The evolution of big data

When you take the term ‘big data’, the concept has gone through a slew of transformations to arrive at what it is called today. Still people do connect it with ‘data science’ and ‘data mining’. So, what is big data? How is it related to data science and data mining?

To begin with, it is not a new concept that many take it to be. The first usage of the word ‘data mining’ can be traced to nearly two decades back, and it was defined as “the practice of examining large databases to create new information.”

Then in 2001, as it is widely reported, Computer Science Professor William S. Cleveland combined data mining and computer science and coined the term data science. So, data science became the practice of using data mining with computers.

With the advent of the internet and the birth of social media platforms, suddenly the digital world was left with tons and tons of unanalyzed data, until someone realized that there is a lot to be exploited from this humongous amount of data, which eventually came to be known as the big data.

Today, companies have come to understand the full significance of big data and are using intuitive methods to cash in on this technology. In what way companies utilize big data – ethical or unethical, legal or illegal – is a different subject. However, aligning our thoughts to the topic of this discussion, our focus is how big data is impacting today’s businesses.


There are three things that every startup needs to know before they take plunge into the market – their industry, their customers, and their competitors, and big data helps them understanding these three areas.   

Knowing the industry

There are several sources of publicly available data to serve this purpose, but where the problem lies is in analyzing this data. So, you could create a Business Intelligence (BI) programs, or manually analyze it. But, both ways can cost you dearly.

In order to tackle this challenge many companies are employing big data and Machine Learning (ML) algorithms, which, once trained, provides on-demand insights into a particular industry’s trend and situation using serverless computing services such as MS Azure and AWS Lambda. Another reason why these services are widely being used is because of their significant price drop in the last few years.

Knowing their competitors

Big data offers tons of information about competitors’ trial and error efforts. Their strengths, their weaknesses, their end customers’ feedback and discussions on various forms and social platforms, all this information can turn out to be highly valuable for startups in identifying their areas of improvement, service gaps, and above all, help them in avoiding mistakes that their competitors did, or still do.

Knowing their customers

Customers are good, loyal customers are assets. The art of building customer loyalty is being rewritten. Traditional methods such as loyalty bonuses, discount coupons etc. are not as effective as they used to be. So, companies are employing big data to first understand the psychological aspects of their end customers, like their buying routine, product preferences, gender specific or age-specific needs and so on, and then send out targeted ads or customized offering to address their specific needs.

Mobile app development

It is a well-known fact that there are several million apps available in the app stores, but, out of those millions only a handful of them manage to attain financial success and good user retention rate. Several reasons could be attributed to the failure of these apps, leaving the owners of the apps to mull over what went wrong. Today, with big data in the fold, mobile app developers are expressing hope that their puzzle could be solved.

Big data helps understand target audience better

Big data customer analytics help in tracking analyzed insights into crucial areas such as user behavior, their navigation pattern, what they expect during onboarding, their areas on interest, which engagement methods work well with which groups of people, what kind of user experience they look for, and more.

If developers and designer could gain the ability to know the likes and dislikes of their end users, it becomes all the more easy to craft apps which are specific to their needs.

Improving app performance using big data analytics tools

Mobile app data analytics tools banking on big data can work something like a problem detecting tool for mobile apps. You can use it to measure mobile app traffic, can identify which page ranks well with users and which page is facing a glitch. And above all, big data analytics tools acts as a round-the-clock monitor to target weak areas of the app, and help developers to reinforce the apps with necessary actions to improve their performance.

Encouraging user engagement

Push notifications are one of the widely used and effective (only if used tactically) methods to improve user engagement. However, there is a problem with this approach. Since it is an used and abused method, users tend to ignore them. Using big data you can figure out at what time of the day users prefer to check notifications, how frequently they check notifications, and what kind of notifications they are interested in, and so on. This data helps app creator to customize or personalize notifications to engage more effectively with their users.


Data driven marketing helps in spotting patterns, trends and behaviors

Making strategic decisions is key for any marketing, but you need accurate data to make those decisions. If your competitors are not using big data, and if you are set to try it out, then it means you’ve got a better shot at knowing which marketing channels to focus on. Big data allows you to spot trends, patterns, and behavior that your competitors had missed.

As it happens, having big data is not enough. You should know how to collect it, analyze it, display it, and how to meaningfully and morally use it to your advantage.

Tools like Segment helps you find insights on sales, analytics, advertising, and CRM. When you couple technologies like Machine Learning it even tells you what to do with those insights.

Big data use cases

The Weather Channel app

This one shows us how, by using big data, marketers can reach out to their target users with the offering at the right time. By analyzing the behavior pattern of its mobile (and digital) users, coupled with climate data, it gives marketers a chance to send out targeted advertisements.


This multinational retail giant uses text analysis, synonym mining and machine learning to leverage big data to improve the accuracy of their search site. Accordingly to Walmart, by adding these technologies with big data they were able to increase their conversion rate by 10-15%. Considering the market value scale of Walmart, this percentage amounts to million of dollars.

Big data is growing

The revenue forecast of big data shows that there has been a significant and continuous growth for over a decade now, and it is expected to grow exponentially in the coming years. In 2018, the forecast has projected that big data’s global market revenue would grow to $42 billion US dollars.

Big Data Growth

Source: Statista

What’s ahead?

We see a flux of big data applications in businesses across industries. And several other technologies like Machine Learning, Artificial Intelligence, Semantic search are coming into the fold to help leverage big data to the maximum. Now that big data is gaining traction, early adopters are the ones who are expected to gain the most. Any company that’s aspiring to streamline its growth and become future-ready, big data would one of the important areas they need to focus on.

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