Amidst the generally rapid technological advancements nowadays, people tend to get confused over the specifics of machine learning and predictive analytics. Although, they are closely related and very much centered on efficient data processing to enhance accurate predictions for increased productivity, the application of these two concepts express many differences. Machine learning and predictive analytics similarities are a great source of the existing confusion between them and they will both be broken down below to highlight some of their major differences. Clearly distinguishing these two revolutionary modern day concepts will go a long way towards clarifying mix-ups and providing answers to the many ongoing debates related to computer software with potentially positive effects on the productivity of specific businesses. So, lets take this opportunity to differentiate between the two using examples, so it clarifies the misnomer.
Machine learning is underlying intelligence behind most artificial intelligence (AI) applications which involve the development of systems or algorithms which have the ability to learn and improve automatically from data experience without relying on explicit programming based on rules or instructions with the changing data patterns. The concept of Machine Learning is focused on designing computer programs and models which can directly access data and utilize the data alone to improve themselves. The algorithms of Machine learning are wide range tools which are capable of carrying out predictions while learning simultaneously from over trillions of observations. Machine learning can actually be considered an extension of modern technique of predictive analytics and the models designed on this concept have the ability to adapt and evolve as new data is introduced since efficient pattern recognition and self-learning are the backbone of the designs. Machine Learning models refresh and adapt themselves based on the changing patterns by themselves, automating the intelligence, enabling appropriate actions.
Business Application Example of Machine Learning
Almost all prominent companies nowadays depend on the use of machine learning algorithms for the better understanding of their clients and potential revenue opportunities. The hundreds of different existing and newly developed machine learning algorithms all target the derivation of high-end predictions which can guide real-time decisions without so much reliance on human intervention.
A COMMON and uncomplicated but successful business application of Machine learning is the real-time employee satisfaction;
Real-time Employee Satisfaction
A simple machine learning algorithm that uses the data of employee satisfaction ratings between 1 and 100 against their salaries as training data is a perfect business application even though most other real life applications are a lot more complex involving trillions of dimensions. Instead of simply plotting a predictive satisfaction curve against salary figures for the various employees as predictive analytics will suggest, the machine learning algorithm automatically assimilates huge random training data upon entry, and the prediction results are affected by any added training data. All this aims at moving towards more real-time accuracy and more helpful predictions.
This machine learning algorithm like all others apply self-learning and automated recalibration in response to pattern changes in the training data making machine learning a lot more reliable for real time predictions than other artificial intelligence concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.
Machine learning can also be implemented in image classification, facial recognition utilizing deep learning and neural network techniques.
The term PREDICTIVE ANALYTICS can be defined as the procedure of condensing huge data volumes into summary information facilitating human comprehension and consumption. Basic descriptive analytic techniques include averages and counts. Descriptive analytics is concerned with taking a backward view at the past and is very much reflected in the progression to Predictive analytics which now attempts to follow up with what is likely to happen in the future. This more recent concept applies more complex techniques of classical statistics like regression, decision trees, etc. to provide credible answers to queries such as ‘’How exactly will my sales be influenced by a 10% increase in my expenditure on advertising?’’. This leads to development of simulations and what-if analysis for the users to learn more about the query.
All predictive analytics applications involve three fundamental components;
- Data: the effectiveness of every predictive model strongly depends on the quality of the historical data which it processes.
- Statistical Modelling: this component includes the various statistical techniques it applies ranging from very basic to complex functions for the derivation of meaning, insight and inference. Regression is the most commonly used statistical technique.
- Assumptions: This consist of the conclusions drawn from the collected and analyzed data which usually assume the future to follow a pattern related to the past.
Business Application Example of Predictive Analytics
Data analysis is crucial for any business on route to success and predictive analytics can be applied in numerous ways to enhance business productivity. For example; marketing campaign optimization, risk assessment, market analysis, fraud detection etc.
Marketing Campaign Optimisation
To guarantee maximum results, efficiency and optimization techniques are being implemented into marketing campaigns. Gone are the days when valuable resources were wasted by businesses in attempts to capture market niches based on instincts alone. Nowadays, many different predictive analytic strategies exist which can be employed to identify, engage and secure suitable markets for their services and products as well.
A clear application of this is the usage of past search data and usage patterns of website visitors by e-commerce websites such as Amazon as a base for product recommendation. Amazon recommends products to visitors based on their specific interests increasing the chances of sales. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail and almost every other business domain.
As important as machine learning and predictive analytics are to modern business success, understanding the relation between the concepts is equally a vital requirement. Machine learning is a predictive analytics branch and despite having similar aims and processes, there are two main differences between them:
- Machine learning works out predictions and recalibrates the models in real-time automatically after design meanwhile Predictive analytics work strictly on cause data instead and need to be refreshed with change in data.
- Unlike Machine learning, Predictive analytics is applied to still rely on human experts to work out and test the associations between the cause and the outcome.
About the Author
Shailendra Kumar is the Chief Evangelist for Analytics and Leonardo in the APJ&GC region. He is responsible for driving innovative ideas and discussions with SAPs clients in the region. With an experience of over 23 years working with Corporates, Software Vendors and Consulting companies to deliver over One Billion Dollars through advanced analytics, Shailendra joined SAP in July 2017. He has established and lead several data science businesses to generate revenue and drive incremental growth by creating multiple Artificial Intelligence solutions across a variety of sectors, including: High Tech, Financial Services, Retail and Public Sector.
Shailendra is a keynote speaker, influencer and a thought-leader in the Artificial Intelligence space and has published multiple articles about advanced analytics, machine learning, IoT, Artificial Intelligence and Blockchain; and recently published an Amazon bestseller “Making Money Out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics.
Prior to joining SAP, Shailendra held senior executive level positions at IBM, Accenture, Woolworths and Coles.