Today, big data is perhaps the most critical asset for every organization in the business world. The more a company can exploit big data, the better its stance on analyzing, that aids in developing beneficial business choices.
Big data is being utilized extensively across many industries to forecast future trends, discover trends, and draw new inferences.
Before we go any further, let us first define Big Data. Data was historically handled much more efficiently since there was not much to store or analyze. Big data was born when there was a need to hold data sets in considerably larger amounts.
Big Data is a groundbreaking word that signifies different things to different personal productivity levels. Big Data refers to the processing and analysis of exceedingly vast volumes of data.
Like every technical innovation, big data has both advantages and downsides. Let’s take a closer look at them.
Table of Contents
Pros of Big Data
To start, we have highlighted six competitive advantages of Big Data for better business decisions that may be worth your consideration:
1. Increase in efficiency and productivity
Big data technologies enable analysts to examine more data at a quicker rate. The cloud-based analytics increases their output, producing a wave that elevates all boats.
This benefit also provides individuals with additional data about themselves, allowing them to identify sectors where they may be more effective in their business strategies.
The analytic solutions can help organizations and governments reduce waste and increase efficiency, resulting in better utilization of scarce resources and a more efficient operation.
Big Data analytics-generated prediction analyses are utilized to find possible savings in sourcing, scheduling, and navigation, which dramatically aids local traffic management and congestion relief.
2. Personalization and customer service
Improving customer experience is among the primary goal of big data analytics initiatives.
Companies today collect vast amounts of data from many sources, such as customer relationship management (CRM) systems, social networking sites, and other client contact channels.
The most well-known implementation of Big Data is its capacity to help personalize products and services that are particular to client desires, such as credit card companies, Electronic health records, and the financial services industry.
It helps organizations create detailed profiles of their potential customers, allowing them to move beyond the one size fits all mentality, making their services and goods more customizable to suit customer requirements.
They learn about users’ interests and preferences by processing this vast data. And with the assistance of big data technology, companies can now design experiences that are more reactive, unique, and precise than ever before.
3. Transparency
Advanced analytics provides massive data sets that will give customers and citizens more data-driven insights about the services and goods they like to utilize.
Most people are unaware of the scope of Big Data, yet it contributes to greater transparency in industry and government while also eradicating long-standing information asymmetries.
4. Reduce costs for organizations
Enterprises use big data analytics tools for cost reduction of their expenditure ratio. Correct data analysis offers you more control over your money by enabling you to identify any unneeded spending.
Every facet (the highs and lows) of a business depends upon the actual demand of the public, and if businesses can predict that demand, production can be managed. It may reduce the expense of storing raw materials and completed goods.
When leadership teams begin the Big Data implementation procedures, cost savings may not be one of their top priorities. Still, it undoubtedly becomes a welcome byproduct once everything begins to function as it should.
5. Fraud detection
Big Data has the power to halt suspicious transactions, such as those involving banking services. People need to be aware not to reveal personal details since fraudsters are becoming more sophisticated.
Artificial Intelligence and machine learning can identify abnormalities or transaction patterns that don’t fit an account’s typical pattern of activity.
This capability enables credit card firms, banks, credit unions, and numerous other retailers to detect stolen account information, identifying documents, or product access to stop losses.
From the standpoint of financial services, this benefit is so significant that detection frequently occurs before the client is even aware that something is incorrect.
6. Greater agility and speed
Decision-making is facilitated, and significant time is saved by new software’s ability to evaluate and comprehend data sets quickly.
New data may be automatically created in bulk utilizing current data and statistics. Faster speeds may also aid long-term business stability.
Organizations can now analyze extensive data in real-time using stream analytics, which helps them become more adaptable regarding internal procedures, product design, and time to market.
Cons of Big Data
Despite the benefits of big data, significant obstacles make its application difficult or dangerous. The most significant drawbacks are listed below.
1. Privacy and security concerns
Real-time data collection, processing, and evaluation offer the target audience incredible advantages.
However, protecting it from security risks should be a priority because 1 in 4 firms are susceptible to data breaches.
Therefore, it is essential to identify potential security threats from internal and external sources before starting to amass additional data.
Big data’s potential to make firms a weaker target for cybersecurity risks is perhaps its biggest drawback. Massive security breaches have happened to even prominent multinational companies.
Businesses are attempting to invest more in procedures, protocols, and technology to preserve the vast majority of data by adopting the General data protection regulation (GDPR).
2. Lack of technical expertise
Real-time big data analytics may be viewed as a shiny new asset by certain firms, who may want to use it right now.
A business may lose the chance to get essential insights if it faces staffing issues (people or tools) to manage data effectively.
Additionally, it can put them in danger legally. Therefore, improperly managing or misrepresenting the acquisition and use of data may result in severe fines that harm the business.
Big Data specialists and data scientists are among the highest-paid and most sought-after professionals in the IT industry because working with Big Data requires a high level of technical skill.
Costs of handling Big Data might quickly grow by training current employees or recruiting professionals.
3. Challenging to visualize and organize data
The information gathered may be organized or presented as random data in cloud-based infrastructures.
More differences in the relational dataset might make it harder to comprehend the outcomes and come up with solutions.
When predicting future events or examining current situations, the system may overlook many users if the information is incomplete or poorly organized.
Big Data analysis processes aim to transform the data as quickly as possible into a format that can be used.
Although that effort improves the information’s readability, it might be challenging to handle when a sizable volume of it is received.
While huge data sets may be fascinating, very little is often helpful. To give you an idea, one could retrieve a few paragraphs of relevant material out of a hundred pages of information.
4. Data quality
The value of the analytical insights a business derives entirely depends on the caliber of the data it gathers.
Before concerned authorities can use any of the information for analytics, analysts and data engineers must guarantee its validity.
The significance of each datastore will then need to be determined, and it will need to be adequately formatted for assessment. These necessary actions might considerably slow down the reporting process.
Making conclusions based on inaccurate data can have unanticipated and detrimental effects on enterprises.
We refer to quality data that is missing, in many forms, or contains duplicates as low-quality data.
The information and public records collected should be factual and meaningful for big data to be valuable.
5. Compliance issues
Legacy systems are the logic of business intelligence that conserves strategic decisions of the entire company. The strategic moves can sometimes face compliance with government legislation.
A corporation must adhere to all applicable government regulations and industry standards to store, manage, administer, and analyze big data involving sensitive or private information.
As a result, managing data per government requirements, transmission, and archiving will become increasingly challenging as Big Data quantities grow.
6. Discrimination
Programming errors or biased evaluation criteria might negatively affect people, making it difficult for them to get insurance and credit, which is inappropriate.
Summary of Big data pro and cons
| Pros |
|---|
| Increase in efficiency and productivity |
| Personalization and customer service |
| Transparency |
| Reduce costs for organizations |
| Fraud detection |
| Greater agility and speed |
| Cons |
|---|
| Privacy and security concerns |
| Lack of technical expertise |
| Challenging to visualize and organize data |
| Data quality |
| Compliance issues |
| Reduce costs for organizations |
Although it has several drawbacks, the potential benefits far exceed them. It is indisputable that data drives practically everything nowadays, and organizations have just begun to scrape the surface of the opportunities.
While complexity may increase in the future, we may expect increasingly sophisticated Big Data operations to overcome obstacles.
(Last Updated on September 5, 2022)
