What is Big Data?

Big data can be defined as a large collection of data. In a quest to discover new avenues of growth and opportunity, businesses and corporations are becoming increasingly reliant on data analytics pertaining to big data. However, the main characteristic of data is complexity, not volume. These large datasets are continuously collected and, ultimately, analyzed. A variety of talented, tech-savvy professionals are in-demand to properly analyze, manage, and manipulate these massive sets of data.


big data

How is Big Data Used?

There are many ways in which big data can be used. For example, this type of data can be used to measure the performance of a company with data dispersed across several different systems. Aggregating the data from all systems allows the company to measure their overall performance, improve decision-making, and discover new opportunities. The insights provided by these collections of data are incredibly valuable and can heavily impact a company’s future decisions pertaining to customers, operations, marketing, advertising, etc.

Cybersecurity and Big Data

These large collections of data are also useful to cybersecurity initiatives. Intelligence analysis is used to create more efficient and effective security measures. The massive data stores often collect data in real-time. Real-time data can help cybersecurity and law enforcement gain instant insight into any problems or security issues that arise. Analysis of these data stores can improve both intelligence systems and cybersecurity tools.

How Does Dark Data Become Big Data?

Acquiring the appropriate staff and infrastructure needed to comprehensively analyze big data is often costly. Data that remains unanalyzed is called dark data. The cost associated with collecting, managing, and analyzing very large and complex datasets is often hefty, hence why much of these datasets are stored away and remain unused. Companies are often fully-aware of how valuable their dark data may be and are always actively looking for new ways to analyze the data efficiently and without heavy costs.

Challenges Surrounding Big Data

The main challenges surrounding massive datasets are a lack of methods available to analyze it and a lack of professionals that know how to analyze it. Data science is a lucrative and fast-growing industry. Many companies are actively developing data visualization tools and seeking out trained data professionals to harness the power of their massive data collections. This is also an excellent area to invest your time in, if you are involved or interested in the tech industry, since many businesses and organizations are looking for and funding developers and software providers that are willing to participate in the creation of new business intelligence tools.

The Future of Big Data

As technological advancements continue at a rapid pace what can be defined as big data grows larger and larger. These collections of data are growing so large some companies lack the infrastructure to handle comprehensive analysis of the data. This is the main cause of the increase in dark data. The ability to turn this unused data into usable insights is a significant competitive advantage. Some companies even consider their giant data collections as their most valuable asset. The future of these massive datasets is expansive. Companies will continue to look for new and better ways to tap into their data, while also collecting and discovering emerging types of new data.

What do you think the future of big data holds?