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Data March 16, 2020
It’s easy to get lost in the array of technology terms bandied about by data science professionals and consulting firms. There’s artificial intelligence, machine learning, big data, data analytics, data science… and that’s just scratching the surface.
“Data science” itself is a big toolbox, within which we’ll often find several other terms — the tools of the data science toolbox, if you will.
Data science uses technologies and techniques like statistics, data analytics or data mining, and machine learning to turn large amounts of raw data into meaningful and actionable insights.
Machine learning, or ML is the aspect of data science we’ll focus on today.
You already encounter ML throughout your daily life. Virtual assistant systems, like Apple’s Siri, Google Assistant, and Amazon’s Alexa, all use ML to get better at understanding your queries and responding with relevant answers.
Your business could probably use some machine learning, too. But there’s more to it than just saying “let’s run machine learning on these spreadsheets!”
Properly implementing ML into your operations requires experience and expertise. Unless you’ve already got data scientists on your payroll, you might be better-served by working with a machine learning consulting firm.
Let’s discuss why ML should matter to your business, and how to make use of it the right way — without disrupting your operations, impeding your team’s progress, or even misusing good data to create bad outcomes.
Every business needs to make sales to survive, and those that use machine learning in their sales processes have reported significant improvements over previous baselines.
Salesforce found that high-performing sales teams are over four times more likely to use artificial intelligence (or AI) and ML applications than their peers. Management is big on ML as well, with 76% of sales leaders seeing higher sales growth after implementing ML in their organizations.
Machine learning’s value isn’t limited to improving a company’s customer-facing operations. The technology can also improve accounting and finance workflows, streamlining and automating many tedious data-entry and calculation tasks. It can improve supply chains by devising better routes, reducing inventory waste, and tracking machinery use for more effective and timely preventive maintenance.
Some of these descriptions might seem like artificial intelligence (AI) rather than machine learning, but it’s important to distinguish between these two closely-aligned technologies.
AI, like data science, is a larger umbrella under which machine learning resides. AI is an effort to develop technology that can think on its own and develop its own solutions without the need for human input; ML strives to give computer systems the tools they need to learn on their own.
Let’s look at how some successful businesses have used machine learning to improve their internal and external (customer-facing) outcomes.
Trillion-dollar companies aren’t the only ones enjoying greater success and customer engagement with ML. They’re also using ML in ways beyond creating smarter virtual assistants. Here are a few other examples of successful ML implementations:
The National Basketball Association is using machine learning to allow coaches to see which players are “bad shooters who take good shots” and “good shooters who take bad shots,” so they can adjust their game plans more intelligently.
Netflix credited machine learning with $1 billion in savings. The company’s improved recommendation algorithms kept more subscribers on the service, reducing churn and the costs associated with it.
PayPal uses a combination of several machine learning systems for risk assessment and fraud prevention. By automating risk assessment, the company can offer a much larger volume of loans to traditionally “risky” borrowers, improving returns without the need for a large loan-processing staff. ML-driven fraud prevention can block fraudulent activity before it starts by recognizing known fraud patterns in advance.
Yelp’s machine learning technology has been used to support its image-curation efforts, which need to handle millions of pictures taken by customers at thousands of businesses every day. Yelp’s curation team can categorize and label user-added photos far more efficiently with ML.
Most social networks make extensive use of machine learning to curate what users see on their feeds. Facebook, Twitter, and Pinterest all rely on ML to serve up content tailored to each user’s interests and activity history.
With machine learning, Amazon cut its average “click-to-ship” time — the time between a purchase and having that product prepared for shipment — from over an hour to just 15 minutes.
These are just a few of many ML success stories told in recent years.
Many of these companies, being tech companies at their core, worked hard to build their ML systems in-house. But if your company isn’t a technology provider (and even if it is), you probably don’t have the specialized talent to make ML work on your own. Less than 5% of data scientists working in the U.S. specialize in machine learning. Since over 150,000 data science jobs remain unfilled, finding a great ML professional before another company can lure them away can be a bit like finding a needle in a haystack, if the haystack happened to be on the moon.
One solution many companies have increasingly utilized in recent years is machine learning consulting.
A good machine learning consulting firm won’t try to push ML on you as a one-size-fits-all solution to every problem in your business.
Experienced ML professionals know the technology is just one tool in the data science toolbox, and it’s a particularly data-hungry tool that depends on high-quality information to provide meaningful solutions. If your company isn’t generating a ton of data, or you’re not particularly confident in the accuracy and quality of your data, you might need to start somewhere simpler.
Before you start your search, you’ll need to define the scope of the problem your business faces, and the structure and comprehensiveness of your intended ML solution. You’ll also need to determine if the quantity and quality of the data your business generates merits a ML solution.
A good ML consulting firm can help you figure these things out during an initial assessment period before you decide to move forward. Even if ML isn’t the best answer to your problems today, developing a data roadmap with your consultant’s help can help your business get ML-ready for the future.
Any consulting firm you retain should be able to provide records of its machine learning experts’ skills and experience when asked. With so few ML experts on the market, a true pro should be easy to spot. Here are some basic qualifying questions to ask:
If you’ve done some research, asked the right questions, and committed yourself to understanding the details behind your business’ data, you’ll be well on your way to making the right choice.
Machine learning isn’t critical for every business, but it’s becoming an increasingly important element of modern business intelligence practices. A growing business creates a flood of data, and making sense of that torrent often requires expert assistance.
Many businesses already use machine learning in their work without realizing it. Salesforce utilizes ML and AI throughout its suite of services to provide smart automated insights, reduce manual effort, and streamline complex operations. You’ve probably seen what machine learning can do, but if you want to make the most of this technology with a solution purpose-built for your unique business needs, get in touch with RTS Labs today. We’ll help you discover what machine learning can really do, and what it can mean to your company.
Contact us to talk about how we can help.