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Big Data and Machine Learning

Introduction

Big data and machine learning are the two most recent inventions that have changed the information technology landscape. By definition, the two might look similar but they have a huge difference and play different roles. Put together machine leaning and Big data can do remarkable work to transform businesses.

Differentiating Big Data and Machine Learning

Big Data

Big data is a mix of large and extremely complex datasets that are difficult to process and store using normal processing and data management applications. This complexity includes challenges like storing, curating, sharing, searching, analyzing, transferring, and visualization of data.

Big data is seen to have five components: called the 5 V’s

  • Volume – how it occurs.
  • Variety – various types of data that come in the forms of semi-structured, unstructured, and structured data. 
  • Velocity – this is the magnificence of processing data with high speed and accuracy.
  • Value – the outcome of the extracted data.
  • Veracity – the consistency band quality of data.

Big data is derived from many sources namely: Internet of Things, Third-Party Cloud Storage, Social Media, and Online Web Pages.

For big data to make sense there are two functions at work; Data analytics and data mining. Data mining involves the collection of data while data analytics is about applying the meaning of the whole data to make sense and uncover hidden patterns, predictions, and many other functions that can be used in different areas of application.

Machine Learning

Machine learning is the processing part. It can be defined as the automated processing of data using complex algorithms designed to evolve through learning. Machine learning gets its experience from the data that is fed to and improves as it digests the data.

Big data uses machine learning to improve with the ever-mounting and dynamic data to deliver incredible insight. The purpose of machine learning is to pick up the trends in the data and interpret the patterns it bears which can be further translated into valuable insights to be used in the business environment.

How Machine Learning is Applied in Big Data?

Machine learning facilitates the gathering, assimilation, and analysis of data through its automated tools. Cloud computing enhances its speed to processing data regardless of size or source.

Use Cases for Machine Learning in Big Data

The combination of big data and machine learning is the reason for tremendous growth in sectors like automobiles, telecommunications, healthcare manufacturing, and government arms like judiciary et cetera. Here is how machine learning is applied to big data:

Use CaseDescription
Audience Segmentation & Market ResearchWith the need to make targeted marketing strategies, businesses need to understand their audience and target them with customized content for conversion. Machine learning algorithms help to understand these target groups by studying the market based on the data collected. Using unsupervised and supervised machine learning algorithms businesses will know; patterns and behaviors, details of the target audients, and their taste and preferences. The main industries that use this technique are advertising, media and entertainment, and eCommerce platforms.
Predictive Analytics Prediction in business is very important in planning and strategizing operations. Predictive analytics can help businesses make predictions with laser-sharp forecasts and outcomes. It helps to make suggestions of extra products, predict the best medications in healthcare through calculating probabilities, assess the possibility of fraud, and give warnings et cetera. Big data has allowed machine learning to be more accurate. Predictive analytics has been powered by rich content to allow more reasonable analysis to take place.
Recommendation EnginesRecommender system helps businesses know the customer needs especially in online platforms for movie streaming companies. One of the best big data machine learning inventions is the recommender system. The recommendation engine has incredible capabilities and can further filter content data to extract useful information; this helps the engine learn user preferences and tendencies.
Ad Fraud, eCommerce Fraud Statistics say that up to 30% of activities in the ad tech industry are fraud. Ad fraud is on the rise as big data grows. Machine learning algorithms has immensely reduced fraud by assessing the credibility of the advertisements, acknowledging patterns in big data and blocking irrelevant ad out of the system in advance. They track, watch and block ad fraud activities.
ChatbotsThe best example of the use case of big data and machine learning is the conversational user interfaces popularly known as chatbots. Machine learning algorithms allow chatbots to learn a particular customer’s taste and preference after a couple of interactions. Examples are Amazon’s Alexa, Nokia’s Cortana, and Apple’s Siri.
Cloud NetworksMachine learning models like text classification and GPU accelerated image recognition are examples of algorithms that don’t learn once they are deployed, therefore, they need to be supported and distributed by the content delivery network(CDN) like LiveRamp which is best for migration for Big data to the cloud.
Mixed-Initiative SystemsThe system that makes suggestions about videos you should watch on YouTube tracks your history using big data and machine learning algorithms to make recommendations. Big data and machine learning collaborate in areas of human-computer interactions or mixed-initiative systems which results from humans and machines taking action or rather initiative.
Scaling ToolsBy combining big data and machine learning, more information can be collected, processed, and analyze to enhance business operations. When put together it is a unique chance to scale the entire business environment. Industry players can prepare tools for scaling especially in the finance and communication departments

Conclusion

In the future, we shall witness big data grow faster in size and machine learning in speed and relevance. The above use cases are just a scratch of what big data and machine learning can do combined. They will continue to serve many functions that transform industries in future.

There is no doubt, big data and machine learning is the future. Businesses should take the bold move and embrace these technologies to stay ahead of their competition. Google, Amazon, Facebook and other big cooperates are reaping huge benefits from big data and machine learning. You should also do the same.

Topics in Machine Learning

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