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Machine learning and predictive analytics work better together
Artificial intelligence and machine learning, when combined with predictive analytics, allow companies and organizations to get the most out of their data.
Like many AI technologies, the difference between machine learning and predictive analytics lies in applications and use cases. Machine learning’s ability to learn from previous data sets and stay nimble lends itself to diverse applications like neural networks or image detection, while predictive analytics’ narrow focus is on forecasting specific target variables.
Instead of implementing one type of AI or choosing between the two strategies, companies that want to get the most out of their data should combine the processing power of predictive analytics and machine learning.
At the core of machine learning
Artificial intelligence is the replication of human intelligence by machines. This includes numerous technologies such as robotic process automation (RPA), natural language processing (NLP) and machine learning. These diverse technologies each replicate human abilities but often operate differently in order to accomplish their specific tasks.
Machine learning is a form of AI that allows software applications to become progressively more accurate at prediction without being expressly programmed to do so. The algorithms applied to machine learning programs and software are created to be versatile and allow for developers to make changes via hyperparameter tuning. The machine ‘learns’ by processing large amounts of data and detecting patterns within this set. Machine learning is the foundational basis for advanced technologies like deep learning, neural networks and autonomous vehicle operation.
Machine learning can increase the speed at which data is processed and analyzed and is a clear candidate through which AI and predictive analytics can coalesce. Using machine learning, algorithms can train on even larger data sets and perform deeper analysis on multiple variables with minor changes in deployment.
Machine learning and AI have become enterprise staples, and the debate over value is obsolete in the eyes of Gartner analyst Whit Andrews. In years prior, operationalizing machine learning required a difficult transition for organizations, but the technology has now successful implementation in numerous industries due to the popularity of open source and private software machine learning development.
“Machine learning is easier to use now by far than it was five years ago,” Andrews said. “And it’s also likely to be more familiar to the organization’s business leaders.”
Behind the art of predictive analytics
As a form of advanced analytics, predictive analytics uses new and historical data in order to predict and forecast behaviors and trends.
Software applications of predictive analytics use variables that can be analyzed to predict the future likely behavior, whether for individual consumers, machinery or sales trends. This form of analytics typically requires expertise in statistical methods and is therefore commonly the domain of data scientists, data analysts and statisticians — but also requires major oversight in order to function.
For Gartner analyst Andrew White, the crucial piece of deploying predictive analytics is strong business leadership. In order to see successful implementation, enterprises need to be using predictive analytics and data to constantly try and improve business processes. The decisions and outcomes need to be based on the data analytics, which requires a hands-on data science team.
Because of the smaller training samples used to create a specific model that does not have much capacity for learning, White stressed the importance of quality training data. Predictive models and the data they are using need to be equally fine-tuned; confusing the analytics or the data as the main player is a mistake in White’s eyes.
“The reality is [data and analytical models] are equal,” White said. “You need to have ownership or leadership around prioritizing and governing data as much as you have the same for analytics, because analytics is just the last mile.”
Applications of machine learning and predictive analytics
Data-rich enterprises have established successful applications for both machine learning and predictive analytics.
Retailers are one of the most predominant enterprises using predictive analytics tools in order to spot website user trends and hyperpersonalize ads and target emails. Massive amounts of data collected from points of sale, retail apps, social media, in-store sensors and voluntary email lists provide insights on sales forecasting, customer experience management, inventory and supply chain.
Another popular application of predictive analytics is predictive maintenance. Manufacturers use predictive analytics to monitor their equipment and machinery and predict when they need to replace or repair valuable pieces.
Predictive analytics is also popularly deployed in risk management, fraud and security, and healthcare applications across enterprises.
Machine learning, on the other hand, has a wider variety of applications, from customer relationship management to self-driving cars. These algorithms are in human resource information systems to identify candidates, within software sold by business intelligence and analytics vendors, as well as in customer relationship management systems.
In businesses, the most popular machine learning applications include chatbots, recommendation engines, market research and image recognition.
Intertwining techniques for a cutting edge enterprise
Enterprise trend applications are where predictive analytics and AI can converge. Maintaining best data practices as well as focusing on combining the powers of machine learning and predictive analytics is the only way for organizations to keep themselves at the cutting edge of predictive forecasting.
Machine learning algorithms can produce more accurate predictions, create cleaner data and empower predictive analytics to work faster and provide more insight with less oversight. Having a strong predictive analysis model and clean data fuels the machine learning application. While a combination does not necessarily provide more applications, it does mean that the application can be trusted more. Splitting hairs between the two shows that these terms are actually hierarchical and that when combined, they complete one another to strengthen the enterprise.