What’s keeping business leaders from adopting machine learning (ML)? According to Deloitte analysts David Schatsky (@dschatsky) and Rameeta Chauhan, “[The] tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort.”[1] Julian Box (@The_Cloud_Box), founder and CEO at Calligo, suggests costs, availability of data, security concerns, compliance risks, and ethics are other reasons businesses don’t deploy machine learning.[2] Whatever discomfort business leaders feel, Eric Siegel (@predictanalytic), a former computer science professor at Columbia University, believes they should buck those feelings and embrace machine learning. He explains, “Machine learning … is genuinely powerful and everyone oughta be excited about it.”[3]
Box agrees companies should get excited about machine learning because it has wide applicability. He explains, “When it comes to ML, its power is its incredible flexibility. … ML is so flexible it can be used to make improvements anywhere there is data. This puts the onus on businesses to creatively self-identify their own use cases.” Box admits many business leaders face a conundrum when it comes to implementing machine learning, they believe it can help their business but how it can help “can be hard to [determine] when technology is complex, new and multi-faceted. After all, how can you use a tool you don’t fully understand?” And the editors at eWeek warn that trying to implement a machine learning project without that understanding isn’t wise. They explain, “It’s easy to get caught up in the hype surrounding AI and machine learning, with business leaders chasing shiny objects for an AI application that might have little to do with critical business goals.”[4]
Machine Learning Use Cases
One way to gain a better understanding of how machine learning can be leveraged is to see how other companies are using the technology to drive business value. The editors at eWeek assert, “The ultimate goal of machine learning shouldn’t be a flashy, futuristic example but instead a system to drive revenue and results for the business. The result of effective machine learning isn’t likely a robot, chatbot or facial recognition tool — it’s machine learning-driven programs that are embedded behind the scenes, driving intelligent decisions.” Journalist Amelia Brust suggests one of the best uses of machine learning is augmenting decision-making. “Machine learning and artificial intelligence,” she writes, “were intended to make people more productive, not replace them.”[5] Making people more productive can mean providing them with actionable insights as well as relieving them from having to make routine decisions. This is why Enterra Solutions® is focusing on advancing Autonomous Decision Science™ (ADS®). The Enterra ADS® platform can also be used to capture expert knowledge. When we talk to companies who have an analytic problem, they typically approach the challenge by assembling a team of three experts:
• A business domain expert — the customer of the analysis who can help explain the drivers behind data anomalies and outliers.
• A statistical expert — to help formulate the correct statistical studies, the business expert knows what they want to study, and what terms to use to help formulate the data in a way that will detect the desired phenomena.
• A data expert — the data expert understands where and how to pull the data from across multiple databases or data feeds.
Having three experts involved dramatically lengthens the time required to analyze, tune, re-analyze, and interpret the results. Enterra’s approach empowers the business expert by automating the statistical expert’s and data expert’s knowledge and functions, so the ideation cycle can be dramatically shortened and more insights can be auto-generated. Entrepreneur and tech expert Henry Peter, a member of the Forbes Technology Council, sees great benefits from this approach. He states, “A creative way of leveraging business intelligence and insights … derived from machine learning … is to connect those insights to actionable layers of business operations. This includes notifications and an automated flow of actions that can inform many of the business stakeholders, leading them to act on the gained insights.”[6] Other members of Forbes Technology Council suggest a few more ways companies can leverage machine learning.
• Mapping out and neutralizing threat patterns. Mirza Asrar Baig, founder and CEO of
CTM360, states, “I believe the best use of machine learning in this digital age is preventing cyberattacks from happening at an early stage. … With the help of AI, machine learning, big data and threat intelligence, we can further understand and map out threat patterns to neutralize threats early.”
• Automating routine tasks. Trisha Swift, a Managing Director at PwC, suggests, “One easy starting point for applying ML is in automating routine and repeatable tasks. … Leveraging ML to ease administrative burdens and automate tasks can help build up internal consensus for its return of overall business value.”
• Improving payment processes. Rob Harvey (@robharvey3), Chief Product Officer at Sidetrade, asserts, “Traditionally seen as siloed or back-office processes, finance and sales have been transformed by ML into a unified operation. With ML-powered order-to-cash processes, you get predictive analytics, better cash forecasting, and ML-generated selling and dunning strategies that increase the top and bottom lines.”
• Leveraging advanced analytics. Machine learning expert Julian Jewel Jeyaraj (@JulianJewel) observes, “The speed of innovation and AI’s sizable economic impact will render businesses that are ignorant of their opportunities obsolete. Business leaders should therefore keep up with the sheer speed of innovation and make quick investment decisions in technology. The one creative use of BI and ML that leaders can benefit from is to turn data into efficiency through advanced predictive analytics.”
• Enhancing customer experience. Chris Menier, a Vice President and General Manager at Vitria Technology, insists, “Improving customer experience is one area that will greatly benefit from machine learning and BI. Leaders can apply analytics to predict customers’ support needs in real time, identify the root cause of service-impacting issues and more efficiently triage outages.”
• Tapping into real-time intelligence. Shruti Bhat (@ShrutiBhat), Chief Product Officer and Senior Vice President of Marketing at Rockset, observes, “While business intelligence has been around for a while, the new era is all about real-time intelligence.”
Obviously, this list in not exhaustive. As Box pointed out, “ML is so flexible it can be used to make improvements anywhere there is data.”
Concluding Thoughts
The editors at eWeek conclude, “Machine learning has the power to fully transform an enterprise. Therefore, it’s natural for business leaders to get lost in the hype and lose sight of the real value it can deliver day-to-day. The truth is, the real value of machine learning is that it allows businesses to try new things, amplify creative strengths, reveal new discoveries and enable collaboration across the organization. However, these benefits will only be realized once organizations get past the hype and are willing to walk into the weeds.” Vyacheslav Gorlov is the Senior Solutions Architect at ClearScale, adds, “It’s important to remember that machine learning is a tool — it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.”[7] If you’re not excited about the possibilities presented by machine learning, you should be. Your business could depend on it.
Footnotes
[1] David Schatsky and Rameeta Chauhan, “Machine Learning and the Five Vectors of Progress,” The Wall Street Journal, 24 January 2018.
[2] Julian Box, “Why Businesses Don’t Deploy Machine Learning (And How to Overcome It),” EnterpriseAI, 26 March 2021.
[3] Eric Siegel, “Why A.I. is a big fat lie,” Big Think, 23 January 2019.
[4] Editors, “Using Machine Learning to Drive Business Value,” eWeek, 26 April 2020.
[5] Amelia Brust, “Using machine learning to ‘automate’ employee expertise,” Federal News Network, 23 May 219.
[6] Forbes Technology Council, “14 Creative Ways Companies Can Leverage Business Intelligence And Machine Learning,” Forbes, 21 October 2020.
[7] Vyacheslav Gorlov, “Five common use cases where machine learning can make a big difference,” AI News, 3 March 2021.
