Unlocking the true power of Big Data with Machine Learning

Machine learning is taking a big leap in Big Data stream. Today, Google predicts that you should leave now to catch a flight and Amazon recommends a book that you should read- are a few of the many machine learning usage instances that we come across in our lives daily.

For many enterprises, Big Data serves as a strategic asset — which reflects the aggregate experience of an organization. Each customer, partner, or supplier response or non-response can provide the enterprise a learning experience altogether.
But gathering and maintaining large collections of data is a task, but extracting useful information from these collections is an even more strenuous task. Big Data not only changes the tools one can use for predictive analytics, but it also changes our entire way of thinking about knowledge extraction and interpretation and adds a new aspect to it simultaneously.

If we look at Big data from its traditional background, it has always been dominated by trial-and-error analysis. Taking this kind of an approach becomes impossible when datasets are large and heterogeneous.

In recent times, companies have focused more on how to store and manage this data. How can they best architect their enterprise technology stack, to gain value from Big Data in terms of Hadoop, streaming real-time data, NoSQL and traditional data warehouses? Hosting data on-premise or on the cloud is another major question which the enterprises face.

These are fair questions to ask, but they don’t get to the core of why Big Data is a big deal. Only with advanced analytics, and specifically machine learning, can companies truly tap into their rich vein of experience and mine it to automatically discover insights and generate predictive models to take advantage of all the data they are capturing.

This advanced analytics technology means that in addition to leveraging historical data for trend reporting, businesses can predict what will happen in the future based on analysis of historical and new data. The value of machine learning is rooted in its ability to create accurate models to guide future actions and to discover patterns that are never seen before. To get the right output and quality a good understanding of what the data represents should be the key pointer.

But what is Machine Learning?

There is a lot of confusion about what machine learning is in the Big Data ecosystem. Machine learning is the science of finding patterns and making predictions from quality representative data leveraging various algorithms. It is different from more traditional methods of prediction in the sense that the machines do not have to be explicitly programmed to include multiple known rules.

Machine learning methods are particularly effective in situations where insights need to be uncovered from data sets that are large, diverse and fast changing — Big Data. Across these types of data, machine learning easily outperforms traditional methods on accuracy, scale, and speed.

Let’s look at the example of a company which is building a new business model – leveraging data from IoT sensors to transmit real-time data about their employees’ physical activities while on the job. The goal is to improve worker productivity and also prevent injuries while on the job. This is a classic use case of Big Data and Machine Learning – real-time streaming data from IoT sensors, IoT platform, Cloud infrastructure, filtering & aggregating IoT data to data that can be used for Insights and applying Machine learning on top of the data to provide insights.

Using a combination of supervised and unsupervised techniques, Machine learning methods are vastly superior in analyzing potential customer churn across data from multiple sources such as transactional, text, social media, and CRM sources.

High-performance machine learning can analyze all Big Data set rather than a sample of it. This scalability not only allows predictive solutions based on sophisticated algorithms to be more accurate, it also drives the importance of software’s speed to interpret the billions of rows and columns in real-time and to analyze live streaming data. This scalability has been made easier and accessible to all, and not only those with deep pockets, through the Cloud-based platforms which provide their own ML libraries.

We live in an era of Big Data that includes data from IoT devices. The market for IoT devices is set to explode – Gartner predicts 21B IoT endpoints by 2020. While earlier paradigm shifts in businesses were powered by steam engines, carbon products, electrical power, semiconductors, computers, and the Internet, we are currently experiencing a boom which is driven by Big Data.

Machine learning is finding its mark in the business world. What was once primarily of academic interest now has practical real-world applications and does not require a lot of infrastructure support to get started.

In the age where big data is creating ripples by mining huge chunk of data, organizations can tap into tremendous opportunities to discover insights that can lead to better and faster business decisions. The organizations that can realize value from their data assets faster through advanced analytics such as machine learning will become winners and others will be left behind. The emphasis is on real-time and highly scalable predictive analytics, using out-of-the-box techniques that simplify some of the typical data scientist tasks. Machine learning techniques can solve applications using a set of out-of-the-box algorithms that differ from more traditional statistical techniques.

 

The article was originally published on ciol and is re-posted here by permission.

 

Arvind Purushothaman

Practice Head and Senior Director – Information Management & Analytics, Virtusa. Arvind has more than 19 years of industry experience, with focus on planning and executing Data Management and Analytics initiatives. He has a comprehensive understanding of the IT industry best practices, technologies, architectures and emerging technologies and his role includes: Designing and overseeing implementation of end-to-end data management initiatives and delivering architectural initiatives that drive revenue and improve efficiency in line with business strategy including technology rationalization in line with emerging technologies. Prior to taking on this role, he was involved in architecting and designing Centers of Excellence (COEs) as well as service delivery functions focused on Information Management encompassing traditional Data Warehousing, Master Data Management and Analytical reporting. Arvind’s previous experience includes stints in organizations such as PwC, Oracle and Sanofi before joining Virtusa. Arvind is a prolific speaker and has represented various industry forums including UNICOM and Gartner BI events. He has also presented a number of webinars on HR Analytics. Arvind graduated from BITS, Pilani, and obtained his MBA from Georgia State University.

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