about the user’s specific predilections to show him or her customized product recommendations, articles, blog posts, videos and real-time updates about the user’s favorite sports and athletes.
So, if advertisers, tech companies and social media giants are embracing AI, can the world of big business embrace it, too?
In a 2017 report by PwC Global called “Global Artificial Intelligence Study: Exploiting the AI Revolution,” the firm estimated that by 2030, AI would contribute $15.7 trillion to the global economy and by that same year, local economies would see an approximate 26% boost in GDP from AI activities.
The study confirms that the central question surrounding AI is not its viability but rather its ability to be used responsibly. (Some of those same Facebook bots that helped flag self-threatening posts also developed their own language and had to be shut down earlier this summer. And weeks ago, Elon Musk tweeted that in a few years robots will “move so fast you’ll need a strobe light to see them.”)
Beyond the idea of AI being a science not fully understood, it’s also an initiative that must be deployed for the benefit of the consumer and not as a harbinger of damaged goodwill. Skilled employees with both domain and data expertise are essential for the successful implementation of AI in any business. Customer data must be protected; AI technologies must be reviewed and updated as the science behind them develops. Do you have the necessary talent in place to properly control AI technology? That answer will decide whether you’re ready to have an AI-enhanced system at work.
Still, if you’re not quite at a point where AI works for you, predictive analytics as examined by a skillful leader or employee can still be a powerful benefit for your business. At Berkshire Hathaway HomeServices Loft Warehouse in Detroit, president Jerome Huez has created what he calls “The desirability Index” as a way to take a snapshot of what consumers most want in a property—amenities, property location, unit size, and more. This index helps determine the predicted value of a property. This allows developers to perform a cost-benefit analysis for the various finishes, amenities and options available for consideration.
The index is generated by MLS data and validated quarterly to ensure the figures being imputed into the model are accurate and current. Huez then spends hours analyzing the data generated by the Index because as he says, “The model can tell me, based on the Desirability curve, what the consumer wants but it can’t tell me why.”
Working with property investors across the city, Huez and his agents are creating a brand-new skyline of Detroit—an urban metro experiencing its own transformative renaissance—based entirely on data and predictive analytics.
“I was tired of going into meetings and talking subjectively instead of objectively about why something was priced a certain way,” he explains. In a year or two, Huez expects the process of creating the Desirability Index will be fully automated. Then, decisions about the characteristics of real estate developments will be based on what a machine says is the right thing to do, a confluence of AI with predictive analytics.
“It’s exciting for us to be able to provide this service to our clients,” Huez explains of his now-manual model, noting that no other brokerage in Detroit has anything close to a Desirability Index to help investors determine how best to build. “This competitive advantage means clients don’t want to work anywhere else but with us,” he says. “And where else can they get the kind of information we’re generating every day?”
CHRIS STUART is the SVP of Business Development and Operations for HSF Affiliates. You can find him on Twitter: @HSFChrisStuart.
3 responses to Predictive Analytics, Big Data and the Rise of Artificial Intelligence
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Great blog post-Chris!
Interesting. Makes me want more to read.