With the advent of technology and the arrival of modern communications systems, computer science professionals worldwide realized big data size and value. The world has been dealing with big data for a long time now, and we have come up with several ways to manage and employ it to benefit our industries, schools, governments, and defense systems.
As big data evolves and unravels more technology secrets, it might help users achieve ambitious targets. But do you know there are certain disadvantages of big data along with the pros? This article will talk about the challenges faced in using, storing, processing, and retrieving big data. Big Data certification course will support you in learning big data skills from the greatest mentors to help you build a career in big data.
Top 10 Disadvantages of Big Data
1. Need for Skilled Personnel
We see data in different forms; it can be categorized into structured, semi-structured, and unstructured data. Only those equipped to deal with big data can help organizations resolve issues, make data-backed decisions, and use it to generate crucial business information that facilitates scaling.
However, in the absence of skilled personnel, organizations might be left floundering, unable to make sense of the huge amounts of data generated every day. Moreover, one has to consider the cost of losing potential returns when data lies unused. Due to this, big data experts and scientists are in great demand everywhere.
2. Privacy and Security Concerns
The chances of data in large volumes being vulnerable to security threats, data breaches, and cyberattacks are quite high. Unless it is safeguarded via modern tech means, data security is a very real threat. But the tools and methodologies to secure data are fairly expensive, leaving small organizations and individuals at risk. To ensure safety, we need the right infrastructure, encryption, network authentication protocols, etc. Gigantic data centers might be needed in some cases.
Privacy is another grave issue, especially on social media where extremely sensitive personal data is shared and stored. Another key problem noted by professionals is the lack of ethical online behavior since it is tough to ensure compliance with cyber ethics. Also, IoT devices make 24/7 access easy but pose some level of danger to data without 2-step verification and end-to-end encryption. Some of the worst cases seen include financial fraud during online payment processing or bank transfers.
3. Unreliable Data Quality
While we need data to deliver top-notch customer service, update the stakeholders of a project, liaise formally with government offices, or teach children basic math, low quality can hamper such experiences. Unreliable data might lead to business losses, wrong decisions, incorrect analysis, inadequate operations performance, ill-advised investment plans, and many other frustrating outcomes.
Take patient management systems as an example. If your data is incomplete, incorrect, or outdated, the results will be less than optimum. This is one of the disadvantages of big data in healthcare.
4. Complexity
Complexity is governed not just by data size but also by variety, type, speed, processing challenges, and retrieval methods. The more complex your data, the higher the chances of data processing inefficiency. Complexities in data gathering and handling require advanced data management systems, which can be a problem for many organizations. Additionally, to deliver value through big data, verifying data relevance to specific business scenarios is important.
Not many are qualified to differentiate between complex and fast-growing datasets that affect results and regular data that needs no special handling. Also, complexities may result in tech support-related hassles. For example, social media sites receive scores of photographs and status updates every second. If a social media platform crashes even for a day, it feels almost intolerable to users.
5. Cybersecurity Risks
Cybersecurity risks come in many forms. It not only refers to losses due to data theft, hacking, security breaches, malware, phishing, social engineering attacks, and other technical vulnerabilities but also risks of tarnished brand image and reputation.
To combat these risks, an organization must have a competent, fully-equipped incident response team. With big data, cybersecurity risks aggravate as the threat level rises dramatically. Dealing with such risks requires resources in the form of funds and manpower, in addition to infrastructure.
Cybersecurity threats are rampant in every industry. For instance, when one considers e-learning and online tutoring platforms that generate data in enormous quantities, it is hard to ignore the disadvantages of big data in education.
6. Legal and Regulatory Issues
Legal and regulatory issues in big data might persist despite many measures companies undertake to safeguard users and insulate themselves from potential lawsuits. Data governance, i.e., the internal policies designed to manage data storage and access, plays a key role in controlling such risks.
Data ownership and protection is a responsibility that falls on the shoulders of the entity providing services. Hence, legal risks might come from both internal and external sources. For example, if employees leak crucial business information, they may endanger the financial well-being of the company and its customers. Similarly, hackers taking advantage of a weak system can expose companies to lawsuits and cost them millions.
A huge amount of data is also held by government offices (direct and third-party) in the form of taxation records, credit scores, personal identification numbers, etc. Hence, they must employ the right people and technology to avoid data loss, as losing data may expose them to legal problems.
Another possible scenario is when companies begin a new project and need to work with big data. They must think about database licensing and copyright protection, which involves heavy legal fees and registration charges.
7. Hardware Needs
Both big data processing and analytics need specific hardware to function properly. For effective management, it is important to have computers with good technical specifications and configurations, backup drives, video conferencing equipment, servers, network systems, etc.
Also, software applications are just as important—data processing software, cloud-based services, applications for deriving predictions, decision-making, and risk mitigation, and analytical tools for social media management, statistical computation, etc., are useful tools in every sector.
8. Costs
Every activity related to big data brings significant costs. These include hardware, software, risk management applications, security controls, remote information access controls, etc. Organizations with large amounts of data must develop risk mitigation plans to preempt possible expenditures.
Moreover, legal and compliance fees for using big data and its technologies also add significantly to the costs. As a result, companies must consider these disadvantages of big data in accounting for costs.
9. Difficulty Integrating Legacy Systems
Digital transformation is visible in every field today. Integrating legacy systems with big data applications or any form of data integration with legacy systems may be a lengthy, time-consuming affair. In addition, technical documentation explaining the features of the old system may not be available even after years of use.
Another crucial factor to reckon with is the existing IT architectural framework. Is it suitable or does it need modifications? During integration, measures to prevent cybersecurity issues should be implemented beforehand. This will ensure no new vulnerabilities or threats are introduced in the existing or transformed computing environment. To avoid issues during integration, it is advisable to prepare a blueprint for digital change management.
10. Rapid Change
Changes in the business environment necessitate changes in big data applications and analytical tools. This may force organizations to incur huge costs when economic policies, international trade, or business conditions change at short intervals. Integrating systems to accommodate and reflect such changes frequently requires time, money, and skilled personnel.
Companies using cloud-based services might find the bills piling speedily. Staying relevant in fast-changing business settings and acclimatizing to changes can present significant challenges without a solid IT management plan.
Conclusion
The drawbacks of big data can only harm businesses if the leadership is unprepared to tackle them or does not anticipate any trouble at all. Setting technology priorities based on business requirements and long-term goals can help enterprises dodge many of the issues discussed in this article and save millions. Technology upgrades and upskilling can go a long way in protecting companies’ finances, products, services, and reputation.
Maintaining a healthy skepticism might be more beneficial than looking at only the downside of big data and its outright negation. Go for the KnowledgeHut Big Data certification course and study practical examples of how corporations manage big data and big data analytics.
