August 1, 2017
Big Data & Analytics

Pros and Cons of Big Data

Pros and Cons of Big Data

It’s easy to understand the appeal of the instantly accessible data that so many organizations have begun to collect and store. However, real-time analysis of that data is the way to truly make it valuable to businesses. And, like anything else, there are both advantages and disadvantages to the big data boom.

Big Data Basics

The term “big data” refers not only to large data sets, but also to the frameworks, techniques, and tools used to analyze it. It can be collected through any data-generating process such as social media, public utility infrastructure, and search engines. Big data may be either semi-structured, structured, or unstructured.

Typically big data is analyzed and collected at specific intervals, but real-time big data analytics collect and analyze data constantly. The purpose of this continuous processing loop is to offer instant insights to users.

Big Data Statistics

Data around the world. Source:

Pros of Real-Time Big Data

There are amazing benefits to real-time big data analytics. First, it allows businesses to detect errors and fraud quickly. This significantly mitigates against losses. Second, it provides major advantages from a competitive standpoint. Real-time analysis allows businesses to develop more effective strategies towards competitors in less time, offering deep insight into consumer trends and sales. In addition, data collected is valuable and offers businesses a chance to improve profits and customer service.

Perhaps the greatest argument in favor of real-time analysis of big data is that it may be used to provide cutting-edge healthcare. Proponents of big data point out that healthcare organizations can use electronic medical records and data from wearables to prevent deadly hospital infections, for example. To these proponents, privacy cannot trump the lives big data might save.

Cons of Real-Time Big Data

As valuable as this kind of big data can be, it also presents serious challenges. First is the logistical issue. Companies hoping to use big data will need to modify their entire approach as data flowing into the company becomes constant rather than periodic: this mandates major strategic changes for many businesses. Next, real-time big data demands the ability to conduct sophisticated analyses; companies who fail to do this correctly open themselves up to implementing entirely incorrect strategies organization-wide. Furthermore, many currently used data tools are not able to handle real-time analysis.

One of the biggest concerns many laypeople and politicians have about real-time analysis of big data is privacy. Civil liberties advocates have attacked the use of big data from license plate scanners and drones, for example. The idea is that authorities should not be able to circumvent constitutional protections against unreasonable searches.

The Future of Big Data

Ongoing development of best practices for real-time analysis of big data should continue to be a priority for businesses and government agencies. Each company will need to carefully assess whether pros of such big data use outweigh the cons for their particular case; the answer to that question will vary widely from business to business. Adoption of best practices and close attention to changes in the way we think about big data, however, will be important to all businesses.

Big firms are increasing their investments in Big Data initiatives and such efforts for data-driven culture open new capabilities and pay back with the measurable results. For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Here’s how Fortune 1000 Executives report on Big Data usage and projects where they see the value.

Big Data Projects in terms of business value

Source: Big Data Executive Survey 2017,

Data is growing faster than ever before and if you’re thinking about converting masses of data into value for your company  –  don’t hesitate to contact Ciklum. Find out how Big Data and Analytics solutions can help your business to increase revenue and growth.

Find out how Ciklum data scientists analyzed 7 million banking transactions and built 3 models to predict client behaviour. Get the Report: “Predictive Analytics In Banking. Use Cases” 

Read also:

Editor’s Note: This post was originally published in September 2015 and has been updated for accuracy and comprehensiveness.