How can Machine Learning bring your business to the next level?

“Machine learning and AI … will empower and improve every business, every government organization, every philanthropy — basically there’s no institution in the world that cannot be improved with machine learning.”

Jeff Bezos

Where will the data drive you?

No matter what business you are in, you most probably have heard that data-driven companies are more prosperous, more profitable, and confident. And these rumors are undoubtedly credible. Data mature businesses are 162% more prone to exceed their revenue goals than those less sagacious. Regardless of their size, most companies plan to invest more in data management in the next several years. Still, at the moment, almost half of businesses don’t use data to make commercial decisions.

We are starting to realize that those who rule data will rule the world. It seems to be the slogan of the age we live in — the Information Age. And yet it’s not clear how to rule it (the data, not the world), even the data you already possess. Gathering and storing is obviously not enough anymore. Data collection is just a component, the first, crucially important step on the road to success. As with all other assets, you need to make the data work for you. However young and/or small the company you are, it’s pretty certain you have managed to save enough to call it big data, and most probably, you don’t realize its full potential. Moreover, we have come up with a novel term — Big Data as a Service, which helps companies use insights obtained from large information sets. The demand for processing the data affects the supply, which results in a growing market of tech companies that are happy to offer help in improving your business intelligence.

Can a machine learn?

In fact, business intelligence involves the procedures and technologies used by companies for the data analysis of its information. Being the first step in Machine Learning, Data Analysis is a process of understanding the data, finding patterns, and trying to obtain inferences due to which the underlying patterns are observed. Machine Learning starts when you “train” a system to learn those patterns to be able to predict the upcoming pattern. It actually teaches computers to do what we, until recently, believed only humans can do — learn from its own experience. ML algorithms use specific computational techniques to “learn” new information straight from data without using a predetermined equation. Multiple machine learning applications are formed through a complicated algorithm or source code built into the machine. It creates a model that identifies the data and makes predictions around the data it identifies. The model then uses built-in parameters to develop patterns for making decisions. When further information is available, the algorithm adapts the parameters. Machine learning is based on the ability to use machines to examine the data for a structure without a theory of what that structure is.

There are two main techniques of ML — unsupervised and supervised learning. As you can guess from its name, the supervised one uses classification and regression techniques to produce predictive models, while unsupervised techniques have no training labels for the samples as the algorithms themselves can find a suitable arrangement and patterns in the data given. Also, there are semi-supervised and reinforcement learning methods. The latter, for example, is used for playing games like chess and Mario, for traffic light control, robotics, and many more.

Deep learning is a subcategory of ML. It also can be supervised, unsupervised, and reinforcement. The “deep” in DL originates from using multiple layers in the network and is contrasted to Shallow Learning, where we have an input layer, one or just several hidden layers, and an output layer. “Deep learning is in fact a new name for an approach to artificial intelligence called neural networks, which have been going in and out of fashion for more than 70 years” explains Larry Hardesty, an author of MIT News Office. Artificial neural networks are based on studies of the brain and nervous system. Inspired by nature, it simulates the way our brains analyze and process information.

Big money — big data

We may dive deep into theory, but what are the practical examples of machine learning in businesses? The classic cases are mesmerizing. They show great companies getting even greater.

For example, Facebook applies Machine Learning to recognize our faces, understand and translate the texts we write, show us posts and ads we will most probably be interested in. It even guesses the people you might be familiar with in real life using “People You May Know”. And it is mostly right! Machine Learning algorithms are used to analyze our profiles, interests, friends, and various other factors, like public figures or businesses that you interact with a lot.

Talking about the e-commerce giant Amazon, most of us would first think of Alexa voice assistant and the machine learning behind its autonomous delivery drones. But recently, Jeff Bezos pointed some of the less promulgated ways of using artificial intelligence and machine learning to grow its marketplace. They aggregate and analyze purchasing data on products, which enables them to forecast demand more accurately. The company also applies machine learning to interpret purchasing patterns, thus identifying fraudulent purchases. Paypal, by the way, uses the same approach, which results in a 0.32% revenue fraud rate — a good result compared to the 1.32% industry average. Moreover, Amazon uses searching and shopping information to provide unique product suggestions and ads, which is similar to what Facebook does.

When it comes to Netflix, the most notable feature is Personalization of Movie Recommendations. It uses the watching history of other users with similar tastes to recommend what you may be most interested in watching next. But there are many other usages, some being less obvious and more technical. Let’s take Streaming Quality. The company uses past viewing data to forecast bandwidth usage, which helps decide when to cache local servers for speedier load times during expected peak demand.

Apple’s artificial intelligence chief John Giannandre claimed Apple uses machine learning in practically every aspect of how we interact with our devices, and there is much more to come. “There’s a whole bunch of new experiences that are powered by machine learning. And these are things like language translation, or on-device dictation, or our new features around health, like sleep and hand washing, and stuff we’ve released in the past around heart health and things like this” — explains Giannandre. He believes there’s no corner of iOS or Apple experiences that will not be changed by machine learning over the next few years.

An industrial automation expert Siemens uses ML to implement predictive maintenance applied to the automation system of choice in factory settings and industrial machinery. Hence, possessing vast amounts of data, the foundation for establishing supervised machine learning is already settled. To implement Predictive Maintenance at cooling systems of the NASA Armstrong Flight Center, Siemens operated analytics services provided by Azima DLI, a US-based provider of predictive machine condition monitoring and analysis services. The cooperation was beneficial to Siemens as Predictive Maintenance delivers a great return on investment, it’s less expensive than reactive maintenance and can save on recurring costs. In another project, Siemens started a 12-month Predictive Maintenance pilot with Deutsche Bahn to watch the fleet of their trains.

