Machine Learning Guide for Executives

This article is aimed at providing business leaders with a better understanding of Machine Learning and what business value it could bring to an organization.

 

In 2021, the world of business is more technology-driven than ever. Seemingly every day we can hear news of some company having a breakthrough in a particular industry. What makes a business move a success? This question requires a complex answer, but it is safe to assume that effective decision-making today requires keeping a finger on the pulse of the technology. Additionally, the proper and timely execution can be a crucial factor to taking the lead with the idea that is already developed by different companies in your niche.

Among the technologies that are making a paradigm shift in some industries, are definitely Artificial Intelligence and Machine Learning. There are plenty of use cases that already proved the business value of these cutting-edge technologies, which became super-effective for some organizations in improving the bottom line and optimizing processes.

This article might be helpful for those, who are interested in business intelligence innovations, the modernization of legacy systems, and the ones who want to make sure to increase the ROI of their product. If you are an executive of an organization on the path of digital transformation, this guide on Machine Learning will help you understand the main reasons to turn your organization into a data-driven company and how to convert information into an additional revenue stream.

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Why is data collection so important for organizations in the first place?

Let’s answer this question by explaining the four main advantages that data collection can bring to an organization of any size and operating in any industry. Of course, you can think of more benefits for your particular case, but these advantages can be unlocked by anyone.

Better decisions

Proper data analytics can be a key driver for correct and timely decision-making. This is what 49% of respondents said in this survey. These are predictable results because even the smallest organization generates an enormous amount of information that can turn into actionable insights. If you have a marketplace, for example, that operates globally and ships products all over the world, you can extract an impressive amount of data daily. But what about global enterprises? The amount of data they have is astonishing. Making your decisions based on correct data is one of the most important things you can do to make your business strategy a winning one.

Making sense of the performance

Speaking of large enterprises, sometimes it is even hard to understand what factors lead to certain outcomes, because of many elements in the processes. Analyzing the data, you can pinpoint exactly what department in your company damages your business results, and find the ways to fix that.

Optimizing the operations

Obtaining an understanding of your internal operations is the first step to making them better. Every company has its weak points that influence performance negatively. It can be just one process or the entire department. You can increase ROI by reinforcing the areas of your business that have room to grow.

Improved marketing

This area is especially dependable on the accurate information and awareness of the current situation on the market. You can make a fortune by knowing what exactly your customers want and predicting their next possible preferences and actions.

Big data is watching you

Machine Learning will help you to manage and get an advantage out of data

There is a major flaw in data collection for organizations—there is simply too much information that a business can collect. Every day the world receives around 2,5 exabytes of new information, while the overall amount of data across the globe is estimated to exceed 45 zettabytes. Every person nowadays produces data, a lot of data, as well as even the smallest organizations. As a business leader, you can try to make a conclusion thanks to manual analysis, or get help from the team of analysts. But in order to detect the smallest nuances and extract value out of information, it is better to use some kind of automated solution. Machine Learning technology is one of the best ways to achieve this.

Basically, Machine Learning (ML) is a branch of Artificial Intelligence technology that focuses on teaching computers how to learn without being explicitly programmed by humans. In recent years, the advancements in technologies, especially the breakthroughs in Big Data, led to the growing popularity of Machine Learning. Contrary to popular belief, Machine Learning does not require a big amount of data, in some cases. Some challenges can be dealt with using the right algorithms and meaningful data. However, every ML project requires meaningful data, time for data analysis, and coding. You can take Machine Learning as a long-term investment that can benefit your business greatly in the future when the full potential of the technology will be uncovered. The most effective ways to use Machine Learning in your company include:

Analyzing customer behavior

Speaking of a big amount of data received from a business, E-Commerce and Retail are at the top in this category. Even the best marketing experts fail to analyze manually and get all possible insights from the average amount of client information from a business in this industry. By joining forces with ML-powered solutions, marketers can be much more effective and predict the possible moves of the customers in future marketing campaigns.

Making customer experience better

Getting insights on customer behavior will help you to plan the customer’s journey and elevate the experience of each client. Obtaining knowledge on what exact adjustments you need to do in your business, will save you money on wrong and ineffective actions you could do otherwise.

Predictive maintenance

Moving on to another industry, Manufacturing, the power of Machine Learning can help here too. The biggest companies still follow very expensive corrective and preventive maintenance strategies, which often are not very effective. The ML technology can help to find not obvious patterns in factory and machinery data to provide valuable insights. This is called predictive maintenance and can significantly lower unexpected expenses.

Detecting and preventing fraud

The banking and finance industries, along with any companies working with financial transactions, can benefit from improving cybersecurity. Machine Learning technology allows building very effective and sophisticated financial security solutions.

Financial analysis

Machine Learning is already very popular in algorithmic trading, loan underwriting, and portfolio management, having some remarkable success stories.

Suggesting products

The top marketplace platforms in the E-Commerce industry have already implemented ML in some way to suggest the products for the buyer. The algorithms analyze the purchase history and draw conclusions of what the particular buyer may be interested in.

