For businesses to survive today, they have to embrace new trends quickly.
We’re no longer living in the world where new developments, like airplane travel or television, take decades to reach market saturation. Today’s technology adoption cycle is blink-and-you’ll-miss-it fast, even compared to the advent of computers, mobile devices and the internet, which transformed their respective markets in just a few decades.
We are now living in the age of exponential connectivity. It’s hard to keep up with all the new devices out there that can provide connected data and, therefore, a real-time look at how buyers are making their decisions. Expectations are running high on how marketers can take advantage of this plethora of data streams. With Internet of things devices projected to exceed the 50 billion mark by 2020, do marketers have the right business solutions to keep up?
This is a question I explored during a recent in-house event held in conjunction with the University of Pennsylvania Wharton Customer Analytics Initiative. And the answer is, yes, they do, but they must integrate artificial intelligence into existing analytics.
Analytics in the Enterprise
Before hitting fast-forward on their data efforts, companies need to assess the state of their current analytics.
A guy who knows a thing or two about technology transformation, Jeff Bezos, once said, “if you are fighting analytics, you are fighting the future.” But when you go to describe the state of analytics at most enterprises, the words that come to mind may more likely describe hindrances instead of enhancements.
During the event, I touched on these five — static, reactive, siloed, opaque and rules-based. As data grows, different business units have different data — or different definitions of the same data — making it hard to move, share or analyze. But the data is still there, so expectations of how a company will perform based on its analysis are pegged high.
How can a company measure incremental sales in this disparate data landscape? Getting it all right in real time, to make a real impact with the buyer, is possible by using artificial intelligence as a business solution.
AI for Analytics
From housing it to gaining insights from it, AI can help businesses deliver on the promise of their data. Gartner predicts that by 2019, deep learning — AI’s self-improving grandchild — will provide best-in-class performance for demand, fraud and failure prediction. Enterprise needs to prepare for this revolution that is two short years away.
Big companies are already making significant technology investments in AI. Google, for example, used its DeepMind AI to cut the cost of its hardware energy consumption by 15 percent. By leveraging neural networks, Google determined the most efficient way to control for 120 variables in its data centers, analyzed through a series of sensors.
This same concept could be applied on the analytics side. Currently around 80 percent of time spent with data is used up simply manipulating the data. But if AI were leveraged for the analytics itself, companies could spin up the next generation of data analysis — self-service analytics. Instead of feeling deflated by these siloed business intelligence tools, self-service analytics will enable more business users to get marketing information from their data, exactly when they need it. This move will allow enterprise to focus on their buyer in real time and deliver on their business’ value.
Bring it In
Users are demanding analytic innovation. Instead of letting it happen outside your company, bring it internal, so your marketers can meet the high demands placed on them. Adopting AI creates a flexible strategy that can address both accelerating technology and the increasingly personalized needs of your buyers.
How does AI help deliver a better customer experience? Learn more here.
Mo Patel – Practice Director, AI & Machine Learning, Think Big Analytics
Mo Patel, based in Austin Texas, is a practicing Data Scientist at Think Big Analytics, A Teradata Company. In his role as the Practice Director, Mo is focused on building the Artificial Intelligence & Deep Learning consulting practice via mentoring and advising clients and providing guidance on ongoing Deep Learning projects. A continuous learner, Mo conducts research on applications of Deep Learning, Reinforcement Learning, and Graph Analytics towards solving existing and novel business problems. Mo brings a diversity of academic and hands on expertise connecting business and technology with Masters in Business Administration and experience as a former Management Consultant while also having worked as a Software Engineer with Masters in Computer Science and Bachelors in Mathematics.