Organizations no longer need to run through hoops to get relevant data about their market, business, clients, and so on. With the digitization of the business world, obtaining and gather information/data is no longer a challenge.
While data collection is easy today, extracting meaningful insights from the available data remains a stiff task. This where machine learning and predictive analytics can help.
Machine Learning? Predictive Analytics? Not the Same
By encouraging an integrated data-driven approach, machine learning and predictive analytics enable organizations to make more accurate decisions. Businesses use these technologies to gain new market insights, predict customer behaviour, and reduce operating costs by improving the effectiveness and efficiency of the business processes.
Often, the terms ‘machine learning’ and ‘predictive analytics’ are used interchangeably. This can be misleading. While there is a strong relationship between the two, machine learning and predictive analytics are two distinctly different concepts.
They have varying characteristics, which means that their usage and outcomes can be different from each other. Let’s explore the two concepts/technologies to identify the differences, see where they overlap, and find out their uses.
What Are the Overlaps and Key Differences?
Machine learning and predictive analytics—many people confuse one with the other and use the terms interchangeably. Though they are both centered on efficient data processing, machine learning and predictive analytics have many differences. You’ll be able to spot these differences once you have a grasp over both concepts.
Following is a brief explanation of machine learning and predictive analytics:
Machine Learning
A current application of Artificial intelligence (AI), machine learning is based around the idea that we should really just be able to give machines access to data and let them learn for themselves. Machine learning enables computers to get into a mode of self-learning without being explicitly programmed.
When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves. A data analysis method, machine learning automates analytical model building. In simpler words, it allows computers to find insightful information without being programmed into where to look for a particular piece of information. To do this, ML uses algorithms that iteratively learn from data.
The machine learning system comprises of wide array programs that are generated by learning the needs and want of the consumers. These programs detect the opinions of customers about a business on social media and other platforms to make valuable suggestions. Put simply, machine learning systems provide high-end predictions that can enable decision-making in real time with little or no human intervention.
Predictive Analytics
Perhaps the most commonly used analytics type, predictive analytics provides businesses with a picture of future scenario or most likely outcome. An advanced analytics type, predictive analytics uses machine learning algorithms to make predictions about future trends, behaviour, and activity.
Based on current and historical scenario, predictive analytics helps businesses to analyze data to find patterns, which are used to make predictions about future events. There are three fundamental components of all predictive analytics applications. These include:
Data - The quality of the historical data available for processing is a key factor in the effectiveness of every predictive model
Statistical Modeling - To derive meaning, insight, and inference from the data, predictive analytics uses various statistical techniques, with regression being the most commonly used technique.
Assumptions - Predictive analytics draws conclusions from the collected and analyzed data that usually assume that the future will follow a pattern related to the past
The Difference Between Machine Learning and Predictive Analytics and Where They Overlap
Predictive analytics is often mistaken for machine learning by casual users since it is one of the most common enterprise applications of machine learning. While machine learning is an excellent way to form predictions from data, it is much bigger than predictive analytics.
Machine learning comprises of a broad spectrum of business use case that falls outside the domain of predictive analytics. These include, but are not limited to:
- Facial recognition
- Natural language processing
- Managing user-generated content
- Helping search engine perform better
To add to the above, predictive analytics, unlike machine learning, still relies on human experts to work out and test the associations between cause and outcome.
Where Are Predictive Analytics and Machine Learning Used Mainly?
We listed some of the uses of machine learning in the previous section. Following are the most common uses of machine learning defined:
Increasing the Value of User-Generated Content (UGC) - By identifying the best and worst UGC, machine-learning can filter out the bad bubble up the good without needing a real person to tag each piece of content.
Speeding up Product Discovery - some businesses employ machine-learning strategies to give their online customers the benefits of machine learning when they are browsing for products.
Understanding Customer Behavior - machine learning excels at sentiment analysis. In fact, a lot of customer-related decisions today are driven by machine learning.
Organizations use predictive analytics to solve difficult problems and uncover new opportunities. Following are the most common uses of predictive analytics:
- Fraud detection
- Optimizing marketing campaigns
- Improving operations
- Reducing risk
Summary
Despite having similar aims and processes, machine learning and predictive analytics are two entirely different concepts, which is evident above.
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