How AI is Enhancing Data Health

How AI is Enhancing Data Health

May 26

Data has become the lifeblood of modern organizations, from small businesses to large multinational corporations. It is a key resource for achieving growth, strategizing for success, and staying ahead of the competition. By providing vital insights into consumer preferences, behavior patterns, market trends, and financial performance, data enables businesses to understand, accelerate, and optimize their operations.

However, the digital revolution has brought about an unprecedented influx of data that presents significant challenges such as data complexity, scalability issues, and data quality concerns, which raise the need for efficient data management. Fortunately, DataOps has emerged as a comprehensive approach that addresses these challenges by optimizing the entire data lifecycle. It encompasses streamlined data collection, efficient storage, robust data analysis, and seamless deployment, thereby enabling organizations to harness the full potential of their data in a structured and efficient manner.

However, even with DataOps in place—processing data while maintaining quality and accuracy can take up to 80% of a team’s time. By leveraging the vast capabilities of AI, we significantly accelerate this process and ensure faster insight generation.

In this blog, we will discuss the benefits of AI for improving data health.

Factors that Influence Data Health

Data health refers to the quality and accuracy of data. It is an essential aspect of DataOps which guarantees that data is reliable, consistent, free from errors, and ensures that it can be trusted for analysis and decision-making. The factors which influence data health are as follows:

  • Data Accuracy: The degree to which data reflects the real-world entities and events it represents.
  • Data Completeness: The extent to which data contains all the necessary information required for analysis and decision-making.
  • Data Timeliness: The degree to which data is up-to-date and relevant for the intended use.
  • Data Relevance: The degree to which data is applicable to the specific analysis or decision-making task at hand.
  • Data Reliability: The degree to which data is consistent and dependable over time.


Challenges in Managing Data Health

A major challenge in managing data health is the sheer volume of data being generated. As businesses collect data from various sources, it can quickly become overwhelming to keep track of all the data points and ensure their accuracy.

Additionally, data may exist in different formats or structures, making it difficult to consolidate and clean. Ensuring data accuracy and completeness requires continuous monitoring, validation, and cleansing to maintain the data's integrity. This process can be resource-intensive and time-consuming, requiring a dedicated DataOps team and robust data management processes.

Another hurdle in managing data health is the ever-changing nature of data. As businesses evolve and expand, their data needs may also change, requiring updates to data collection, data processing, and data enrichment procedures. This requires a dynamic approach to data health management that can adapt to changing needs while ensuring the accuracy and consistency of data.

How can Data Health be Improved?

To understand how data health can be improved it is important to discuss the issues that commonly occur in collected data. Some of these issues are given below:

  • Duplicate entries: The data has multiple copies of the same data point, event, or record.
  • Missing data: The data has important information missing.
  • Unclassified data: The data has to be analyzed for sensitive information or flagged for violating specified criteria.
  • Anomalies: There are values in the data that do not conform to its normal behavior.
  • Incorrect calculations: The source that generated the data did not perform the right calculations or there was a problem with the device.
  • Inconsistencies: The data has unexplainable variances.
  • Structural errors: Errors that have a very erratic nature. For instance, “Korea” spelled as “KoREa” in the country column in a dataset.


These are only a few of the many issues that DataOps teams have to cater to while processing data. To improve data quality, the DataOps team has to first analyze data to identify these issues and then develop strategies and algorithms to fix them.

Even with access to the best big data processing tools, identifying, and eliminating such errors requires a considerable level of manual effort.

Enhancing DataOps: Leveraging AI to Improve Data Health

AI can help DataOps teams reduce the time they spend on data health management while ensuring the accuracy and reliability of the data they work with. Not only does AI help eradicate data quality pain points, but it also allows the automation of repetitive and tedious processes.

In the following sections, we’ll discuss exactly how AI can enrich DataOps to achieve optimal data health:

Error Detection

Identifying pesky errors and hidden mistakes in data can be a tedious task. AI algorithms like K-Nearest Neighbors and K-Means can be used to identify anomalies and abnormalities in structured data, while sophisticated NLP models can detect if there are semantic or syntactic inconsistencies inside a text corpus. Similarly in image data, low resolution, noise, and blur can be detected using CNNs.

Generating Smart Suggestions

State-of-the-art AI algorithms can be used to create context-aware models that can parse through a huge volume of data and suggest or even automate possible corrections which would otherwise take a significant amount of time to implement. For instance, if in a text corpus, the letter “S” is written as “$” and “5”, and in some words, it’s completely missing then the NLP-based AI can suggest the appropriate action according to the particular case.

Ensuring Compliance

A key responsibility of the DataOps team is to ensure that the data is compliant with any standards that have been placed by authorities such as the FDA, GDPR, etc. Keeping track of compliance standards and ensuring them in billions of data points is a strenuous task. AI classification algorithms can autonomously spot non-compliant information like personally identifiable information and flag those data points so that they can be properly adjusted.

Automating Data Enrichment

Adding ancillary information to data can help improve its quality and in turn result in high-value insights and manageability. This can easily be achieved through AI-based named entity recognition, object detection, and sentiment analysis algorithms which help generate meta-data that can be used to sort, filter, classify, and analyze the available data without having to endure rigorous annotation processes. Bad-quality data such as images and audio files can also be enhanced using AI-based super-resolution algorithms that can upscale data quality and prevent useful data from being disposed of due to bad quality.


In conclusion, data has become a critical asset for businesses in the modern age, providing vital insights into consumer preferences, behavior patterns, market trends, and financial performance. However, managing data health and ensuring its quality and accuracy can be a significant challenge, given the sheer volume of data and its ever-changing nature. DataOps has emerged as a comprehensive approach that optimizes the entire data lifecycle, and by leveraging the vast capabilities of AI, businesses can significantly improve data health efficiently. AI algorithms can be used to identify errors and inconsistencies in data, generate smart suggestions, and ensure compliance with standards. By using AI to automate repetitive and tedious processes, DataOps teams can reduce the time spent on data health management and ultimately help organizations focus on making better decisions and serving their customers better.

Visionet strives to generate maximum value for its clients by utilizing cutting-edge technologies to ease modern workflows. Our data management services streamline operations and accelerate the deployment of models into production - ultimately helping organizations to be more efficient, effective, and agile in their data management.

Contact Visionet to upscale your data management solutions today.

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