With considerable and ever-growing interest, the world of software development is talking about Machine Learning solutions. There is a good reason for this: Machine Learning has experienced a growth spurt in the industry thanks to the many applications and advantages gained from its capabilities.  Its accelerated growth is due to the fact that the industry just achieved the required computational power that is needed to process Big Data.

Now, businesses in multiple industries want to fully tap into the potential offered by Machine Learning to improve their overall performance, provide a superior customer experience, deliver operational efficiency, and reduce costs.

It’s becoming increasingly important to identify whether the Machine Learning technology and algorithms can achieve all these goals for businesses. Next, we are going to talk about the current state of Machine Learning in business to get a clear picture of where it is and how it can evolve.

Introduction: Current state of Machine Learning in Business

While Machine Learning solutions have been in use by production operations since the early 2000s, it is now more mainstream than ever. Nowadays, Machine Learning has been widely embraced by different industries and businesses, whether they are early adopters of Machine Learning capabilities or still exploring how to leverage Machine Learning.

To understand the current state of Machine Learning in business, executives must first distinguish between Artificial Intelligence, Machine Learning, and Deep Learning, as these terms have become relatively interchangeable in the industry, even though they are separate concepts. You can learn more about this subject in our previous blog post about “What is Machine Learning?”

Machine Learning is not the all-purpose, magical solution for business problems but oftentimes, companies drain Research & Development budgets trying to justify the need for Machine Learning development. For starters, companies should have a clear understanding of the value they hope to gain from Machine Learning and how Big Data plays a key role in its success.

One of Machine Learning challenges is its price and the time involved, which is why it is important to identify whether  Machine Learning is needed in the first place. If there is a clear need for a Machine Learning solutions, companies must then face the challenge of grouping together the right team of Machine Learning consulting professionals and Artificial Intelligence experts, who are relatively scarce in the industry.

To sum up, there are numerous benefits of Machine Learning that are shifting the way the industry is investing in this field, but it is costly, time-consuming, and resources are a constraint.

Now that we understand the key points to take under consideration in the current state of Machine Learning in business, we are going to discuss what Machine Learning can actually do for companies of all shapes and sizes.

Machine Learning use cases for businesses?

To successfully apply Machine Learning for businesses, the first step is to identify what business problem needs to be solved.  Next, businesses must evaluate if the available data is clean enough to be leveraged by Machine Learning tools and techniques. Then, and with the help of experienced data scientists, businesses must build and examine a development plan to calculate the investment and the effects of using a Machine Learning system.

For these steps, companies can choose to outsource or build the system in-house. This team of professionals will be in charge of implementing the Machine Learning algorithm into the business process in order to enjoy the outcomes and improvements of using Machine Learning.

One of Machine Learning problems is that ML by itself isn’t going to magically accomplish all business tasks. Instead, the technology must be leveraged via models, tools, and techniques to obtain insights about a company and then generate an informed strategy.

As defined by Business.com, there are three major types of Machine Learning use cases: supervised, unsupervised, and reinforcement learning.

• Supervised: This is the most common type of Machine Learning, with approximately 90% of Machine Learning development projects using this approach. In essence, this type of Machine Learning uses input data and a target variable that needs to be predicted. For example, the input data can be a person’s gender, age, personal preferences, and so on. And the target variable can be about how likely it is for a person to access a website that is marketed in a Facebook ad. The supervised Machine Learning model analyzes the input data to discover patterns and predict who is likely to take action, based on past decisions.
• Unsupervised: This Machine Learning model deals with input data as well but it does not have a target variable to predict. Instead, it groups input data into collections of data sets that are then worked through via a Machine Learning algorithm to discover patterns. This model is oftentimes used for marketing clustering.
• Reinforcement learning: This Machine Learning model is a more free-form model that data scientists use to specify the rules, the environment, and the outcome. Machine Learning algorithms are used to attempt numerous strategies to learn from past experience and maximize the outcomes.

Once a business selects the type of model it wants to use for a specific problem or challenge, it can dwell on the numerous benefits of Machine Learning:

• Data classification: With Machine Learning, developers can build a model to quickly identify and classify numerous types of data. Given the vastness of data available, this Machine Learning application is particularly helpful to classify massive amounts of data, which would otherwise be virtually impossible for humans to manipulate. 
• Predict: Businesses can count on uncertainties. But it is the way they deal with the uncertain that brings value to a company. With Machine Learning algorithms, users can predict future outcomes within a given degree of probability. Additionally, predictions can also be used in recommendation systems that are based on a client’s past activity. A prominent Machine Learning use case is the Netflix entertainment service, which applies Machine Learning to predict what content a user might be interested in seeing.
• Discover patterns: Actionable insights are one of the most sought-after benefits of Machine Learning in businesses of all shapes and sizes. While it appears to be similar to data classification, it is actually a sophisticated means to unearth groups of clients that may otherwise be overlooked. Thus, companies can personalize services and retain and attract clients. Additionally, pattern discovery also serves the purpose of finding anomalies in massive data sets using a Machine Learning algorithm. Data visualization: Informed decisions are better decisions. With Machine Learning techniques, companies with fluent data streams can visualize data for a clear and defined understanding of what is happening at all times and at all points of a system or process. Additionally, with data visualization, users can quickly spot anomalies and take action swiftly.

