The world of business is now more technology-driven than ever. Artificial intelligence and machine learning are definitely among the technologies that are making a paradigm shift. Plenty of use cases have already proved the business value of these, from better decision-making to increased performance to optimizing business processes.
If you are an executive on the path of digital transformation, this guide on machine learning will help you understand the main reasons behind the need to turn your organization into a data-driven company. Prepare to read how machine learning can convert your raw data into an additional revenue stream and improve the bottom line of your business.
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 anyone can unlock these advantages.
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 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.
This area is especially dependent on accurate information and awareness of the current situation in the market. You can make a fortune by knowing what exactly your customers want and predicting their next possible preferences and actions.
Machine Learning will help you manage and get an advantage out of data
A significant challenge in data collection for organizations is an abundance of available information that a business can effectively 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 individual generates a substantial amount of data, as well as even the smallest organizations. As a business leader, you can draw conclusions through manual analysis or seek assistance from a team of analysts. But to detect the smallest nuances and get the value out of information, you might consider using an automated solution. Machine learning technology and data extraction using AI are some 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, 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 quality sets of data, time for data analysis, and coding. You can take machine learning as a long-term investment that will benefit your business greatly in the future when the full potential of the technology is uncovered. The most effective ways to use machine learning in manufacturing and businesses from other industries include:
Analyzing customer behavior
Speaking of a large 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 into customer behavior will help you plan the customer’s journey and elevate each client’s experience. Obtaining knowledge on what exact adjustments you need to make in your business, will save you money on wrong and ineffective actions you could do otherwise.
In fields like insurance underwriting, machine learning helps crunch through mountains of data to unlock hidden patterns and make smarter risk assessments, leading to more informed business decisions and ultimately, a more competitive edge.
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. ML technology can help to find hidden patterns in factory and machinery data to provide valuable insights. This is called predictive maintenance and can significantly lower unexpected expenses. By adopting predictive maintenance for manufacturing, businesses can proactively address equipment issues before they lead to costly downtime.
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 businesses to build effective and sophisticated financial security solutions.
Machine learning is already very popular in algorithmic trading, loan underwriting, and portfolio management, having some remarkable success stories.
The top marketplace platforms in the e-commerce industry have already implemented ML in some way to suggest products for the buyer. The algorithms analyze the purchase history and draw conclusions about 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 company invested in an production 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 one 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 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 tips of Machine Learning for business executives
You don’t have 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 a 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.
How to develop a Machine Learning project?
If you want to build machine learning pipelines for and do great things with machine learning, start by 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 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.
Artificial intelligence and machine learning solutions, in particular, can be implemented in a business of any size and across various domains, from finance to manufacturing. So, the chances are, your competitors already started to implement ML-powered solutions 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. If you are ready to invest in a long-term project, you’ll reap the powerful benefits of machine learning, such as optimization of the entire production process by harnessing historical data or improvement of customer experience by predicting customer behaviors.
The best way to implement machine learning into your business is to partner with an experienced software engineering company. We at Intelliarts love to help companies solve challenges by building machine learning solutions. If you have any questions related to ML technology or need a consultation — feel free to reach out to us.