Machine learning in business processes explained simply

Data Science and related technologies such as Artificial Intelligence (AI) and Machine Learning (ML) continue to enjoy great popularity. They enable businesses to automate processes and make it easy to analyze large volumes of raw data for commercial purposes. 

Machine learning algorithms generate models that can recognize data and behavior patterns with little programming effort. Additionally, they enable them to learn iteratively from new data. This constant evolution allows companies that leverage machine learning technology to stay on top of the latest developments and benefit from accurate, high-performance data processing. Let’s dive into the details of the benefits of using machine learning for business use cases now. 

What is machine learning? 

Before we get into the practical applications and advantages of Machine Learning, we first need to understand how this technology works. 

Machine learning is the generation of knowledge from experience by software. Thus, an artificial system is trained to learn from a few examples before applying them to more general domains. Algorithms first build a “static model” based on “training data”, teaching it to recognize specific patterns and regularities. The model is then checked for accuracy against “testing data”. At the end of this process, the system can detect these same patterns in new, unknown data sets. 

In practical terms, machine learning lets software recognize patterns within raw data, allowing it to extract meaningful information suitable for further processing without human intervention. This enables highly efficient, rapid, and scalable data analysis, as well as many applications in process automation.

Practical machine learning use cases within business operations

Now that we understand how machine learning works, let’s take a look at how businesses can use this modern technology in practice. 

Process automation

Automation has become essential in almost all industries to cover repetitive, low-complexity tasks in a resource-saving manner. An important example is data extraction from ID cards, medical certificates, invoices, and other frequently encountered document types. Machine Learning models automatically recognize data fields even on complex documents and extract information as key-value pairs. These are immediately ready for further digital processing and do not require any manual input from employees. 

Taking the idea of automation even further, roles previously filled by employees can be covered by robots. Powered by machine learning algorithms and artificial intelligence, this kind of optimization is becoming a reality in the manufacturing sector. By fully digitizing and automating processes, businesses save costs while improving efficiency and scalability.

Behavioral analytics and predictions

Analyzing user/customer behavior is a core use case of machine learning – especially in retail. Here, a lot of information is collected with every purchase, for instance the product category, the date of purchase, and the number of items per transaction. Thanks to machine learning algorithms, this data can be transformed into predictions about future customer behavior, the popularity of individual products, and emerging trends. 

This improves companies’ ability to manage inventory and orders, to optimize logistics processes based on the predicted demand for specific items, or to develop personalized marketing strategies. In eCommerce, machine learning can additionally investigate browsing habits and support retargeting. 

Data protection measures

Finances, insurance, shopping – almost all aspects of everyday life can be managed digitally today. While this certainly increases convenience, the digitization of personal data also creates new risks. Data protection has thus become a vital issue for businesses, and one in which machine learning can play a key role.

Companies rely on increasingly elaborate security measures such as firewalls, intrusion prevention systems, or strict data storage policies. The implementation of these regulations often requires dedicated teams that monitor the systems, maintain them, and develop them further. 

In the future, these teams could be supported by machine learning. For example, ML algorithms can be applied to monitoring or vulnerability analysis. To do this, models are trained to detect irregularities in regulated processes and display them to the staff. 

Risk assessment can also benefit from ML: Complex algorithms predict the risk of a cyberattack based on data from past attacks. In the same way, machine learning can also help locate security vulnerabilities. 

As you can see, businesses can benefit from Machine Learning technology in numerous ways. More than that, it is increasingly necessary to rely on advanced methods to reliably future-proof processes. Accordingly, Forbes reports that about 76% of companies planned to prioritize ML & AI technologies in their budget planning for 2021.  

Want to learn more about mobile data extraction based on advanced machine learning algorithms – and reap its benefits for your business? Our experts look forward to exchanging ideas with you. Let’s talk.

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