Now, let’s take a look at some of the problems that Machine Learning can alleviate.

Types of business problems that ML can help with

Companies face many different types of challenges and problems that can be mitigated by applying Machine Learning effectively. 

• Market analytics: Machine Learning is capable of helping companies identify and pinpoint specific client relationships and predict behavior. This is important because, with these insights, companies can build a more sophisticated and robust client strategy.
• Client loyalty and experience: With the help of Machine Learning algorithms, users are can interact with chatbots, help desk agents, or use digital assistants to obtain personalized customer support. . This increases customer loyalty and retention, as clients are more likely to continue using a product or service if the customer experience and support are enjoyable. For example, with predictive modeling, companies can offer a more personal experience for customers by applying learned data about customer habits and preferences.
• Operational efficiency: With Machine Learning, companies can significantly improve processes. This results in increased performance, cost-effectiveness and an overall time-saving strategy for operational activities.
• Manage risks and detect frauds: By using Machine Learning, companies can easily discover patterns, anomalies, and mitigate risks. Machine Learning models can be used to learn how to detect fraud patterns in real time and identify anomalies in different processes.

Best Practices and benefits of Machine Learning for businesses

Data

Machine Learning thrives not only with lots of data but with the right data. Rich and quality Big Data is essential to provide a Machine Learning ecosystem with the necessary, high-quality information to gain valuable insights.

While companies may have massive amounts of data, it is not always recent, relevant or of actual use. In fields where new data comes in droves on a daily basis, such as the healthcare industry, it is important to weed out the new data from old and unnecessary data. If the data sets are not relevant or tied to current trends in your business, there is no real value in using them in Machine Learning.

Another best practice for data in Machine Learning is to clean your data. Big Data is flowing with unstructured data that is messy and requires lots of effort to clean before it can be leveraged by a Machine Learning process. By cleaning your data, you identify and correct errors to provide an accurate picture and decrease invalid insights.

Talent

Hiring the right professionals can be daunting in a field where resources are scarce or just emerging. From Data Scientists to Machine Learning consulting experts to Chief Analytics Officers, putting the right team of professionals together is critical to the success of a Machine Learning project.

Because there is a real need for these types of professionals in the field, companies must be ready to pay a premium for them. Alternatives to this situation include hiring a 3rd party vendor team or nurturing in-house talents under the leadership of an experienced Data Scientist. These Machine Learning professionals must help businesses bridge the gap between technical aspects and a business vision. Otherwise, data just sits there with untapped potential.

Adapt

What is new today, is old news tomorrow. This is the reality we live in. The world is changing quickly and new technologies emerge every day, so businesses must be ready to adapt, evolve and embrace new models, tools and technologies for Machine Learning project ideas.

While a specific model may work, businesses must be cautious and prepared to iterate on the go and make rapid changes as necessary. Machine Learning solutions require preparation and thorough testing before going live. This includes selecting data, cleaning data, examining live environments and more. With this reality in mind, businesses must understand that solutions are dynamic and will vary depending on their  Machine Learning use cases.

Businesses must expect iterations, adjustments, and fixes to achieve the desired results of using Machine Learning.

How does Machine Learning drive value in businesses?

Machine Learning in business has advanced to the point where Machine Learning tools, techniques and technologies can be delivered on a grand scale, without compromising speed, accuracy, or efficiency.

Real-time insights drive an improved decision-making framework that helps businesses act swiftly and execute business strategies with a higher success rate. Predictive models in Machine Learning help scale support to countless requests per second and process responses accordingly.

The value of Machine Learning in business is quantifiable and is at a stage where it provides critical business advantages. According to a report from Statista, 39% or organizations worldwide rely completely or to a certain degree on automation, Machine Learning, and Artificial Intelligence.

The future is brimming with possibilities for Machine Learning and we are only just realizing the width and breadth of its scope in business.  The full reach of Machine Learning will permeate into virtually every aspect of our lives, which is why businesses are evolving and innovating new solutions to generate value in ways that weren’t dreamed of before.