Much of the world interact in the online space, from talking to your friends on Facebook and signing up for a music streaming service to ordering that book you’d been seeking and stocking up your pantry through an online grocery service. All these transactions—and those of the rest of the world—have added up to the collection of massive amounts of data that may not seem important but can actually be analyzed to give valuable insight to any project, endeavor or particular aspect of life. These massive parcels are called Big Data.

Big Data is defined as large data sets on such a scale that traditional data processing techniques can’t handle them, but value can still be extracted from them. The term is also used for predictive analytics or other methods that are used to determine and extract the data set’s value, so it’s increasingly used to describe the data processing itself.

Think of the 4 Vs in Big Data

Data scientists characterize Big Data with four Vs: volume, velocity, variety and veracity. Volume is the most defining characteristic of Big Data as it deals with huge amounts of data generated in an organization—not just terabytes but up to the upper n-bytes, such as peta- (1,000 TB), exa- (1,000 PB) and zetta- (1,000 EB), depending on the organization’s size.

Velocity is the rate at which the data flows in and out of a system, and people involved with Big Data are concerned with the ability to process this data in real-time or near-real-time. Think of a car that has hundreds of different sensors trying to record your speed, engine temperature, and a myriad factors that affect automotive performance. Data has to be measured, stored, analyzed and sent back as quickly as possible in order to be read by the driver so they can determine the next course of action. Our world is made up of many such systems that record so much information that needs to be processed and reported quickly back to users so that they can be used.

Variety of Big Data denotes that data comes from many different sources, and in different forms. Healthcare systems usually deal not just with basic information such as a patient’s weight, symptoms or medical history, but also video logs, machine readouts, and diagnoses, which make up a combination of quantified and qualitative measurements—multiplied by a million patients over! Healthcare systems need to process many different types of data to make valuable conclusions about their users and affect policy.

Veracity concerns the accuracy or correctness of the information collected. Not everything collected can be trusted, mainly because it’s hard to monitor the relentless gathering of such huge volumes of data. Data scientists working with Big Data must find ways to ensure the integrity of their data sets so that the findings extracted from them can be trusted by leaders of organizations and turned into concrete steps for improvement of service.

Big Data helps infer laws, predict trends for better decisions

Increasingly, a distinction is being made between Business Intelligence, which is the processing of Big Data to predict trends and find better opportunities in business, and the traditional meaning of Big Data.

Traditionally, Big Data, such as in research, academic and scientific fields, involves inductive statistics on massive amounts of data with low information density to infer laws and relationships. For example, a population study might take into account huge volumes of seemingly mundane parameters including number of people in the household, average daily expenditure, and location to infer how proximity to schools affects the economic situation of a community.

Business Intelligence, on the other hand, applies descriptive statistics to data with high information density in order to find trends. Business Intelligence is now a huge industry, with companies forming their own teams of data scientists to process Big Data to determine ways in which they can improve decision making and performance, find new business opportunities, and even improve their cybersecurity.

It’s important to note that Big Data is impersonal and without context—it still requires people with the right skillset to analyze the information and be able to see relationships between factors that previously were not seen as related to each other.

Big Data helps businesses understand consumers better

One example where Business Intelligence is used is in personalization, or in understanding individual customer preferences and adjusting to them. Early efforts at personalization involved using on-site context such as customer profiles, clickstreams, and purchase data to define what products the company could further recommend to a user. With marketers today having access to more data, they can more easily hone in on what customers want in a user experience through e-mail engagement and response, geolocation, social media interactions, in-store behavior, and various other factors.

What you should look for in a big data analytics platform is capability to scale and speed-to-insight. With a flexible and scalable analytics platform, you can quickly ramp up or down based on your analytic workload, and optimize costs. With business users having the capability to create their own visual dashboards themselves (and not wait for IT), you can execute data-driven actions faster.

Personalization increases intimacy between consumer and provider

Why should enterprises be interested in working with Big Data and personalization? It’s because personalization makes transactions feel more intimate, making it more likely to positively impact purchasing experiences. When the store knows what you want and offers it to you, you’re going to feel as if they’ve been at your beck and call all this time, and trust is established.

When you’re online, it’s most likely that you click on links that are more relevant to you. If you collect sneakers and see an advertisement for a sale on the latest kicks, chances are that you’ll click on that instead of an advertisement for something else. Personalization in the form of targeted content (advertisements, product offers and services) is more likely to be converted into sales, and that’s why a company should be listening to the customer—or at least knowing their habits.

“In order for interactions to feel individualized and human, they must be well-informed,” said Sean Madden, Executive Managing Director at Ziba. Big Data allows enterprises to see patterns in the behavior of their consumer base so that they can anticipate needs and accordingly make improvements in the user experience. The goal is to find competitive advantage in the customer’s perception of the company, because that’s where it’s most powerful.

Making Impact — Immediately

In today’s fast-paced digital economy, businesses don’t have the luxury of long project timelines. They want their data teams to deliver value immediately. To help our customers overcome their big data challenges, we have built a unified, end-to-end big data analytics platform from the world’s most successful open source data projects that delivers scale, speed, and simplicity. To find out more, you can check the Amihan Analyze hub.