Machine Learning (ML) can help to extract meaningful insights from raw data to resolve complex business problems. Data analytics and machine learning algorithms can play this magic to the best. Machine learning algorithms can learn iteratively from big chunks of structured and unstructured data and derive insights with the help of a computer system.
In terms of business administration, machine learning will help to enhance the scalability and flexibility of businesses and can also help to enhance daily business operations. Artificial Intelligence with machine learning algorithms now has gained in popularity among business analysts. Many important factors like easy data availability, growing data volume, faster and cheaper computational skills, and cost-effective data storage contribute to the boom of machine learning.
So, modern organizations can now benefit from easily understanding how they can effectively use machine learning to resolve their everyday operational problems.
Real-time use cases of machine learning
There are plenty of options when it comes to machine learning algorithms for business administration in general. We can see that many of the technology giants like Microsoft, Amazon, Google, etc., are now coming up with their own machine learning applications. The reassuring fact is that machine learning can also be used by even small businesses and startups to derive insights from market data and plan their products and services in a more effective manner. Further, we will discuss some of the common areas where machine learning can help businesses.
1. Predictive maintenance of machinery
Manufacturing companies across the globe can now follow their corrective and preventive maintenance practices with the help of machine learning algorithms. Usually, it may be quite expensive and inefficient while they are doing it manually. With the advancement in machine learning, these companies will be able to make the algorithms to discover better insights about their machinery. This approach is called predictive maintenance, which will help tackle any challenges associated with unforeseen machinery failures and can also eliminate any unnecessary expenses. Machine learning architecture also can effectively use historical data and collaborate it with visualization tools and analytical methodologies to create a feedback loop in predictive maintenance.
2. CLV or customer lifetime value prediction
Customer lifetime value is one of the major challenges which businesses, especially online businesses, and marketers face. With the help of machine learning, companies can now access huge volumes of data from which they can derive some meaningful insights. Data mining and machine learning will help them predict the behavior of the prospective customers, market trends, purchasing patterns of people exactly, and analyzing these will help the marketers send the best possible offers to the right customers at the right time.
In any case, machine learning requires strong database assistance to function properly and accurately. For enterprise database management for machine learning and AI applications, you can consult with a RemoteDBA.com experts who can guide you through the process and offer the best quality support.
3. Automated data entry
Another major challenge in the case of data management is the need for manual data entry. It is discovered that machine learning programs can be able to do take up this process in a better manner. Machine learning-related predictive modeling techniques will help to avoid errors during manual data entry. With ML, the employees and administrators can save a lot of their time for manual data entry and reinvest it into something more productive.
4. Spam detection
Machine learning can also help in detecting spam, which is now used effectively for quite some time. Email providers were previously using some role-based techniques and other methodologies to filter out spam. However, the ML-based spam filters are creating some rules now which use the help of neural networks to detect spam and phishing messages.
5. Product recommendation
Machine learning methodology of unsupervised learning can be used to develop various product-based recommendations. Many e-Commerce providers are now using machine learning algorithms effectively to bring up product recommendations. Machine learning can use the customer behavior historical data to match it with the product inventory and identify the hidden patterns about market trends and customer preferences at various times. First of all, these products can be suggested to the customers based on their history, motivating them to purchase the same.
6. Image recognition applications
Image recognition is a modern technology that is also called computer vision. This is a machine learning subsidiary area, which can produce some symbolic information from the given images and be supported with high dimensional data. This can also involve machine learning, data mining pattern recognition, and some kind of database knowledge discovery. Machine learning in image recognition can be very important in effective business administration, which companies use in various industries such as automobiles, healthcare, social media, etc.
7. Financial analysis
There are chunks of quantitative and historical data with which machine learning will also be able to use for financial analysis. Machine learning can be used in portfolio management—loan underwriting, algorithmic trading, etc. However, there are many future applications of machine learning in finance, including many reports and conversational data to ensure customer service, security administration, and do sentimental analysis for stock trading, etc.
8. Healthcare and medical technologies
Machine Learning can also be used to diagnose ailments, which now helps Healthcare organizations improve their patient care. Machine learning algorithms and data analysis can also help reduce the cost of treatment and assist the diagnostic tools for effective analysis and treatment plans. Machine learning is now used in healthcare to make perfect diagnoses, recommendation of medicines, and identify high-risk patients, etc. Predictions and insights can also be drawn by using different patient records and data sets along with the symptomatic presentations.
Along with these common use cases, machine learning can also be used effectively to improve customer satisfaction and in specialties like cyber security, customer support, and many other business intelligence functions.
