Several companies that offer data-based services often struggle to deliver real-time services. The major reason behind this inefficiency is the use of traditional data integration techniques for data consolidation. The process of physically extracting, transforming, and loading data to a storage system is lengthy and resource-intensive.
To overcome delayed operations, businesses can opt for data virtualization, a more modern approach for consolidating organizational data. It provides a unified view of data stored in diverse systems without requiring its physical movement. By combining virtualization with predictive caching, organizations can reduce their query latency and ETL operational costs. Thus, data virtualization, along with machine learning capabilities, can help in improving daily data operations.
With the evolution of AI, the data virtualization process has become much more effective as AI facilitates automated data discovery, caching, and metadata management. Alternatively, data virtualization can help in developing robust AI/ML models through real-time data access for model training and fine-tuning. As a result, AI and data virtualization are complementary to each other.
According to a report released by Industry ARC, the data virtualization market size is projected to increase to nearly $16000 million by 2030 with a CAGR of 20% between 2024-2030. This indicates the increasing adoption of data virtualization by numerous organizations for managing data-driven operations.
This article explains how data virtualization and AI aid each other to facilitate effective data management and the development of robust AI applications.
How AI and ML Technology Can Improve Data Virtualization
Automatic Data Discovery
Machine learning algorithms can automatically discover relevant data sources within numerous data systems that are used by your organization. AI models with NLP capabilities can help in linking similar data records scattered across different sources. This reduces the complexities of data matching, saving time required for understanding relationships between numerous data points.
For example, customer_id in one dataset can represent the same entity as customer_no in another dataset. You can also use AI and machine learning algorithms for semantic data mapping to identify contextually related data records.
Schema Mapping and Evolution
Different data sources can have different schemas, which can lead to data inconsistencies while creating a virtual data layer. During data virtualization, schemas of various source data systems must be compatible to maintain data consistency.
There are a few AI tools that can help in mapping schemas of different data systems. To do this, the AI tools utilize NLP, ontology, or anomaly detection techniques. Using NLP embeddings, you can identify data fields with similar semantic meaning. Given below is an example of using embeddings to track semantically same data records:
You can set a minimum score, and any value above that score, for example, score > 0.80, indicates automatic mapping.
On the other hand, in ontology-based matching, you need to utilize domain-specific knowledge to combine data from heterogeneous sources. For example, using ontology-based matching techniques, you can join the data fields ‘sales’ and ‘revenue’ of different datasets that actually convey the same information.
Schema mapping involves mapping data fields or columns, while data matching involves linking similar data records. Thus, the two processes differ from each other. Using only AI algorithms for schema mapping can sometimes lead to incorrect mapping and reworking. In practice, it is better to implement AI schema mapping, along with human expertise, to avoid the occurrence of any errors.
Effective Data Caching
Data caching is a practice of storing frequently used data temporarily at a quickly accessible location, like memory or a dedicated cache system. This facilitates faster retrieval of important and often-used data records. Caching during data virtualization can improve the performance of your data workflows by enabling faster data retrieval and query execution.
As shown in the example above, machine learning models can aid in refining the caching mechanism by predicting beforehand which data will be frequently requested. You can also use AI to identify data that requires complex queries or time for retrieval. Based on this, you can create local caches of such data for faster business operations.
However, predictive caching delivers effective results only during high-demand periods like end-of-year reporting. In reality, a one-size-fits-all approach backfires, so fine-tuning models is essential.
Rise of AI Agents
AI agents can play a significant role in streamlining data virtualization. Built on top of LLMs, these agents can understand natural language and perform data-driven tasks. You can give prompts to AI agents to search for relevant data sources, eliminating the need to perform the same task manually. For example, you can ask your AI agent to find the sales dataset for European regions for the first quarter. The agent will find the required datasets and combine them through a virtual data layer to give you a complete picture of sales in Europe.
Metadata tagging is the process of adding labels to various fields within a dataset and forms a critical part of data virtualization. Based on the training data, an AI agent can analyze columns within your dataset and provide relevant tags. For example, while preprocessing data during virtualization, an AI agent trained on enterprise data may tag the column named ‘cust_id’ as a customer identifier based on a naming convention or historical training data.
In addition to this, you can also use AI agents to perform quality checks on your virtualized data. The agents can be trained to perform rule-based checks and detect anomalies to ensure data integrity.
How AI-based Data Virtualization is Better than Conventional Data Virtualization?
Here is a comparison table that portrays how AI-driven data virtualization differs from traditional data virtualization:
Challenges and Best Practices for Performing Data Virtualization With AI and ML
Maintaining Data Quality
Challenge : To train effective AI models, you require access to high-quality datasets. However, during data virtualization, there is a high possibility that the data that you have collected from multiple sources is incomplete or contains duplicate records.
Best Practice: Utilize AI-based data cleaning solutions to cleanse and transform data in all the source systems before virtualization.
