What is Big Data?
If you’re reading this, you’re probably already aware that Big Data is quite the buzzword these days, and has been receiving a great deal of interest and media attention. But the term itself is somewhat vague—the two words together don’t quite explain the concept.
Put simply, Big Data is large-scale information and data generated by business activities, together with sources such as social media and mobile. Typical big data includes information from sources such as retail point-of-sale terminals, bank ATMs, Facebook posts, and YouTube videos. Because of its scale and complexity, companies looking to effectively capture, process, store, search, share and analyze this data using sophisticated software. Big Data analysis can reveal hidden correlations between two data points and can uncover trends or other insights that can be used to market products and services to customers, anticipate demand, or improve performance.
[Image Source: Pexels]
A simple example, hypothetically: If a retailer were to analyze shopping baskets of its customers, it might realize that buyers of a travel-sized shampoo bottle tend to spend a lot more than the average customer on pet food. This pattern, which is otherwise almost impossible to guess, could lead the way for a strategy to offer relevant coupons to buyers of the shampoo bottle. These coupons could be for pet accessories, toys, etc.
Trends and patterns like these are almost impossible to find using traditional means of data analysis unless you’re looking for them, and that’s where technology comes in.
Why You Should Care
Generating new insights through harnessing the ever-expanding plethora of data being created by increased digitization is widely quoted as being the key to competitive advantage. The American research and advisory firm, Gartner predicts that “Big Data will deliver transformational benefits… and… will enable enterprises adopting this technology to outperform competitors by 20% in every available financial metric.” And not just business, widespread adoption of Big Data analytics would change everything from healthcare to education, to government policy-making.
Here’s a quick video from the World Economic Forum that will help you understand this better:
Where Big Data Can Help
Behavior changes, particularly those which are related to new technology, always take a lot of time and effort. However, once a business is able to adopt the Big Data style of working, they would easily discover its wide applications.
A strong Big Data analysis team can help businesses raise the bar in:
– Competitive advantage
– New revenue opportunities
– Increased profitability
– Optimized demand and supply chain/predictive manufacturing
– Improved product development, innovation, and quality
– Enhanced customer service
– Operational efficiencies/process optimization
Imagine running a business in which marketing and promotion are 100% tailored to individual consumers by having a complete picture of their movements, interests, and behavior gathered from smartphones and social media updates.
Imagine a world in which analytics on machine sensor and log data enables preventative measures so that production lines and IT systems never break down.
Imagine if data streamed in real-time from sources such as Twitter, Facebook live video, and Snapchat are analyzed to assess and maximize the impact of every marketing campaign.
Okay, There’s Got to be a Flipside
Working with Big Data isn’t exactly a plug-and-play system. Remember Gartner from a few paragraphs ago? Here’s what they recommend: The focus for discipline in this space is the question, “What value can we generate from this data, and is it more than what it costs us to accumulate, administer, and apply it?” The key is to move from insight discovery to implementation and institutionalization in a timely way. This requires agility and speed, and it is important to identify all the relevant sources of data needed to generate insight.
The key to success is in understanding how to quickly and cost-effectively acquire, process, and analyze the appropriate data sources to find the signal amongst the noise. (For all you know, those mini-shampoo-buying pet owners are too small and unpredictable a demographic to spend any marketing energy or money on.) This can include the discovery of patterns and relationships as yet unknown, or even using and developing algorithms for predicting future events. Timeliness is key–insight must be available at the right time for the need it is supporting.
[Image Source: Pixabay]
And of course, the Big Question of privacy: We hear of data breaches every now and then. Target’s high-profile credit card hacking happened over three years ago, yet the loss to the company and its customers, monetary and otherwise, will probably never be fully recovered.
Data systems will continue to become more secure, but what about the privacy of people-centric data? This remains a huge concern because there are moral gray areas to cross when you use information about people’s personal lives, even for their own benefits.
So will robots eat up data analytics jobs too?
Today, the market for Big Data technologies is fragmented, confusing, and fast-moving. Investments of millions need to be made in chasing results that not enough people fully comprehend. So of course, sophisticated software will be crucial in Big Data Analytics, but nothing can fully replace human insight and creativity. In fact, human interpretation of the results thrown up by Big Data analytics software can significantly improve the actions that would emerge from software or human understanding alone.
[Image Source: Pexels]
Here’s a simple situation: You’ve just returned from vacation and let a friend use your computer to research summer getaway destinations, while in another tab, your Facebook account is logged in. Imagine the wastefulness of receiving travel ideas and deals on your news feed for the next several days!
The pedigree and reliability of source data can be hard to determine–especially for sources such as social media–and new approaches to data governance and quality need to be put in place, which by design cannot be fully automated.
As organizations, both businesses and others, navigate the ocean of possibilities with Big Data, they should try to adopt a pragmatic approach based on starting small and simple to demonstrate value and then scaling once proven.