Rupal Agarwal explains the process behind data creation and collation, and why Big Data is not a fad but a revolution that will impact every business present and future.
Do we have a choice to not inhale CO2 while breathing? Do we have a choice of not having artificially ripened fruits? Do we have a choice to escape from digitization? Whether you like it or don’t, we have all been potential targets for Big Data and it is completely transforming the way we do business and is impacting most of our lives.
What is Big Data?
Big Data, in simple terms, involves collection of several bronobytes of digital data. The basic idea behind the phrase ‘Big Data’ is that every activity you perform leaves a digital trace/ data. From the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days and the pace is accelerating.
Let us focus on the eternal question harnessing the minds of every retail organization, “Will it help to increase my sales?”
Where does Big Data generate from?
We shall first focus on Activity Data, which involves collection of data from various activities that we perform, right from watching music & videos on YouTube to shopping for a particular product from a particular mall, to searching a specific product online & comparing its prices, to reading various eBooks online and zillions of such activities that we have been performing. To illustrate further, a car dealer has a constant eye on all the users comparing features of several cars on the car information websites. Similarly, a wedding couture would be targeting people who’d be logging on to & hovering over wedding wear / wedding planner websites.
Next is the Conversation Data. As the name suggests, it is derived from the numerous digital conversations initiated & followed by the user. We converse on Facebook, Twitter, LinkedIn, Instagram, Emails, Messages, Skype, Blog & so much more. Facebook has the data of nearly 1.44 million active users, which they have efficiently categorized into age-specific, gender-specific, profession-specific, area-specific, interest-specific & several such groups. For a construction company, who is coming up with a flat scheme close to the IT Park, it can bank on data of users working in IT Companies close to that particular area. For a luxury brand, coming up with a new edition of laptop bags for women, it can target data of all those professional women between the age group of 25 to 40, who have liked “Luxury Brands” pages, and present at that particular place during the launch of the product.
Conversation Data can be in the form of text, images or videos. The netizens of the world share over 1.8 billion photos each day. The data generated through such photos & videos is massive & this ‘Big Data’ is eventually used by companies to share their happy customers’ testimonials, loyalty card benefits & several such customer-centric programs.
Another such data collection source is through Sensors. Your smart phone contains a global positioning sensor to track exactly where you are every second of the day; it includes an accelerometer to track the speed and direction at which you are travelling. We are surrounded by innumerable devices which have in-built sensors and can monitor movements and track our preferences. We now have Smart TVs that are able to collect and process data; we have smart watches, smart fridges, and smart alarms. The Internet connects these devices so that the traffic sensors on the road can send data to your alarm clock which will wake you up earlier than planned because a blocked road means you have to leave earlier to make your 9 am meeting.
The value of ‘Big Data’ depends on the volume, velocity, variety and most importantly the veracity of the data. The accuracy determines the utility of the data.
Big Data can provide a better understanding of the customer needs. Through the churning of several data collection sources, a telecom company devised a variety of mobile plans serving the needs of consumers from various segments – a high data plan for college student vs. a low national and international calling plan for a business person. An FMCG company introduced the marketing campaign for a low fat snack for women during the wedding season. Considering the data analytics that men comprise of more than 40% users of face wash, a cosmetic company introduced a fairness face wash & cream especially targeting the men.
It also understands and optimizes business processes. Retailers are able to optimize their stock based on predictive models generated from the social media data, web search trends and weather forecasts. One such excellent example is the game of Angry Bird, which has penetrated from just being a game to the lives of the users thereby promoting sales of its goodies like bags, bottles, pouches, and T-shirts. Another example of business process optimization would be supply chain or delivery route optimization using data from Global Positioning System (GPS).
Big Data helps us to connect to our customers in the way they like (Social Media/ Text/ Emails), at the right location (when they are close to our store) engaging them with personalized real time offers. For an 18 year old who enters our store, a quick pop-up saying ‘Flat 15% off on your favorite Maybelline Kajal’, will surely bring joy to her face. Real time analytics also help us in determining the customer demand, competitor activities, inventory levels and the best price of the product.
The target of Big Data has always been moving upwards & is directly proportional to the optimum use of technology by the users. The applications of Big Data are endless! Currently we are only seeing the beginning of a transformation into a Big Data economy.
Any business that doesn’t seriously consider the implications of Big Data runs the risk of being left behind. Focus for the ‘E-Era’ must be Big Data, Better Experience and Big Buying.