Big data problems are real issues that organizations encounter when they attempt to process large volumes of data. These problems are poor quality data, data safety, storage, and usage. Big data challenges are issues that prevent people from using big data correctly. These challenges make storing, protecting, and managing vast quantities of information difficult.
As companies and organizations use larger and larger amounts of data, they struggle to deal with it all. They are working with poor data, insufficient storage, security issues, and privacy concerns. This article describes all these issues in simple language. It discusses key big data management challenges, including data quality issues, big data security challenges, privacy concerns, scalability issues in big data, and others. Moreover, we also outline the impact of each of these problems on businesses, particularly in India.
Big Data Challenges
A lot of companies have enormous trouble with data quality. That means the data might be incorrect, outdated, or unhelpful. Big Data collects data from multiple sources (applications, websites, sensors, machines) for processing. However, these results only contradict the data if the data is correct . This is termed a big data quality issue.
What are Data Quality Issues?
Data quality issues occur when the data has errors or is lacking. That’s a significant problem in big data because data is available in a large volume and from various sources. So, verifying whether all the data is good isn’t easy. If the data is bad, in whole or in part, the analysis can be wrong. This wastes time and money and drives poor decision-making.
- Data quality problems can fall into several different types:
- Data miss: At times, certain portions of data are absent. So, the full picture is difficult to glean.
- Duplicate Data: The same piece of data can be shown multiple times. This gives wrong results.
- Stale Data: It might have old data, which is not useful anymore.
- Incorrect Data: The data can be wrong because of typing errors or machine glitches.
These challenges cause problems in big data analytics. So, the reports and charts from this data will also be wrong. That means businesses could make terrible decisions based on faulty information.
Why Should We Care About Data Quality?
Smart choices with big data for companies. For instance, they want to find out what their clients like. If the data is incorrect, they will not have the correct answers. Good data makes schools, hospitals, and banks work better. Bad data creates confusion.
Several large organizations in India use big data tools as well. However, they lack enough trained individuals to validate data quality. They have even bigger problems.” They could not tell what data is real or fake in a step towards AI.
How to Solve Data Quality Management Problems?
Here are some ways big data companies can improve data quality issues:
And use software that runs data checks automatically.
- That could involve training low-wage workers to clean and check data.
- Prevent data processing mismatch with one format only
- Keep updating data frequently and cut out the old data.
- The use of these methods assists in solving large data management challenges. It also reduces costs and enhances decision-making.
Big Data Security Issues and Privacy Challenges
One of the major concerns about big data is security and privacy. Companies need to safeguard data from hackers and protect people’s sensitive information. This is referred to as big data security challenges & big data privacy concerns.
Security and Privacy Challenges—What are they?
When data is stored in the cloud or shared online, it is already easy to steal or lose. Hackers can access systems and steal personal information such as names, addresses, passwords, or bank details. This is significant in India, where tons of people now use online banking and shopping tons of people use.
Companies might also collect data without informing those people. This is a privacy problem. They want to know how their data is used. Companies can break privacy laws and lose trust if they are not careful.
Best Practices to Use Big Data Security Solutions:
- Installs no strong password or login system
- Not enough control over who sees the data
- Few good tools exist to detect cyber attacks
- Data leaks when giving away data or transferring data
- The major big data privacy issues are as follows:
- Gathering data without seeing if users will accept.
- The second is using personal data to show online ads without getting permission.
- Things to avoid: Not deleting user data upon request
When the data is large and pulled from multiple sources, these can be difficult to manage.
Why Are These Challenges Significant?
Both health records and bank data can be harmful if stolen. During 2023, multiple reports emerged from various Indian banks that they had suffered cyberattacks costing them crores of rupees. Small startups do get hacked as they don’t use proper safety measures.
People stop sharing their data when they lose trust. That means companies cannot gather data issues and follow the privacy problems. Due to new data protection laws in India, user data safety is necessary.
How do We Address the Security and Privacy Issues?
Companies need to:
- Use robust passwords and two-step login
- Limit who can access the data
- Use encryption to hide data
- Keeping security software up to date
- Notify users before gathering data
- Give users the option to delete or edit their data
Scott has a useful tool. Using these steps, users feel more secure. It also aids companies in complying with the law and preventing hacking.
