Business executives are still trying to figure out how best to leverage machine learning. They are finding it’s not always easy to separate hype from reality. Back in 2016, Lukas Biewald (@l2k), CEO of CrowdFlower wrote, “There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I’ve [repeatedly] heard that old joke ‘Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it’.”[1] As I will discuss below, some organizations are doing machine learning. At the highest level, machine learning is about making better business decisions. Mary Branscombe (@marypcbuk) explains, “Business intelligence has gone from static reports that tell you what happened, to interactive dashboards where you can drill into information to try and understand why it happened. New big data sources, including Internet of Things (IoT) devices, are pushing businesses from those reactive analytics — whether you look back once a month to spot trends or once a day to check for problems — to proactive analytics that give you alerts and real-time dashboards. That makes better use of operational data, which is more useful while it’s still current, before conditions change.”[2]

Machine learning and advanced analytics

When Branscombe writes about “proactive analytics,” she refers to the three most advanced types of analytics. George Karapalidis (@gkarapalidis), head of data science at Vertical Leap, explains there are four different types of analytics that can be applied to data depending on the type of data and the desired result.[5] They are:

1. Descriptive analytics. Descriptive analytics can help discover what happened in the past. As Karapalidis puts it, “Before we learn where to go, we need to know where we came from. That’s the key question descriptive analytics solutions tackle.”

2. Diagnostic analytics. Diagnostic analytics can help explain why something happened. Karapalidis writes, “Diagnostic analytics tools help you uncover the root cause of some problems.”

3. Predictive Analytics. “Predictive analytics,” writes Karapalidis, “‘joins the dots’ between the accumulated and analyzed data points, conveying what and why something happened, into models suggesting what can happen next. It indicates the probability of certain outcomes with high accuracy and takes the guesswork out of your decision-making process.” Mark Dunn, Director of Nexis® Data as a Service at Nexis Solutions, adds, “As machine learning and predictive analytics become more sophisticated, companies can base decisions on evidence, and deep learning will push the boundaries even more, with better problem-solving and language comprehension.”[6]

4. Prescriptive analytics. Prescriptive analytics informs you what you should do to achieve a particular outcome. It’s a type of analytics made possible by the emergence of cognitive computing technology. Karapalidis notes, “Prescriptive analytics is yet to move from the margins to the mainstream. It’s an emerging area of analysis attempting to answer the complex question of ‘what actions to take if I want to get outcome A?’ Prescriptive tools come up with multiple future outcomes based on your current/past actions; match those futures with your goal and advise you on the action you need to apply.”

Of course, to extract value using analytics you need the right data for the right model. Obtain the right data, identify a specific problem, and apply the right analytics and you get the results for which you are looking. It’s not hype, but it’s not as easy as it sounds. Alan Descoins (@dekked_), CTO & partner at Tryolabs, suggests executives ask eleven questions before starting a successful machine learning project.[4] They are:

1. What are your organization’s business goals?
2. Should machine learning reduce costs or increase revenue?
3. Which is your clear and realistic way of measuring the success of your ML initiative?
4. How does your organization handle the risk?
5. How do you acquire the right talent?
6. Do you have a clear high-level understanding of what machine learning is?
7. Is access to information guaranteed?
8. Have you planned the initiative as a mid-term project?
9. Is your organization collecting the right data?
10. Is your organization collecting the data in the right format?
11. Have you taken human labeling of data into consideration?

For a fuller discussion of each question, read Descoins’ article. He concludes, “Machine learning algorithms are changing nearly every industry. They’re increasing productivity, boosting sales and helping us make more informed decisions.”

Machine learning business applications

As I noted at the beginning of this article, some executives have difficulty separating hype from reality. Below is a notional list of machine learning applications currently in use.

• Making user-generated content (think social media) valuable
• Traffic prediction
• Preventive maintenance
• Finding products faster
• Text translation
• Customer engagement
• Insights into customer behavior
• Product recommendation
• Spam detection
• Fraud prevention
• Personal assistants
• Autonomous vehicles

Biewald observes, “Most of the conversation about machine learning in popular culture revolves around AI personal assistants and self-driving cars (both applications are very cool!), but nearly every website you interact with is using machine learning behind the scenes. Big companies are investing in machine learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI.”

Concluding thoughts

The question is not “if’ companies will leverage machine learning in the future but “how” they will leverage it. Dr. Nipa Basu (@nipabasu), Chief Analytics Officer at Dun & Bradstreet, concludes, “Machine learning is changing the way businesses look at data and presenting new analytics opportunities for companies of all sizes. Increasingly, how organizations leverage new technology for machine learning in business will be a key deciding factor in whether they can ride the waves of change or find themselves washed up on the data analytics shore.”[5]

Footnotes
[1] Lukas Biewald, “How real businesses are using machine learning,” TechCrunch, 19 March 2016.
[2] Mary Branscombe, “Why machine learning is the new BI,” CIO, 20 April 2016.
[3] George Karapalidis, “Examining the four types of big data analytics,” The Drum, 18 December 2018.
[4] Alan Descoins, “11 questions to ask before starting a successful machine learning project,” Customer Think, 25 February 2019.
[5] Nipa Basu, “3 ways to make machine learning in business more effective,” TechTarget, October 2018.