Machine Learning and Its Impact to Software Development

Why is Big Data such a big deal? According to Gartner, Big Data is projected to drive enterprise IT spending to $242 billion. Big Data provides the ability for organizations to leverage their data to reveal key insights that inform smart business decisions. It literally forms the basis for machine learning and predictive analytics. Which is a good thing considering the very real occurrence of networks and enterprises being inundated with data from social, mobile apps, and IoT that produce a 24×7 continuous flow of data.

The total number of ‘things’ connected on the Internet is expected to hit the 200 billion mark by 2020, which supports the fact that the volume of data is doubling every 2 to 3 years, and is expected to reach about 40 trillion gigabytes by 2020. What’s more is that not only the amount, but even the pace of the data flow is accelerating: Facebook users watch the equivalent of 750 years of video every day, over 300 hours of videos are uploaded on YouTube every minute, up from 100 hours every minute in 2013, and each day over 500 million tweets are generated on Twitter. What’s the best way for companies to manage all of this data? Better yet, how do professionals go about organizing this data in ways that will yield insights that help drive business decisions?

Vendors in the analytics space have helped to address the problem by making it easier to access and visualize data. However, most fall short of being able to scale beyond providing key metrics based on data generated from past behavior to a more sophisticated reporting that is capable of producing tangible insights based on predictive models. Data rarely comes in a neatly wrapped package ready for analysis and manipulation. Big data is often synonymous with unstructured data, which includes files like email messages, videos, photos, audio files and many other types of business documents that lack any sort of formal structure. Experts estimate that 80 to 90 percent of the data in any organization is unstructured.3 For this, machine learning, an offshoot of AI, is needed. Machine learning can analyze Big Data sets and it works to interpret the billions of bits and bytes of data in real-time.


Machine learning is an incredibly powerful artificial intelligence tool, which can process petabytes of information and assign order to an otherwise chaotic world of big data. In a nutshell, it’s the modern science of finding patterns and making predictions from data based on multivariate statistics, data mining, pattern recognition, and predictive analytics.1 The value of machine learning is rooted in its ability to create models that guide future actions and to discover patterns missed by the naked eye. Machine learning methods are vastly superior in analyzing potential customer churn across data from multiple sources such as transactional, social media, and CRM sources. This advanced analytics technology means that instead of looking into the past for generating reports, businesses can predict what will happen in the future based on analysis of their existing data.

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This data may reside in historically unstructured systems like emails and calendars or from call centers or legacy voicemail systems. Machine Learning algorithms will not only aggregate and organize this data, it will mine this information to generate insights and predictions, which can then be used by professionals to make informed business decisions. With the help of Machine Learning and AI, outliers are identified more quickly and more effective ways of testing and quality assurance are discovered.


Machine Learning and AI still are in their infancy and impact only a small portion of software engineering on a small subset of projects; however, their popularity is growing in the technology sector and their impact is still significant. At Stanford, one of the largest classes on campus was a graduate level machine-learning course covering both statistical and biological approaches. More than 760 students enrolled.2

Software developers will be able to build better software faster, using AI and Machine Learning technologies such as deep learning and natural language processing. Based on this philosophy, computerized intelligence will change the nature of Development and QA jobs instead of eliminating them – at least for the time being. Going forward, what constitutes a good Developer will be based on how well they can manipulate raw data and mold it into a format that a machine learning algorithm can digest. They will need to know how to monitor the accuracy of a predictive model and understand the inner workings well enough to make changes when necessary. Netflix has over 57 Million users and generates about 30 Billion predictions per day to users on a variety of platforms and devices. It’s important at the outset of a project to ask how often and how quickly models will need to make predictions and weigh the flexibility of these goals against the accuracy of the model.2

Developers are not Data Scientists. Software developers and data scientists might both participate in building a machine learning initiative, but it would be unfair to expect a software developer to be able to fully utilize all the capabilities of machine learning. But basic machine learning literacy is important and achievable.


Machine learning is a powerful tool for the modern business. In fact, some believe that businesses that fail to derive value from machine learning will face huge competitive disadvantages as the field continues to grow and adoption increases around the world.4 Tomorrow’s most successful businesses will excel at organizing people to put data into action with machine learning. It’s time to get literate and demand more from this transformational technology.


Raj Subramanian

Raj Subramanian

Developer Evangelist for

Raj Subramanian is a former developer who moved to testing to focus on his passion. Raj currently works as a Developer Evangelist for, that provides stable self-healing AI based test automation to enterprises such as Netapp, Swisscom,Wix and Autodesk. He also provides mobile training and consulting for different clients. He actively contributes to the testing community by speaking at conferences, writing articles, blogging, making videos on his youtube channel and being directly involved in various testing-related activities. He currently resides in Chicago and can be reached at and on twitter at @epsilon11. He actively blogs on and his website His videos on testing, leadership and productivity can be found on YouTube.