Machine Learning and Smart Business
“In the context of business,” writes David Linthicum (@DavidLinthicum), a consultant at Cloud Technology Partners, “artificial intelligence is called ‘machine learning’ — that’s the best way to explain it in boardrooms. Machine learning can determine patterns, think about how those patterns are emerging, and even get better at predicting patterns before they emerge.” I believe that’s an oversimplification of what’s happening with artificial intelligence (AI) in the business world; but, Linthicum is correct that machine learning (ML) is playing an increasingly important role in many businesses. Big data expert Bernard Marr (@BernardMarr) observes, “Historically, when new technologies become easier to use, they transform industries. That’s what’s happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc.), more and more industries will put the technologies into use, and more and more startups will appear with new ideas of how to disrupt the status quo with these technologies. By my predictions, the AI revolution isn’t coming, it’s already here, and we’ll see it first in a few key sectors.” Those sectors, Marr asserts, are healthcare, finance, and Insurance. All of those sectors are particularly ripe for ML capabilities. Ed Burns (@EdBurnsTT) reports, “Machine learning algorithms and artificial intelligence tools are receiving a lot of attention in the analytics world these days, and industry experts and experienced users say the plaudits are well-deserved.”
Machine Learning needs to be Matched to Business Needs
“After being a niche technology for decades,” writes Craig Stedman (@craigstedman), “machine learning is stepping more into the analytics mainstream along with other forms of artificial intelligence, such as deep learning and cognitive computing.” Even though ML is the flavor of the day, Stedman rightfully insists, “Organizations need to find machine learning uses ‘that will have a meaningful impact’ on business operations.” Aman Naimat (@Aman_Naimat), Senior Vice President of technology at Demandbase, told William Terdoslavich (@williamtnyc), “There are many functions where machine learning can be applied effectively, marketing, drug discovery or patient monitoring are sweet spots for machine learning. [W]e should not apply machine learning to tasks where humans are very effective, like air traffic control at an airport. If a task is already optimized, incorporating machine learning would not serve to maximize any return on investment.”.
Linthicum suggests an inventory control system is a good example of an area ripe for ML. “You know that the amount of inventory you keep on hand is determined by demand,” he writes. “You also know you can do a fairly good job of predicting demand based on what the demand was last year and the year before. You might be able to pull off 65 percent accuracy. Machine learning takes this concept to the next level, in that you can change how the system needs to think via the patterns you detect. In the inventory example, you can also take into consideration economic data, weather data, changing demographics, the overall pattern of the business, and so on.” Using ML for inventory control would fall under the general heading of IT operations analytics (ITOA). Boštjan Kaluža (@bostjankaluza), Chief Data Scientist at Evolven, writes, “There’s been a significant increase in machine learning applications in ITOA due, in large part, to the ongoing growth of machine learning theory, algorithms, and computational resources on demand. Many organizations are finding that machine learning allows them to better analyze large amounts of data, gain valuable insights, reduce incident investigation time, determine which alerts are correlated and what causes event storms — and even prevent incidents from happening in the first place. In fact, machine learning can help cure a variety of IT pains.” Accenture analysts conclude cognitive computing (which leverages ML) will provide the “ultimate long-term solution” for many business challenges.
Don’t Wait too Long to Decide How to Use Machine Learning
Gary Cokins (@GaryCokins), founder of Analytics-Based Performance Management, suggests business leaders shouldn’t wait too long to determine how best to use ML in their business. “Many executives, managers, and organizations underestimate how soon they will be affected and the severity of the impact,” he writes. He continues:
“This means that many organizations are unprepared for the effects of digital disruption and may pay the price through lower competitive performance and lost business. Thus it is important to recognize not only the speed of digital disruption, but also the opportunities and risks that it brings, so that the organization can adjust and re-skill its employees to add value. Organizations that embrace a ‘digital disruptor’ way of thinking will gain a competitive edge. Digitization will create new products and services for new markets providing potentially substantial returns for investors in these new business models. Organizations must either ‘disrupt’ or ‘be disrupted’. Companies often fail to recognize disruptive threats until it is too late. And even if they do, they fail to act boldly and quickly enough. Embracing ‘digital transformation’ is their recourse for protection.”
Are cutting edge technologies right for your business? Deloitte Consulting analysts conclude, “For organizations that want to improve their ability to sense and respond, cognitive analytics can be a powerful way to bridge the gap between the intent of big data and the reality of practical decision making.”
Rob Marvin (@rjmarvin1) observes, “ML algorithms are embedded in the fabric of much of the technology we use every day. ML innovations spanning computer vision, deep learning, natural language processing (NLP), and beyond are part of a larger revolution around practical artificial intelligence. Not autonomous robots or sentient beings but an intelligence layer baked into our apps, software, and cloud services that combines AI algorithms and Big Data under the surface.” It’s not in our personal lives, but in our business lives, where we are feeling the greatest affect. He explains, “The trend is even more pronounced in business. ML is no longer solely used for specialized research projects undertaken by a team of data scientists. Enterprises now make use of ML to gain actionable business intelligence (BI) and predictive analytics from ever-increasing amounts of data. That’s why it’s more important than ever to be aware not solely of what ML is but also the most effective strategies in which to use it for tangible value.” Linthicum adds, “Does it make sense for your enterprise to use cloud-based machine learning? I think the answer for most is yes. Most business processes can benefit from the use of automated decisions that work with machine learning and good data analytics.”
 David S. Linthicum, “Machine learning hits the cloud — and more businesses,” InfoWorld, 4 October 2016.
 Bernard Marr, “3 Industries That Will Be Transformed By AI, Machine Learning And Big Data In The Next Decade,” Forbes, 27 September 2016.
 Ed Burns, “Machine learning algorithms set to transform industries,” TechTarget, 10 June 2016.
 Craig Stedman, “Match machine learning uses to bona fide business needs,” TechTarget, 10 June 2016.
 William Terdoslavich, “What Machine Learning Can (and Can’t) Do,” DMN, 7 March 2017.
 Boštjan Kaluža, “Machine Learning: Bridging the Gaps in IT Data Silos,” Datafloq, 13 December 2016.
 “From Digitally Disrupted to Digital Disrupter,” Accenture, 2014.
 Gary Cokins, “Visualization, analytics and machine learning – Are they fads, or fashions?” Information Management, 13 March 2017.
 Rajeev Ronanki and David Steier, “Cognitive analytics,” Deloitte University Press, 21 February 2014.
 Rob Marvin, “The Business Guide to Machine Learning,” PC Magazine, 28 April 2017.