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Machine Learning Vs Predictive Analytics – Know the Difference

The time has passed when organizations had to run through hoops to get relevant data about their market, business, clients, and so on. With the emergence of digitization in the business world, it is no longer a challenge to get and gather information/data; but processing that data into meaningful insights is still a challenge. This is where machine learning and predictive analytics are stepping in. Organizations are utilizing machine learning and predictive analytics to enable more accurate decision making via integrated data-driven approach. By using these technologies, enterprises are able to get new market insights, predict customer behavior, and reduce operating costs by improving the effectiveness and efficiency of the business processes.

With the proliferation of Big Data and IoT adoption, The AI and Machine Learning market is expected to cross USD $40 billion by 2020.

While both Machine Learning and Predictive Analytics help in the manipulation of large volumes of data and helping in drawing meaningful conclusions, it is not accurate to use the terms interchangeably. They are different from each other and have different characteristics. Their usage and end results can be different and subjective. Let’s dive into understanding these two technologies, their advantages, and some words of caution.

Machine Learning

In simple terms, Machine Learning can be defined as a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. With Machine Learning, a computer/program can infer rules inherent in data. It enables programs to evolve and adapt as new data is added. In Machine Learning, the system is provided with data, a learning algorithm, and a task which indicates what kind of result is expected, and then the system works on itself to achieve the best result possible using the data provided to it.

Technically, a Machine Learning system has a wide array of programs generated by learning the consumers’ needs and wants. These programs make suggestions basis their conclusions by detecting customers’ opinions about a business on social media platforms. The output of machine learning systems is high-end predictions that can make decisions in real time with less or no human intervention.

Let’s look at some of the advantages of Machine Learning –

  • While machine learning mainly requires a large amount of data, it proves to be a cost-effective technology because it limits or eliminates human involvement. This technology uses fully automatic methods, simplifies complex data tasks and as a result, offers scalable predictive analytics.
  • Machine Learning offers the ease of assessing large amount of data within no time. It can be extremely challenging for humans to process and comprehensively analyze large data-sets and produce reliable results. Machine learning, being data-driven and systemized in nature, with its ability to make decisions on its own with far less or no reliance on human direction, protrudes as a dark horse for accurate assessments.

Watch out for –

  • Once we have trained our machine learning model and validated it on a relatively smaller dataset, then the same is applied to forecast the hidden data. If, by chance, the data being seen is biased, then it could lead to bad decision making.
  • Data scientists might start having a lot of expectations from Machine Learning systems which the systems may not be able to fulfil. There is still a lot of room for improvement in un-supervised learning. So the system does need a human helping hand (hand-coded data) and necessary inputs.

Predictive Analytics

Predictive Analytics is a form of advanced analytics that uses machine learning algorithms and statistical analysis techniques to analyze current and historical data to make predictions about future trends, behavior, and activity. Predictive analytics helps businesses with the analysis of data which they need to plan for the future – this is based on different current and historical scenarios. By identifying the potential risks and opportunities beforehand, organizations can tweak their operations accordingly, and that makes predictive analytics a cost-effective system for businesses allowing them to use the insights gained with more accuracy.

Advantages of Predictive Analytics –

  • Predictive analytics helps businesses predict every eventuality. Organizations use predictive analytics to forecast the upcoming trends and patterns, consumer behavior and make rational decisions basis their findings. As one would expect, if they are able to successfully forecast certain eventualities, they can boost their business and firm’s revenues.
  • Predictive analytics helps organizations optimize customer intelligence, reduce customer churn, optimize customer loyalty, map customer journeys and tweak the marketing campaigns based on that, and identify new trends and growth opportunities.

Let’s look at some of the advantages of Predictive Analytics

  • In this frequently evolving era, there is no certainty in consumers’ behavior, which means their behavior keep changing and to match to that businesses need to update their models to keep up with the accuracy of the end result.
  • Data is useful only when it is complete, substantial, and accurate. When data is collected from different sources and formats, special efforts need to be taken to ensure data quality. In the absence of that, the predictive analytics will return inaccurate results.

Both Machine Learning and Predictive Analytics are powerful technologies which are helping organizations worldwide. Top enterprises such as Google, Amazon, IBM, etc. are continuing to invest heavily in Machine Learning and Artificial Intelligence and working towards their advanced applications to complex business problems. It will be very interesting to see how these technologies evolve in the near future and how businesses will utilize them optimally.

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