What is Big Data? Definition – How it works – Uses

In a dynamic, global economy, organizations have begun to more heavily rely on insights from their customers, internal processes, and business operations to uncover new growth opportunities. In the process of discovering and determining these insights, large complex sets of data are generated that then must be managed, analyzed, and manipulated by skilled professionals. The compilation of this large collection of data is collectively known as big data.

What is Big Data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the five “V’s.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

The five V’s that define Big Data

Just because a data set is big, it isn’t necessarily big data. To qualify as such, data must possess at least the following five characteristics:

• Volume: While volume is by no means the only component that makes big data “big,” it is certainly a primary feature. To fully manage and utilize big data, advanced algorithms, and AI-driven analytics are required. But before any of that can happen, there needs to be a secure and reliable means of storing, organizing, and retrieving the many terabytes of data that are held by large companies.
• Velocity: In the past, any data that was generated had to later be entered into a traditional database system – often manually – before it could be analyzed or retrieved. Today, big data technology allows databases to process, analyze, and configure data while it is being generated – sometimes within milliseconds. For businesses, that means real-time data can be used to capture financial opportunities, respond to customer needs, thwart fraud, and address any other activity where speed is critical.
• Variety: Data sets that are comprised solely of structured data are not necessarily big data, regardless of how voluminous they are. Big data is typically comprised of combinations of structured, unstructured, and semi-structured data. Traditional databases and data management solutions lack the flexibility and scope to manage the complex, disparate data sets that make up big data.
• Veracity: While modern database technology makes it possible for companies to amass and make sense of staggering amounts and types of big data, it’s only valuable if it is accurate, relevant, and timely. For traditional databases that were populated only with structured data, syntactical errors, and typos were the usual culprits when it came to data accuracy. With unstructured data, there is a whole new set of veracity challenges. Human bias, social noise, and data provenance issues can all have an impact on the quality of data.
• Value: Without question, the results that come from big data analysis are often fascinating and unexpected. But for businesses, big data analytics must deliver insights that can help businesses become more competitive and resilient – and better serve their customers. Modern big data technologies open up the capacity for collecting and retrieving data that can provide measurable benefits to both bottom lines and operational resilience.

How does it work?

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

• Integrate – Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale. During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.
• Manage – Big data requires storage. Your storage solution can be in the cloud, on-premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current computing requirements and enables you to spin up resources as needed.
• Analyze – Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

Some benefits

Organizations that use and manage large data volumes correctly can reap many benefits, such as the following:

• Enhanced decision-making. An organization can glean important insights, risks, patterns, or trends from big data. Large data sets are meant to be comprehensive and encompass as much information as the organization needs to make better decisions. Big data insights let business leaders quickly make data-driven decisions that impact their organizations.
• Better customer and market insights. Big data that covers market trends and consumer habits gives an organization the important insights it needs to meet the demands of its intended audiences. Product development decisions, in particular, benefit from this type of insight.
• Cost savings. Big data can be used to pinpoint ways businesses can enhance operational efficiency. For example, analysis of big data on a company’s energy use can help it be more efficient.
• Positive social impact. Big data can be used to identify solvable problems, such as improving healthcare or tackling poverty in a certain area.

The uses of Big Data

Data analysts look at the relationship between different types of data, such as demographic data and purchase history, to determine whether a correlation exists.

Such assessments may be done in-house or externally by a third party that focuses on processing big data into digestible formats. Businesses often use the assessment of big data by such experts to turn it into actionable information.

Nearly every department in a company can utilize findings from data analysis, from human resources to production to marketing and sales.

The goals of big data can be to increase the speed at which products get to market, to reduce the amount of time and resources required to gain market adoption, to target the right audiences, and to keep customers coming back for more.

Big Data is only going to get bigger

With the growth of devices and transactions that generate increasingly complex data streams, effectively using that data is rapidly becoming a significant competitive advantage for many companies. In fact, some companies consider data to be one of their most valuable assets. Therefore, big data should only get bigger as organizations look for more and better ways to tap into existing data and gather new and emerging types of data to make critical decisions, answering questions that were previously considered beyond reach.