Big Data Challenges: Scalability and Storage Issues
Another major issue is how to store and manage huge amounts of data. This is known as big data storage problems and scalability issues in big data. Businesses are now required to keep accumulating data. However, storage systems are often slow, crowded, or costly.
Scalability and Storage in Big Data.
The system should be able to grow with more data. If the data amount today is 1GB and 100GB next month, the system should not crash. Most companies run into scalability issues in big data. Their PCs or cloud systems crash or slow down when the data expands.
Big data storage problems occur with insufficient space or too expensive storage. Other data types , such as sound or images, are unstructured and difficult to store in normal databases.
Why Is Such Trouble So Typical?
Big data grows fast. Social media, mobile apps, and shotguns generate data every second. Millions of users produce content in a day in India. Many startups and companies begin with small servers. But when data expands, their systems don’t get improved. This leads to huge integration nightmares with data and slow work.
Plus, many people employ no-cost or low-cost tools that can’t handle high-volume data. If the data exceeds that limit, the entire system crashes. Companies also fail to delete data that no longer needs to be deleted, which can quickly fill up storage.
What is the Solution to Scalability and Storage Issues?
Businesses can address all big data storage and scalability challenges as follows:
Data is reclusive; it likes to hide in its little shackles.
- How to learn Hadoop, Spark, and other big data processing tools
- Periodically r~move aged unnecessary data
- Use a proper method for the data backup
- Choose systems that grow with data
Here’s a bit of a compare/contrast table:
| Problem Type | Cause | Solution |
| Scalability Issues | The sudden growth in data | Use scalable platforms like Hadoop |
| Storage Problems | Low space, unstructured data | Use cloud & backup systems |
| Integration Challenges | Many formats, sources | Use data integration tools |
These measures maintain controlled storage and address deep-rooted big data integration issues. If businesses deal with these early, they start running smoothly as well as economically.
Relevance to ACCA Syllabus
Understanding challenges related to big data is also crucial for the ACCA syllabus Strategic Business Reporting (SBR)/ Advanced Performance Management (APM). These subjects assess the candidate’s ability to analyze and appraise financial and non-financial data. In ACCA Big data skills also assist accountants in interpreting a large data set, which is now prevalent in audit, risk, and decision-making processes.
Big Data Challenges ACCA Questions
Q1: A huge challenge that accountants face when it comes to big data is
A) No accounting knowledge
B) No skills for analyzing graph
C) Data integrity and quality assurance
D) Limited access to journals
Ans : C) Data integrity and quality assurance
Q2: Why data governance in ACCA: the importance of big data?
A) It avoids tax evasion
B) It regulates the generation, availability, and utilization of data
C) It prepares the trial balance
D) It resets year-end balances
Ans: B) It dictates how to design, access, and utilize data
Q3: What role does unstructured data play in the challenge of financial reporting?
A)It diminishes the utilization of financial statements
B) It facilitates consolidation
C) It makes standardization and interpretation difficult
D) It provides more comparability.
Ans:C) It makes standardization and interpretation difficult
Q4: How do big data analytics fit in the audit and assurance of ACCA?
A) It is an external auditor replacement
B)It makes sampling and risk assessment more accurate
C) It eliminates the need for documentation
D)It raises manual testing
Ans: B) It enhances the accuracy of sampling and risk assessment
Q5: What skill do ACCA professionals need most regarding big data tools?
A) Language fluency
B) Spreadsheet styling
C) Interpreting the data and dealing with it in an ethical way
D) Budget printing
Relevance to US CMA Syllabus
In areas like performance management, internal controls, strategic planning, etc., they address big data challenges under the US CMA syllabus. CMA exam testing of how accounting, finance, and other professionals leverage data analytics to inform business decisions. With exposure to large financial and operational data volumes, CMAs are learning to lower the risk and improve decision-making accuracy.
Big Data Challenges US CMA Questions
Q1: The below statement is one of the biggest challenge of big data in strategic analysis?
