With the advent of the Internet of Things (IoT) and Web technologies evolution, there is vast growth in data. Subsequently, the traditional technologies like Relational Database Management System (RDBMS) have their limitations to handle big data. New technologies are developed to manage them and to derive useful insights.

With the digitization of most of the processes, the emergence of different social network platforms, deployment of sensors, adoption of smart devices, wearable devices and explosion in the usage of Internet, generates vast amounts of data continuously. Moreover, the Internet changed the way businesses now operate, the functioning of the government, education and lifestyle of people worldwide. Today, such trends are in a transformative stage, where the rate of data generation is very high, and the type of data generated surpasses the capability of existing data storage techniques. Also, such data carry massive amounts of information than ever before due to the emergence and adoption of the Internet.

Data Generation Technologies

Making sense of the significant data generated, it helps the organization in informed decision-making and provide a competitive advantage. Earlier, organizations used transaction-processing systems that inherently used Relational Data Base Management Systems (RDBMS) and simple data analysis techniques like Structured Query Language (SQL) for their day-to-day operations that helped them in their decision making and planning. Nonetheless, due to the increase in the size of data especially the unstructured form of data. For instance, customer reviews of their Facebook pages or tweets – it’s almost impossible to process these data with the existing storage techniques and plain queries.

Today’s current and emerging focus of big data analytics is to explore traditional techniques such as rule-based systems, pattern mining, decision trees and other data mining techniques to develop business rules even on the large data sets efficiently. Moreover, the business’s either developing algorithms that use distributed data storage, in-memory computation or using cluster computing for parallel computation. Earlier these processes were carried out using grid computing, which was overtaken by cloud computing in recent days.

No technology is full proof; however, given the benefits and drawbacks of grid computing or cloud computing, it might prove useful to process a massive amount of data processed for big data analysis or live stream data analysis.

Big Data Analytics

Though the industry defines big data in various forms, there is no specific definition. Few retailers identify what big data does while very few have focused on what it is. The meaning of the big data on the basis of 3Vs is relative – Volume, Velocity, Variety. Volume refers to the magnitude of the data that is being generated and collected. In other words, it is increasing at a faster rate from terabytes to petabytes. Velocity refers to the rate of generation of data. Experts describe traditional data analytics as data generated on periodic updates- daily, weekly or monthly. With the increasing rate of data generation, retailers collect and analyze data in real- or near real-time to make informed decisions.

Variety refers to different types of data that the analytics generate and capture. Moreover, they extend beyond structured data and fall under the categories of semi-structured and unstructured data.

The concept of big data analytics has left no sector untouched. Few sectors like Telecommunication, Retail and Finance, are early adopters of big data analytics, followed by other industries.

Technologies in Supply Chain Management

With consumers progressively redefine the supply chain delivery model, the next few years is going to be a test for retailers as they seek to differentiate themselves from their competitors through their innovative approach to sourcing, replenishment and distribution. The data floodgates are now open for businesses of all sizes and descriptions. Moreover, they bring a myriad of opportunities for timely analysis in pursuit of competitive advantage. Although the focus is currently slanted towards customer behaviour, data is available at multiple points in the supply chain. Moreover, it comes in many forms—traditional (structured), ad hoc (unstructured), real-time, and IoT–or M2M-generated, to name but a few.

Companies that begin to implement big data analytics successfully can reap rich rewards from cost-saving efficiencies and revenue-generating innovations. Big Data will enable businesses to achieve a digital transformation, allowing them to maintain competitiveness in the face of any disruptive startups—which are data-driven almost by definition—that spring up in their markets.

However, useful business insights don’t automatically flow from a torrent of various information. Retailers must identify, organize, and analyze actionable data, and the results implemented across relevant parts of the business. As a result, it requires planning, budgeting and the right tools and expertise.

Upcoming Data Trends

For big data industry-watchers, the most emerging prominent sector for 2017 was AI, machine learning, automation & cognitive systems. Machine learning in 2018 is now becoming the biggest disruptor and analytic applications embedding machine learning are starting to become the norm. Increasing levels of automation are almost an inevitable requirement if organizations are to avoid drowning in data—or, in other words, Artificial intelligence will grow in importance as data volume begins to increase.

In the coming years, we will soon shift from the mindset to talking much about Big Data; instead, the focus will move on the outcomes of big data which retailers must deliver in a self-service approach.


Written By: Rajiv Prasad