If you’ve been following technology trends, you’ve likely heard about the hype around machine learning and how data-driven decision-making helps organizations to flourish. Because of unconventional software implementation approaches and the absence of a specific roadmap, the number of questions arises about introducing this technology to any business. Is it possible to reduce costs or increase revenue leveraging machine learning technology? Can it help you leverage profound insights? Is this the right time for you to jump on the bandwagon?
It all goes back to the 1950s when Alan Turing created a now well-known “Turing Test” to find out if a computer has real intelligence. A lot of water had to flow under the bridge before this field started being used for finding required patterns among data.
As a sub-field of artificial intelligence, machine learning uses statistical models to “teach” algorithms autonomously learn from data without explicit programming. Like in the sci-fi movie, algorithms make it possible for computers to communicate with humans, autonomously drive cars and improve cybersecurity to find terrorist suspects.
Machine learning has been used in customer lifetime value prediction, predictive maintenance, financial analysis, image recognition, cybersecurity, and the list is far not exhaustive.
Fintech seems to be taking advantage of this technology a lot, as machine learning is widely used in financial data modeling. Nowadays, banks have an incredible opportunity to go further than using traditional methods for data modeling. Adoption of the ML methodologies in risk modeling, portfolio management, and algorithmic trading opens a door for making fast decisions in trading (so-called “high-frequency trading”), provides the ability to train algorithms on millions of examples of consumer data and behavior (e.g., delinquency, late payment or balance usage history) to detect trends that might influence the future.
In spring 2015, NBA league used the analytics generated by Second Spectrum, a Machine Learning start-up based in California. With the help of predictive models, the jury was able to distinguish between more and less skilled contenders which influenced the decision-making process in a positive way.
The manufacturing industry can use machine learning to identify meaningful patterns in factory data. Predictive maintenance reduces the risk of unforeseen failures and the quantity of needless preventive maintenance operations.
The reason behind machine learning popularity is that it enables big data processing. However, it’s not a magic wand that will make all of the data work for your good without a solid strategy.
Businesses face multiple obstacles along the way, and you have to find out what stops your company from receiving higher revenue. For instance, if you notice that duplication of data or inaccuracy is a major problem for your organization you might want to automate the processes leveraging machine learning predictive algorithms. Using discovered data, machine learning enables systems to learn how to deal with time-intensive documentation and data entry tasks. This approach frees up employees’ time for higher-value tasks.
When incorporating machine learning to the strategy, companies go through the stages of data collection, patterns discovery & outcome expectation and the application of the insights. If the first two are more or less obvious, the final stage might be somewhat confusing. How are you going to apply the data you’ve collected?
Take General Electric. A multinational conglomerate company now generates millions thanks to the data collected from deep-sea oil wells and jet engines. Knowing what to do with the obtained data helped the organization to optimize performance and receive even more profound insights for the next steps. The data application can’t be automated and require specific actions due to different issues that arise across organizations.
Poor quality training data for your machine learning model can bring destructive results. To generate high-quality data and make the perception model successful it is required to thoroughly label the information like text, images or any other features that must be included.
Depending upon the project, specialists use either standard or detailed quality testing that allows monitoring the quality of data annotations, measuring goals and fighting bias. Even if data is unstructured, experienced data scientists can clean a mess and optimize your data set for successful modeling.
Data scientists, the new superheroes rank number 1 in LinkedIn’s Top 10 and are in demand not only in Silicon Valley. The unique skillset combination makes data scientists expensive talents. According to Bloomberg, the starting salary for a data scientist is about $200,000 in NY or Silicon Valley, which makes companies opt for offshore outsourcing.
Until this is mainstream you have a chance to be an early adopter of machine learning technology. To decode the new, businesses need to use the right technologies to optimize processes and operations and unleash new levels of data leverage. If you feel this article is talking to you, join the machine learning bandwagon today!