Avoid Data Downtime and Improve Data Availability for AI/ML

AI/ML and analytics engines depend on the data stack, making high availability and reliability of data critical. As many operations depend on AI-based automation, data downtime can prove disastrous, bringing businesses to a grinding halt – albeit temporarily.

Data downtime encompasses a wide range of problems related to data, such as it being partial, erroneous, inaccurate, having a few null values, or being completely outdated. Tackling the issues with data quality can take up the time of data scientists, delaying innovation and value addition.

Data analysts typically end up spending a large part of their day collecting, preparing and correcting data. This data needs to be vetted and validated for quality issues such as inaccuracy. Therefore, it becomes important to identify, troubleshoot, and fix data quality to ensure integrity and reliability. Since machine learning algorithms need large volumes of accurate, updated, and reliable data to train and deliver solutions, data downtime can impact the success of these projects severely. This can cost companies money and time, leading to revenue loss, inability to face competition, and unable to be sustainable in the long run. It can also lead to compliance issues, costing the company in litigation and penalties.

Challenges to Data Availability

Some common factors that cause data downtime include:

Server Failure: If the server storing the data fails, then the data will become unavailable.

Data Quality: Even if data is available but is inconsistent, incomplete, or redundant, it is as good as not being available.

Outdated Data: Legacy data may be of no use for purposes of ML training.

Failure of Storage: Sometimes, the physical storage device may fail, making data unavailable.

Network Failure: When the network through which the data is accessed fails, data can become unavailable.

Speed of Data Transfers: If the data transfer is slow due to factors such as where data is stored and where it is used, then that can also cause data downtime.

Compatibility of Data: If the data is not compatible with the environment, then it will not be available for training or running the algorithm.

Data Breaches: Access to data may be blocked, or data may be stolen or compromised by malicious factors such as ransomware, causing data loss

Best Practices for Preventing Data Downtime

Given the implications of data downtime on machine learning algorithms, business operations, and compliance, enterprises must ensure the quality of their data. Some of the best practices in avoiding data downtime include:

Create Data Clusters:As storage devices, networks, and systems can fail, creating clusters to spread data will improve availability in case of failures and prevent or minimize data loss. To enable responding to issues at the earliest, tracking and monitoring of availability is also important. The infrastructure should be designed for load balancing and resiliency in case of DDoS attacks.

Accelerate Recovery: Failures are inevitable, and therefore, being prepared for a quick recovery is essential. It could range from troubleshooting to hardware replacement or even restarting the operating systems and database services. It requires the right skills to match the technologies used to speed up the process.

Remove Corrupted Data: Incomplete, incorrect, outdated, or unavailable cause data corruption. Such data cannot be trusted and requires a systematic approach to identify and rectify the errors. The process should be automated and prevent new errors from being introduced.

Improve Data Formatting and Organization: Often, enterprises grapple with the issue of inaccurate data. It is difficult to access and use due to being formatted differently. Deploying tools that can integrate data onto a shared platform is important.

Plan for Redundancy and Backups: Back up data and store it in separate locations or distributed networks to ensure availability and faster restoration in case data is lost or corrupt. Setting up storage devices in a redundant array of independent disks (RAID) configuration is also one approach for this.

Use Tools to Prevent Data Loss: Data breaches and data center damages can be mitigated using data loss prevention tools.

Erasure Coding: In this data protection method, data is broken into fragments, expanded, and then encoded with redundant pieces of data. By storing them across different locations or storage devices, data can be reconstructed from the fragments stored in other locations even if one fails or data becomes corrupted.

Indium to Ensure Your Data Quality

Indium Software is a cutting-edge technology solutions provider with a specialization in data engineering, data analytics, and data management. Our team of experts can work with your data to ensure 24×7 availability using the most appropriate technologies and solutions.

We can design and build the right data architecture to ensure redundancy, backup, fast recovery, and high-quality data. We ensure resilience, integrity, and security, helping you focus on innovation and growth.

Our range of data services include:

Data Engineering Solutions to maximize data fluidity from source to destination BI & Data Modernization Solutions to facilitate data-backed decisions with insights and visualization

Data Analytics Solutions to support human decision-making in combination with powerful algorithms and techniques

AI/ML Solutions to draw far-reaching insights from your data.

FAQs

What is the difference between data quality and accuracy?

Data quality refers to data that includes the five elements of quality:

● Completeness

● Consistency

● Accuracy

● Time-stamped

● Meets standards

Data Accuracy is one of the elements of data quality and refers to the exactness of the information.

Why is data availability important for AI/ML?

Large volumes of high-quality, reliable data is needed to train artificial learning/machine learning algorithms. Data downtime will prevent access to the right kind of data to train algorithms and get the desired result.