- As enterprise data volumes continue to grow exponentially, conventional approaches for managing large datasets swiftly become ineffective.
- Maintaining high-quality data across various sources and ensuring accuracy, consistency, and completeness can be challenging.
- Protecting sensitive information from unauthorised access, data breaches, and cyber threats is a critical challenge.
OUR TAKE
Addressing the challenges on data managements is a huge challenge for organisations and enterprises, not only about the security and privacy, but also about the smooth operation of the whole system.
–Miurio Huang, BTW reporter
As enterprise data volumes continue to grow exponentially, conventional approaches for managing large datasets swiftly become ineffective. Organisations encounter difficulties in consolidating, preserving, and extracting insights from their extensive data reservoirs.
1. Data quality assurance
Maintaining high-quality data across various sources and ensuring accuracy, consistency, and completeness can be challenging. Data quality issues can arise from human error, system limitations, or data integration complexities.
Inaccurate data can lead to flawed analyses, misguided decisions, and eroded trust in the data. Achieving and maintaining accuracy requires stringent validation processes and regular quality checks.
And complete data contains all the necessary information without any gaps or missing elements. Incomplete data can impair the effectiveness of analyses and reporting, leading to incomplete insights and flawed conclusions. Ensuring data completeness involves thorough data collection and validation procedures.
Another quality of data includes the relevance, timeliness and consistency. Relevant data aligns with the specific requirements and objectives of the intended use case. Timely data reflects the currency and relevance of information in relation to the intended use. Consistent data exhibits uniformity in format, structure, and definitions across different datasets and sources.
The challenge of data quality is a critical concern in data management, encompassing various aspects that can significantly impact the usability and reliability of data.
Also read: Understanding the impact of data leakage
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2. Data security and privacy
Protecting sensitive information from unauthorised access, data breaches, and cyber threats is a critical challenge. With the escalation of cyberattacks and data breaches, compliance with data privacy regulations, such as GDPR, HIPAA, and CCPA, can adds complexity to ensuring data security and privacy. As businesses increasingly embrace digital frameworks, robust security protocols are imperative to uphold data privacy and integrity. Businesses should invest in tools for data collection and categorisation to segregate data subject to regulatory mandates. They should establish rigorous policies and procedures governing data management, encompassing aspects such as retention, quality, and access. This approach entails categorising data based on its sensitivity and implementing tailored security measures corresponding to its classification.
Regular risk assessments are also essential to pinpoint potential threats to data security and privacy, enabling the implementation of preemptive measures to mitigate these risks.
3. Data governance and compliance
Organisations must enact effective data governance practices to uphold data integrity and accessibility, ensuring reliability, smooth data flow, and protection against misuse. However, in implementing data governance, organisations must navigate challenges such as a shortage of skilled leadership in data governance, resource constraints, data quality issues, and a lack of control over enterprise data.
The relationship between data governance and compliance is intertwined. Failures in data governance practices and policies can result in breaches of regulatory compliance. Regulatory authorities have the authority to levy fines and penalties based on the severity and nature of the breach, as well as local laws and regulations. These penalties can amount to $20 million or 4% of the previous year’s annual gross revenue, whichever is higher. Furthermore, businesses may face lawsuits from affected parties in cases of data misuse or breaches, potentially disrupting operational workflows for extended periods under heightened regulatory scrutiny.
To tackle challenges related to data governance and compliance, organisations must establish clear policies and procedures, assign roles and responsibilities, provide training to employees, and implement suitable technical and organisational controls. Regular monitoring and auditing of data practices are essential to ensure ongoing compliance and drive continuous improvement.
4. Data integration complexity
Integrating data from disparate sources, formats, and systems while preserving data integrity and ensuring interoperability is a complex and resource-intensive task. How to manage data integration projects effectively is a key challenge for organisations. Integrating modern data necessitates the situation of copping with current data architectures, which are inherently complex, revamping these architectures requires a significant investment of time and resources.
Failure to update legacy systems to align with contemporary business requirements can lead to challenges in integrating data silos, resulting in the sharing of inadequate or inaccurate information across various departments. To support ongoing innovation, data architectures must be adaptable and flexible to meet modern demands, enabling seamless and continuous data analysis.
