“Being able to predict an outcome achieves only about half of the potential value of ML, whereas knowing what to do in order to optimize an outcome delivers full value.” — Mark Sangster, VP, Chief of Strategy, Adlumin, “Machine learning is demonstrating its mettle across industries”
As companies see the value of Machine Learning in all its predictive glory — and witness ML use cases proliferating across business units and industries — goals indeed expand. Whereas ML’s early benefits include automation-related efficiencies and revenue increases, ML’s promise continues to unfold. With unprecedented capacity to identify patterns in data, ML has revolutionized how industries:
- manage and utilize data
- harness its power for data analytics; predicting an outcome lays the groundwork for optimizing an outcome.
- deploy actionable insights; time to impact is accelerated to the point where companies often see overnight progress.
Cross-Industry Approaches with Machine Learning
Many industries now deploy some form of ML, and some industries benefit exceptionally, according to a recent article in Forbes “14 Tech Experts Predict Which Industries and Sectors Will Benefit Most From AI.” Such industries include healthcare, cybersecurity, logistics, financial services, manufacturing, and e-commerce and customer service. The more ML transforms an industry, the more positive its impact on humans. Healthcare is a prime example, as I outline in Health Tech World’s The Power of Machine Learning for Diagnosis, Treatment and Preventive Healthcare. By leveraging large-scale data, ML can improve precision medicine and drug discovery, help physicians predict surgical complications, and even help patients stick to healthy habits thanks to ML-driven personalized nudges.
In this dynamic environment where the bar is always rising, it’s helpful to keep in mind two key points:
1) Baby steps are effective. Even as ambitions for implementing ML “may be boundless, the steps cannot be too small,” according to a recent Harvard Business Review (HBR) report titled “What Makes a Company Successful at Using AI?” The report showed that when manufacturing and operations companies found success with ML, they tended to start by using data and simple tools to make decisions in forecasting and logistics, for instance. Teams then moved to “more advanced [machine intelligence] techniques as they built maturity and familiarity with their data,” say authors Bruce Lawler, Managing Director for MIT MIMO (Machine Intelligence for Manufacturing & Operations), and Vijay D’Silva, senior partner at McKinsey & Co.
The takeaway for all companies who ultimately want to become leaders: “Not everyone should strive to be a leader immediately; they should instead strive to move to the next better state,” say Lawler and D’Silva. This sage advice sounds like the process of ML itself—iterative and adaptive, moving by force of its built-in intelligence toward improvement.
2) The democratization of data is key as ML is implemented throughout a company and becomes “business-as-usual.” ML applications are becoming more user-friendly as ML use cases proliferate and as data silos break down and non-specialists gain greater access. The HBR report found that companies who led the way on ML usage “acquired data from customers and suppliers, and shared their own data back.” Many trained their front-line personnel in ML fundamentals. Leaders often enabled remote access to data and stored a large portion of their data in the cloud. “In short, the democratization of data is a critical aspect to the effective use of analytics.”
As long as the data is clean, the democratization of data can improve decision-making, operational efficiency, and client service. ML-driven apps for data cleansing, such as DataGroomr, that are also user-friendly are ideal for implementation across business units and industries.
Industries Showing Improved Data Utilization through ML
It’s helpful to consider how ML is shaping, and reshaping, industries in order to move in the direction ML is headed—knowing how to optimize outcomes, as noted (above) by Adlumin’s VP of Strategy Sangster. Here’s a snapshot look at some industries experiencing benefits from ML:
Healthcare
ML can augment the intelligence of doctors and help in the areas of diagnosis, treatment, and disease prevention. Specifically, it can:
· improve diagnostics; in an experiment using a causal-reasoning algorithm mimicking the decision-making of clinicians, a machine achieved “expert clinical accuracy” rivaling the top 25 per cent of doctors, according to a recent study in Nature Communications.
· identify new uses for existing drugs—a practice called drug repurposing—and thus expedite the development of drugs
· provide more targeted treatment by leveraging large-scale data, including molecular data, to help classify patients into subtypes
Pharmaceutical
According to “AI in the Pharma Industry: Current Uses, Best Cases, Digital Future, published in PharmaNewsIntelligence, ML can:
· speed up drug and medical device discovery and development
· enable drug repurposing
· accelerate the research and development timeline, making drugs more affordable and increasing the probability of FDA approval
· improve processes, including performing quality control and predictive maintenance, reducing materials waste, and helping to forecast and prevent over-demand and under-demand
Manufacturing
According to HBR, ML in manufacturing & operations can be used for:
· forecasting
· predictive maintenance/maintenance optimization
· logistics and transportation
· product quality assurance by applying machine vision
Cybersecurity
According to Kayne McGladrey, cybersecurity strategist at Ascent Solutions, ML can:
· provide automated threat analysis, eliminating false positives
· quickly focus a person’s attention on the signal, not the noise, so that organizations can rapidly respond to potential incidents before threat actors can establish persistence in an environment
Sales and Customer Service
According to Salesforce and Noelle Silver, founder of the AI Leadership Institute and an AI Executive at IBM, ML in sales and customer service can be used for:
· providing automated customer service agents
taking the customer touch point and predicting the next best action based on user activity
· enhancing customer engagement based on customer data, such as dynamically creating better pricing strategies or selecting better products to cross-sell and up-sell
· identifying leads most likely to become clients
· proactively emailing users who appear to be searching for a product on a company’s website
· helping agents become more effective by recommending resolutions in a shorter time
Ready for the Revolution
“AI will affect every single industry,” according to the Forbes article, so “it’s important to ensure that your company is ready for AI. It means a big transformation.” Recognizing that machine learning can be an expert ally in the quest for human improvement can help to set you on your path.
When it comes to data management, DataGroomr has been at the forefront of developing algorithms through the use of machine learning to quickly and intelligently identify and eliminate duplicates from Salesforce data. Read our blog, The Science Behind Machine Learning to Keep Your Data Clean to learn more about how ML works within the DataGroomr app. Then begin a free trial to experience the results for yourself and your business.