A) Low spreadsheet usage
b) More data deflected at us, but no support for the decisions
C) Obsolete physical stock
D) Delayed bank statements
Ans: B) Data overload with poor decision support
Q2: What is the function of Data Visualizations in CMA performance reports?
A) It replaces budgeting
It compresses complex big data into compact data.
C) It tracks only cash flow
D) It avoids reconciliation
Ans: B) It reduces the complexity of large big data.
Q3: What is its effect on cost management for businesses?
A) Better variance reporting
B) Overhead properly assigned
C) Misleading or deceptive performance metrics
D) BETTER BREAK-EVEN ANALYSIS
Ans: (C) False performance indicators.
Q4: What role can CMAs play for big data supporting budgets?
A) By providing more guesses
B) Using trend patterns and predictive modeling
C) By printing ledgers faster
D) Failure to do scenario planning
Ans: B) Through data trends and predictive modeling
Q5: What control is really important when facing a lot of financial data?
A) Bank signature checks
B) Manual ledger balancing
C) “Data validation and access control.”
D) Photocopying receipts
Ans: C) Data validation & access control
Relevance to US CPA Syllabus
Big data is associated with AUD, BEC, and regulations in the US CPA exam. CPAs must be aware of data reliability, integrity, and cybersecurity risks. Learning about big data challenges in the CPA context is essential since they impact the quality of audit evidence, fraud detection, and reporting accuracy.
Big Data Challenges US CPA Questions
Q1: What is the audit risk of a big data system?
A) Lower profit margins
B) Unauthorized data changes
C) Time for analysis is reduced
D) Too many footnotes
Ans: B) Data changes unauthorized
Q2: Data is often considered the most important element in a CPA’s data audit; however, metadata is something else that is equally important.
A) It shows the footnote count
B) It records data about data
C) It lists account names
D) It presents only tax rules
Ans: B) It is used to record data about data
Q3: Explain how big data challenges can impact assurance services.
A) They cause the manual entries to multiply
B) They allow for reduced internal control testing
C) They complicate the verification process of the accuracy of data
D) Cash-based accounting is simplified
Ans: C) They complicate verification of data accuracy
Q4: What method can CPAs use to manage big data-related fraud risks?
A) Removing all cloud systems
B) Automated audit assistants
C) Not using computer records
D) Failure to estimate transaction trends
Ans: B) Conducting audit using automated audit tools
Q5: How does ineffective data governance impact financial reports in CPA practice?
A) It improves their readability
B) It facilitates compliance better
C) Inconsistency and non-compliance
D) It saves audit time
Ans: C) It leads to inconsistencies and non-compliance
Relevance to CFA Syllabus
In particular, big data is included primarily in the CFA exam as part of investment analysis, portfolio management, and the ethical implications of these decisions at levels two and three. CFA Candidates are expected to be familiar with how alternative data (that is, data other than traditional financial statements) affects forecasting, risk management, and the efficiency of markets. The big data challenges make sure investment professionals use data responsibly and accurately.
Big Data Challenges CFA Questions
Q1: What is the most significant investment risk when utilizing unstructured data big data?
A) Guaranteed high returns
B) Misunderstandings caused by lack of pattern clarity
C) Lack of market volatility
D) Flawless diversification across the portfolio
Ans: B) Wrongly interpreting patterns because of unclear
Q2: What impact does big data have on asset valuation?
A) it lowers the accuracy of forecasting
B) It makes all ratios irrelevant
C) It offers more insight into your market
D) It is only applicable to physical assets
Ans: C) It enables deeper market insights
Q3: What is one ethical issue about using big data in investment analysis?
A) Use of printed charts
B) Reliance on GDP growth
C) Using private data without authorization
D) Lack of password resets
Ans: C) Private Data Unauthorised Utilization
Q4: Why is data consistency essential?
Essentially, A) It makes legalese easy
B) It avoids double taxation
C) It enhances model accuracy and backtesting
(D) It eliminates dividend payouts
Ans: C) It enhances backtesting and model performance
Q5: How does machine learning contribute to big data in the CFA domain?
A) It drafts legal disclosures
B) It creates index funds
C) It discovers hidden patterns and trends
D) It fills annual reports
Ans: C) It discovers concealed patterns and trends
