Machine Learning: Why businesses should it for Data Analytics
Innovation in machine learning (ML) offers many benefits to business data analytics. Businesses are able to benefit from ML because the algorithms that are used make accurate, data-informed choices. According to Forbes, by the year 2020, the Internet will have an estimated 1.7 megabytes of newly created data per second for every human in existence.
AI gives businesses accurate data at a faster rate than other programs, so businesses that use ML will succeed more rapidly than other businesses. Those who want to use ML to help their businesses grow should take time to understand the ML process and understand the algorithms that will best help their businesses flourish.
What is machine learning?
ML is made of algorithms that analyse data and automatically produce an analytic model. It enables businesses to gain insights because computers are not programmed to look for data in any specific area. As a result, businesses can discover information that was not known due to ML to gather information from unspecific locations and recognize useful data patterns and inconsistencies.
Information and AI experts use proven algorithms to understand the meaning of the data that is produced from computers. However, the primary goal of ML is to leverage businesses to a point where they no longer need information scientists and machine learning experts.
Machine learning Algorithms
Machine learning has been so successful because it is comprised of self-teaching algorithms. When these algorithms are subjected to a massive amount of data, they are able to learn from the information for accurate predictions and results. Algorithms can be grouped into
- and reinforcement learning.
An example of a machine learning algorithm solution is streaming company’s recommendation tool, which was produced by machine learning predictions using data that was gathered from a user’s watch history. There are a variety of algorithms that are used for different benefits such as fraud detection.
Business Analytics and machine learning
An article in Fast Company reported that a significant number of business owners do not completely rely on the information their analysts provide. The goal of business owners and machine learning is to gain better data without human error. There are also analytic obstacles business owners and executives face such as implementing newly gathered information into routine business processes.
AI for all lines of businesses
The government uses AI with utilities and public safety, but the government’s primary use of ML has been to locate new ways to reduce spending and increase efficiency. In addition, the government also incorporates machine learning into cybersecurity applications that help identify individuals who commit theft or fraud.
Healthcare is an industry that has used machine learning to improve screening, diagnosis, and treatment. Enlitic is a company that uses ML to analyze images so that doctors are able to accurately diagnosis patients faster. Another company, known as Ginger.io, is studying how effective machine learning is used as part of a treatment plan for those who have anxiety and depression.
The oil industry is also taking advantage of AI. BP is planning to use artificial intelligence and machine learning to create data models that show flow rates and machine vibrations with information from the natural environment. The goal of BP is to make business decisions using natural data, such as the height of the ocean’s waves, which will optimize efficiency.
Machine learning has revolutionized the transportation industry. Uber and Google were among the first companies to test a self-driving automobile, and there have been many companies to follow the pursuit of a self-driving automobile ever since. According to Northwestern’s McCormick School of Engineering, machine learning can be used to estimate time delays due to traffic congestion by using images to recognize unusual patterns in traffic.
The Future of ML and Business Data Analytics
According to the Data Science Community, retail managers will have the capability to make fast and accurate choices without the help of executives. With the advancements in ML, business analytics can now go further than a description and prediction business model, it can move toward predictive analytics and help business owners and executives make accurate decisions quickly.