Examples of Machine Learning usage in businesses

There are plenty of businesses that already benefited from ML innovations and published their case studies. Let’s focus on two leading companies in different industries that made a significant impact using the technology we talk about in the article.

A Predictive Analytics solution for the Manufacturing industry

One of the most prominent case studies was published by a model-based industrial AI innovator that helped a global manufacturing biotechnology company that has over 2000 products and nearly $500 million in turnover.

The manufacturer faced a problem of a 3.6% downtime decrease in a single quarter. The breakdown was somewhere in the production line, which consists of a mixing tank, distillator, reactor, pump, and centrifuge. The worst thing was that the problem caused an abnormally high level of viscosity in the product, which led to blockages between reactor and centrifuge. This resulted in increased time for cleaning, more waste, and frequent stoppages on the production line. According to all parameters, everything should be working fine and the investigation did not uncover any problems.

The manufacturer invested in an Industry 4.0 solution that combined Machine Learning and Data Analytics to find the root of this situation. ML developers managed to identify the correlation of events that triggered the chain of events that caused the problem. It was possible due to the analysis of historical and real-time data from the production line. In an on to two-hour period, a certain combination of parameters between the mixing tank, distillator, and the reactor caused the problem. With insights from ML algorithms, the operational team can now easily prevent this.

As a result, the rate of downtime events dropped by almost 85%, with an over 70% decrease in downtime costs. On-time delivery is now nearly at 100% rate and production line capacity increased by 5.1%.

Making entertainment personalized

As of the first quarter of 2021, Netflix has 207.64 million paid subscribers all over the world, which makes it the most successful streaming company in the world. Netflix has its own challenges, like shortening the attention span of the viewers, more competitors on the market, and high production costs of the original content. The company leveraged Machine Learning to keep viewers as engaged as possible. Machine Learning algorithms are working to provide unique automated suggestions to each viewer. This recommendation service is already acknowledged as the best on the market and helped Netflix earn more on the original content.

This was definitely the right move for the company. Netflix used the complex supervised (regression and classification) and unsupervised (compression and clustering) ML algorithms to precisely determine the habits of viewers. The first couple of years since the implementation of ML showed great results, lowering the churn rate to 9% a year. Today almost 80% of their watched content is driven by an ML-based recommendation engine, saving the company well over US$1 billion annually from lowered churn rates.

Top Machine Learning tips for business executives

Learn more

You don’t need to be a technical guru and the jack of all trades, but a solid high-level understanding of the topic will definitely help. Start with AI For Everyone by Coursera and you will get a great introduction to Artificial Intelligence.

Start from the basics

You don’t need to climb Everest in your first attempt to introduce Machine Learning. A relatively simple solution like Churn Prediction may be a great place to start implementing the innovation into your processes.

Leverage the historical data

In fact, any supervised Machine Learning solution could be a great starting point. With the good amount and high quality of historical data, you can try demand prediction or fraud detection solutions as well.

Big Data is not a must

You don’t always need the high volume of data, the quality sometimes is much more important for the cutting-edge predictive algorithm.

Don’t ignore the Cloud

Corporate giants like Amazon or Facebook used systems in the cloud as an essential part of their internal company infrastructure. Keep in mind that cloud platforms with API-based systems are cheaper and more reusable.

Don’t waste your time

Start your ML journey as soon as possible, especially if you work in an industry where this innovation is still not popular. You can take the lead on the market simply by having a technological edge.

Don't waste your time

How to develop a Machine Learning project?

It will be a good idea to start with partnering up with experienced professionals that will help you with problem framing. Knowing your business challenges and opportunities you can move on to hiring the development team. Building an in-house team will give you the maximal control and opportunities to communicate with each team member personally. However, with Machine Learning it may be difficult to find local experts or the price tag for their services may be too high for your budget. In this case, you can consider outsourcing or outstaffing.

Among the top regions for outsourcing are Eastern Europe, India, and China. Eastern Europe has over a hundred software developers with impressive Machine Learning expertise. Before you partner up with an ML developer, make sure to get a clear understanding of pricing for the services and all possible engagement models. Having access to the development team and learning about their communication tools and practices will also be a plus.

Summary on Machine Learning for executives

Forbes predicts that the Global Machine Learning market will reach an astonishing $20.83B in 2024, growing at a CAGR of 44.06% compared to 2017. Despite the notable success stories, we are still on the verge of the technological revolution.

No matter the size, almost any business can get an advantage out of Artificial Intelligence or Machine learning solutions. The chances are, your competitors are already considering or implementing a solution of this kind into their business processes. You can be a visionary with a groundbreaking startup, the owner of a marketplace, or the CEO of an international corporation—there is a way to turn Machine Learning into an advantage for your company. Yes, it could take a couple of years and a few iterations for your project to meet the goals and exceed them. But if you are ready to invest in the long-term project and get your benefits when it will bloom, Machine Learning is waiting for you!

 

We at Intelliarts AI love to help companies to solve the challenges with data strategy design and implementation, so if you have any questions related to ML pipelines in particular or other areas of Data Science — feel free to reach out.


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