Big Data Challenges and Their Solutions

Big Data Challenges and Their Solutions

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Rashmi Karan
Manager - Content
Updated on Jul 4, 2023 17:16 IST

This blog discusses the prominent big data challenges and provides potential solutions to overcome them. It covers topics such as Big Data governance, maintaining privacy and security requirements, re-identification risks, contractual obligations with third parties, and interpreting current and future regulations.


Big Data has arrived like an avalanche, becoming a transformative element of the business. Big Data represents great opportunities for organizations but also involves significant risks. Businesses must be especially cautious with the risks associated with their identification processes, predictive analysis, and casual data collection. This blog will cover the biggest big data challenges and their potential solutions.

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1. Big Data Governance 

The immense volume, velocity, and variety of data, and data collection from various sources and systems, create complexities in governing Big Data. 

The problem lies in establishing a robust governance framework encompassing policies and guidelines to ensure the ethical and responsible use of data. This involves addressing data quality, integration, privacy, security, and compliance with relevant regulations. 


Establish a robust governance framework with concrete procedures and guidelines to mitigate ethical dilemmas associated with Big Data initiatives. This framework should include –

  • Data governance policy
  • Cross-functional governance team
  • Ethical impact assessments
  • Data anonymization and privacy measures
  • Employee awareness
  • Monitoring and auditing practices
  • Collaboration with regulatory bodies
  • Regular updates to adapt to evolving challenges.

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2. Maintain Original Privacy and Security Requirements Throughout the Information Lifecycle

The collected data can be linked or related to other data sets, which may generate new information or alter the original data in unpredictable ways. This introduces the risk of compromising data privacy and security, especially if the necessary protocols are not diligently upheld.


  • Implement a comprehensive security and privacy framework to safeguard data throughout the entire lifecycle of Big Data processes. 
  • Monitor and maintain security and privacy requirements from data collection to disclosure or destruction. This includes establishing robust security measures, implementing privacy safeguards, conducting regular audits, 
  • Adhere to applicable regulations and standards to protect the integrity and confidentiality of the data.

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3. Re-identification of Risk

Re-identification refers to identifying an individual from anonymized or de-identified data by linking it with other available information. It poses a threat to privacy and can undermine the intended protection measures.

With the vast amount of data generated and combined in Big Data initiatives, there is a potential risk of re-identification through various techniques, such as data linkage, inference, or correlation. 

This risk becomes more significant when different data sets with varying levels of anonymization or aggregation are combined. It makes it possible to piece together information and identify individuals.


  • Prioritize data anonymization in Big Data programs to protect the privacy of individuals and prevent the compromise of sensitive information. 
  • Ensure a proper anonymization process before disclosing data externally or internally. This includes – 
    • Removing or de-identifying personally identifiable information (PII)
    • Employing data encryption techniques
    • Considering the potential risks of combining different data types. 
  • Implement robust anonymization practices to mitigate the harm caused by disclosing sensitive information.

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4. Third Parties—Use and Respect of Contractual Obligations

The challenge of ensuring third parties use and respect contractual obligations in Big Data arises when organizations share data with external entities. Establishing contractual agreements outlining data usage, privacy, security, and compliance terms is crucial, but effectively monitoring and enforcing these obligations can be challenging. 

However, if the third parties do not have adequate data protection systems, they may risk the privacy and security of the information. The challenge is ensuring that third parties adhere to contractual obligations throughout data-sharing.


  • Before sharing data, evaluate third-party organizations’ data confidentiality and security practices to ensure they meet adequate standards.
  • Thoroughly evaluate third parties privacy policies, data handling procedures, and security protocols to ensure they align with your organization’s requirements and regulatory standards.
  • Create formal agreements that clearly define data protection requirements, including confidentiality, security, and restrictions on data usage.
  • Anonymize or de-identify data before sharing it to protect individual privacy. 
  • Remove personally identifiable information (PII) to minimize the risk of compromising sensitive information.
  • Regularly review audits and reports or conduct site visits to ensure adherence to privacy and security requirements.
  • Train employees on the importance of data protection and the risks associated with sharing data with third parties. 
  • Keep updated with data protection laws and regulations to ensure compliance when sharing data with third parties. 
  • Regularly update agreements and practices to align with any changes in regulatory requirements.

Also Read: Top Big Data Interview Questions and Answers

5. Interpret Current Regulations and Anticipate Future Regulations

Governments still do not have specific Big Data regulations; however, some laws regulate the collection, use, and storage of certain types of personal information, such as financial, health, and children’s data. 


  • Understand and comply with current laws that regulate the collection, use, and storage of personal information. Ensure your Big Data practices align with these regulations.
  • Stay updated on the latest trends and discussions regarding Big Data regulations.
  • Participate in relevant industry associations to stay informed about regulatory developments and contribute to shaping future regulations. s.
  • Regularly assess, review and update data collection, use, and storage practices to align with regulation changes or updates.
  • Proactively incorporate privacy and data protection measures into your Big Data initiatives. 
  • Design systems and processes prioritizing privacy and compliance to minimize the risk of non-compliance with future regulations.
  • Obtain professional legal advice on compliance strategies and potential impacts on your Big Data initiatives.

Related Read – Challenges of Big Data Visualization and Their Solutions

Organizations can ensure they leverage their data most effectively by understanding the challenges and potential solutions. We hope we solved your query about the big data challenges.


What are the main challenges organizations face when dealing with Big Data?

Organizations often face common big data challenges such as data privacy, data security, data quality, data integration, compliance with regulations, and managing the sheer volume and complexity of data.

How can organizations address data privacy challenges in Big Data?

Organizations can implement data anonymization techniques, establish robust consent mechanisms, employ privacy-enhancing technologies, and adhere to relevant privacy laws and regulations to address data privacy challenges.

What are the risks associated with Big Data security?

Risks associated with Big Data security include unauthorized access, data breaches, inadequate data protection measures, and the potential for re-identification or unauthorized linking of anonymized data.

How can organizations handle the scale and complexity of Big Data?

Organizations handle the scale and complexity of Big Data by -Implementing scalable infrastructure, leveraging cloud computing and distributed processing technologies, and utilizing advanced analytics tools can help organizations handle the scale and complexity of Big Data.

What are the considerations for ensuring the ethical use of Big Data?

Ethical considerations for ensuring the ethical use of Big Data include obtaining consent, maintaining data privacy and security, avoiding bias and discrimination, ensuring transparency, and adhering to ethical guidelines and industry best practices.

About the Author
Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio