Technology

How AI Can Help Businesses Conquer Data Governance Challenges

In today’s tech-driven world, Artificial Intelligence (AI) has emerged as a transformative force, revolutionizing various sectors. This article explores the immense potential of AI-driven data storage solutions in addressing the critical issue of data governance challenges faced by businesses.



7 min read

As businesses continue to generate a tremendous amount of data, traditional data storage solutions are struggling to keep up. As a result, more and more resources are being spent on maintaining IT infrastructures, adding to the workload of IT personnel and driving up the total cost of ownership. Against this backdrop, effective data governance has become more important than ever, as it ensures that data is managed effectively and responsibly, while also meeting legal, regulatory, and ethical requirements.

Fortunately, the rise of artificial intelligence (AI) in the IT world has introduced us to a new level of data management. For example, AI in data storage solutions offers a range of benefits over traditional methods, including increased data analysis accuracy, cost-effectiveness, scalability, and improved security and privacy considerations, as well as increased efficiency in data storage and recovery.

In this article, we will focus on one of the most pressing concerns for enterprise data—data governance—and how AI can help businesses overcome the challenges associated with managing their data effectively and responsibly.

What is data governance, and what are its challenges?

To simplify the concept, data governance is about making sure that the data an organization uses is available, secure, and follows certain standards and policies, which can be achieved through meticulous data storage, management, and protection. It’s important because businesses rely on data more than ever to make decisions and improve their operations. Without proper data governance, there can be inconsistencies and errors in how data is stored and used across different systems, which can lead to inaccurate reporting and analysis. It can also create compliance issues with data privacy regulations, which can be costly for businesses.

Data silos are one of the most common and annoying challenges to data governance. It is hard to access and analyze data across the organization when data is stored in isolated systems that are not integrated with one another. This can result in data duplication, inconsistency, and redundancy, as well as wasted resources and missed opportunities. It can also impact decision-making by restricting access to critical information, impeding collaboration, and preventing a complete view of the organization’s data.

Other common challenges include data quality and accuracy, data privacy and security, limited or misallocated resources, and complex regulatory requirements. To address these challenges, organizations need to create clear guidelines for data use and invest in the necessary tools and resources to ensure data is accurate, accessible, and secure at all times. An example of such tools is AiRE IntelligentFiler by STORViX.

data governance

How AI-driven data storage solutions can help overcome data governance challenges

AI-driven data storage solutions refer to storage systems that leverage artificial intelligence (AI) and machine learning (ML) algorithms to optimize data management. These systems can be used to automate data governance processes such as data quality control, security, and compliance.

To explain more in detail, we will take a look at the challenges mentioned above and provide you with examples of how STORViX’s AI-driven data storage solution—AiRE IntelligentFiler—tackles these problems.

Data silos

When data is stored in separate systems or formats, it becomes difficult to analyze and gain insights from the data as a whole. AiRE aids in the breakdown of data silos by providing a central storage location where all company data can be stored and accessed easily. It also ensures that data from various sources is combined and integrated, ensuring that everyone is working with updated data.

AiRE also employs a self-healing mechanism to avoid data loss and maintain data integrity. This means that if there are any mismatches or errors in the data, the system can automatically repair them without the need for human intervention.

By using AiRE, organizations can establish governed self-service access to data, which means that employees can access the data they need without having to wait for IT personnel to provide access. This makes data analysis faster and more efficient.

Data quality and accuracy

To tackle poor data quality and a lack of trust in data, AiRE provides several features to ensure data integrity, including end-to-end data protection and fast recovery from malware attacks. AiRE also uses a self-healing mechanism to repair mismatches in data blocks, ensuring that the data is always fresh and accurate.

As mentioned earlier, AiRE provides a central storage solution that allows for the consolidation of data from disparate sources, which can help improve data quality by reducing the risk of errors and inconsistencies. This pooled storage approach also allows for workload optimization through the use of policies and profiles, ensuring that data is being properly managed and utilized.

Additionally, the incremental snapshot function can also help to improve data quality and trust by allowing for more frequent backups and faster recovery times in case of data corruption or loss. This helps to minimize the impact of data errors and downtime on the organization.

Data privacy and security

AiRE can assist businesses in dealing with data privacy and security issues by preventing various types of data corruption. AiRE ensures that the data is accurate and complete by using a self-healing mechanism that repairs any mismatches in the data blocks and prevents data corruption.

Additionally, AiRE’s AI has predictive abilities that enable it to anticipate potential failures and take proactive measures to prevent them, improving overall system reliability and avoiding costly downtime. Its ability to detect patterns and anomalies in data can be used for predictive analytics, providing organizations with valuable insights that can inform data-driven decisions and improve system performance.

Limited or misallocated resources

To successfully implement a data governance program, it is important to appoint a team or someone with the knowledge of how to handle enterprise data. Accordingly, many organizations struggle to achieve ROI in data governance due to a lack of skilled personnel.

Even though AiRE may not directly address the challenge of finding and training data owners, it can alleviate the hassle that data owners have to face in the data management processes through predictive maintenance capabilities and workload optimization. This means that with AiRE, organizations can better manage their data storage resources and ensure that they are being used efficiently.

AiRE’s workload optimization approach allows for different profiles to be used for different workloads, which means that resources can be allocated based on the specific needs of each workload. This approach prioritizes lower latency and higher throughputs for workloads like virtual machines (VMs), while also providing redundant backups and protection for mission-critical data to ensure system resilience.

Additionally, AiRE’s end-to-end data integrity and compliance with complex regulatory requirements can help mitigate risks associated with data governance. By doing so, organizations can be more time and cost effective, resulting in their sustainable growth.

Complex regulatory requirements

Complicated regulatory requirements can pose a significant challenge for organizations, particularly those in highly regulated industries such as healthcare and finance. AiRE can help with this by offering a unified data platform that provides end-to-end data integrity, data protection, and fast recovery from cyber threats.

AiRE IntelligentFiler and our cloud services provided by our partners are designed to work together seamlessly, providing a comprehensive solution for organizations looking to comply with complex regulatory requirements. This includes features like self-healing mechanisms that can repair any mismatches in the data blocks, which ensures end-to-end data integrity.

AiRE also offers advanced data protection features that can prevent data loss due to incomplete writes. These features help ensure that data is protected and available when it is needed.

In addition, AiRE’s fast recovery from cyber threats can help organizations quickly restore their data in the event of a cyberattack. This is critical for organizations that must comply with regulations around data privacy, security, and protection.

Conclusion

As data grows in size and complexity, data governance is becoming increasingly important for businesses. To get the most out of your data, it must be easily accessible, manageable, and secured. It must also be used in accordance with regulations and safeguarded against malicious activities. With the right data intelligence tools, you can mitigate the risks and accelerate the opportunities.

Fortunately, STORViX’s AiRE can help businesses overcome these challenges by providing end-to-end data integrity, predictive maintenance capabilities, workload optimization, and compliance with complex regulatory requirements. Therefore, businesses should consider implementing AI-driven data storage solutions to overcome their data governance challenges and improve their overall data management practices.

Don’t hesitate to reach out to us here or at info@storvix.eu if you want to learn more about our unique solution!

Source:

Techtarget, Data Governance

Quest, The top 7 data governance challenges organizations face and how to address them

Talend, What are data silos


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