Small but smart

Yet, don’t be fooled into assuming that advanced technologies like AI and machine learning are only for FAANG and similar-scale companies. Technology businesses offer machine learning options that meet small and mid-sized businesses’ demands and budgets. The Intuit® Emergent Research study, “Small Business in the Age of AI,” shows that 66% of US small businesses already use automation technology, such as big data analytics, natural language processing, machine learning, and artificial intelligence.

They have unique advantages because of their size. They can adapt more quickly, be more flexible, and benefit from new opportunities faster by exceeding customers’ expectations. The majority of companies see technology primarily as an opportunity and expected a positive impact on it. Automation frees up their time, helping them become more innovative, provide better service, and improve management. And for those of you who are afraid it will either increase or decrease the number of employees significantly — no, it won’t.

But what can machine learning do for a small company?

• Help you make decisions. ML can analyze your supply chain efficiency for smarter shipping and better warehouse management.
• Decrease risk by gathering data on processes and monitoring for human error so that businesses can build better and safer procedures.
• Improve sales performances. Website sales chat solutions may help start chats with visitors on your site. The chatbot begins a dialogue with a visitor and hands off the interaction to a live agent.
• Streamline marketing. Small organizations get a notable result by using online marketing solutions empowered by machine learning and AI. Modern buyers are looking for tailored, relevant customer experiences, and AI can help small companies deliver it. Personalized marketing may raise your revenue by 38% by showing your clients ads based on their website’s activity. Machine learning allows it by processing customer data and making predictions on what to recommend.

Usage of machine learning may be your secret weapon, competitive edge or even your key advantage. Ai-Tradex combines AI, Deep Learning, Machine Learning, and Big data to make the best decisions for investing in stock exchange. Betting Kings works to allow its members to invest in sports asset classes by leveraging AI, analytical tools, and market trends that help invest in the right sports investments for their portfolios. Industrial robots Photoneo use Machine Learning in its bin picking solution AnyPick. It picks randomly located and mixed objects of multiple shapes, weights, and sizes. Because ML drives the solution, it does not require any CAD models or any other information. AnyPick can make up to 500 picks per hour, which increases its efficiency and decreases costs, making it great for applying in e-commerce, warehouses, logistics, food industry, metallurgy, and others. Viz helps doctors to identify abnormalities in brain scans using ML. The company uses high-level deep learning to communicate data about stroke patients directly to those who can treat them. A commercial real estate maintenance company Enertiv benefits from predictive maintenance, thanks to collected data. IoT predictive maintenance, which the company uses, lowered maintenance costs at about 25%, with a 50% decrease in major breakdowns and stretched equipment life 20 to 36%.

Examples are endless, no matter what field we take: from marketing to medicine, from finance to fitness. The benefits are easy to see and simple to monetize. The questions for companies of any size is not whether they need to apply machine learning to improve their business intelligence, but when and how they should do it. The When defines if the next success story is going to be yours or your competitors’. The How helps to foresee the number of complications you will need to deal with implementing Data Science solutions.

Practical advice on How (to be more data-savvy business)

Human learning matters

It’s not just engineers who need to understand the company’s technologies, especially when it comes to something as innovative as Artificial Intelligence. Executives have to comprehend the power of AI to understand how it can help them achieve their business objectives. The technology may allow them to imagine previously unimaginable applications. If you want your company to become better (by using AI), it will be a great idea to learn a bit about the technology yourself. Taking a short introduction course to AI may be an appropriate solution. Let’s have a look at AI for Everyone from Coursera. The course explains common AI terminology, helps you build a realistic idea of what AI can and cannot do. It also guides into recognizing opportunities and gives a glimpse of what it takes to create data science projects.

Collect the data correctly

Many companies find it challenging to aggregate the data properly, even though they are aware of its importance. You need to accept the idea that there is never too much data collected, and the demand will only continue to increase. But where do you start? A critical first step is to carry out a data audit to determine what type of information you possess and where in your company it is located. Next, you can deal with different company units to obtain access to it. In addition to gathering the internal information, it’s essential to recognize the external data which is most relevant to your business. There are even cases when only external data can address general issues, such as discovering current buying patterns.

In-house vs. outsource

One of the most important decisions you are to make on the way to Business Intelligence is whether you plan to build an AI solution in-house or outsource it to your partners. Each of these approaches has its benefits, and it’s vital to consider them to make the right choice.

Talking about an in-house solution, it may seem more flexible and customizable. You also won’t need to explain your goals to anyone and from the outside. But independence has its price. To build expertise within the company, you’ll need experts first. Looking for them, hiring, training, and creating the whole new team is costly and time-consuming. The worst thing is that the result isn’t guaranteed, and the entire project may even appear to be a burden for your IT department.

Cooperating with external partners is a standard answer for those wishing to avoid the complications caused by building an in-house AI solution. Companies specialized in AI already have experienced specialists who work in effective teams. What is a novelty for you is their daily routine, their sphere of expertise. The cooperation with them will probably save you from making mistakes and wrong decisions, which is so expensive and frustrating. Outsourcing usually saves you time and resources, frees you of unnecessary burdens so you can concentrate on setting and achieving your business.