Latency Issues
Challenge: Latency issues can arise when you query data unified using data virtualization, as it requires time to retrieve data from all the relevant sources. Such a time lag in data querying affects the downstream operations.
Best Practice: Leverage caching and in-memory data grids for quick data access. You can use machine learning models to predict data load for improving query performance.
Data Security Risks
Challenge: Sensitive data accessed through virtualization increases the data security risks, such as breaches and cyberattacks. It also violates data sharing guidelines under data protection frameworks like GDPR and HIPAA.
Best Practice: Introduce role-based access control, authorization mechanisms, and data masking in your virtual data layer. You can also leverage AI-based security systems to monitor accessibility and any data leakages in real-time.
Data Governance Framework for AI-enabled Data Virtualization
Data governance architecture is the most critical component of the virtualization process and should consist of:
Role-based Access Control: Restrict access to sensitive data only to selective authorities. This should especially be practiced for handling sensitive data related to healthcare or finance.
AI-powered Monitoring: Leverage AI-based data security frameworks that detect anomalies in real-time. This helps in taking quick actions for resolution when needed.
Lineage Tracking: Utilize data lineage techniques to track the movement of data throughout your workflow operations. This helps in tracing the source data systems if the data records are not according to requirements, helping you refine them in a timely manner.
Role of Data Virtualization in Developing AI and ML Models
Till now, you have seen how AI-powered data virtualization is critical for effective data management. Let’s see what role data virtualization plays in developing AI models:
Real-time Data Access for Model Training
Data virtualization provides you with quick access to the data required to train AI and ML models. This allows you to train your AI models with the most up-to-date data, facilitating real-time or near-real-time operations.
Effective Feature Engineering
While training machine learning models, you often have to perform feature engineering to improve the model’s performance. Data virtualization provides an interface to quickly combine or separate features according to model training requirements. This allows models to continuously adapt to new data combinations without delay, which is critical in sectors such as finance for fraud detection.
Better Model Development
Data virtualization simplifies primary model development by facilitating quick access to different data combinations and feature sets. You can train multiple models effectively by retrieving data from the virtual layer. Such ease of data access is specifically important for conducting A/B testing for various models, iterative training, and faster AI application development.
Challenges and Best Practices for Using Data Virtualization for AI/ML Development
Data Bias
Challenge: The data is unified from several different sources during the data virtualization process. Due to this, it may contain outliers, missing values, or inaccurate data that can introduce bias. AI models trained on such datasets hallucinate and give incorrect outputs.
Best Practice: Continuously assess datasets and clean the data records to improve the quality of the virtual data layer. Then, utilize this data to train AI/ML models if you want to obtain highly accurate results from them.
Data Governance and Explainability
Challenge: During data virtualization, data governance issues like a lack of data lineage may arise due to the loss of information about the source from which data is collected. This impacts the explainability feature in AI models, based on which you can track why the model has generated a specific response. AI explainability is especially critical for industries such as finance and healthcare, and its absence discourages ethical AI usage.
Best Practice: Build a robust data governance architecture with strong data versioning and validation mechanisms. Take inputs from developers, data engineers, and other stakeholders to ensure that data governance does not impact the quality of your AI system.
Contextual Correctness in AI Models
Challenge: To produce accurate outcomes, AI and ML models should have an excellent understanding of data context and semantics. However, the virtual data layer may contain data records with mismatched naming conventions and schemas, leading to contextual inaccuracies in the outcomes created by LLMs.
Best Practice: Leverage AI-powered metadata management and data discovery tools to map data semantics during data virtualization. This will aid in reducing contextual mistakes in LLM outputs.
Common Use Cases of Data Virtualization
Healthcare: In a hospital, you can create a virtual data layer from various data sources, such as patient health records, data from lab tests, and imaging systems. With the help of AI models, you can analyze the virtualized data for anomalies such as unusual test results or missing patient information. After eliminating these anomalies, you can easily access this data for faster diagnosis and treatment.
Finance: In financial institutions and banks, you can use AI-based data virtualization for quick fraud detection. Banks usually store data related to credit cards, loans, and investments across different data systems. Using data virtualization, you can create a unified view of all this data. Then, using AI/ML models, you can detect suspicious transactions to identify fraudulent activities.
Human Resources: You can use AI-powered data virtualization for hiring talented professionals and workforce management. By virtualizing data, you can access complete data related to shortlisted candidates, such as personal information, educational qualifications, and job-related skillsets. Using AI tools, you can find the skill gaps and sort the most qualified candidates for hiring. The AI models can also analyze employee performances and study churning patterns to facilitate effective workforce management.
Retail Sector: You can create a virtualized dataset by combining inventory, sales, and customer feedback datasets. After this, you can use an AI tool to study buying behavior and demand for various products. Based on these insights, you can restock your inventory to ensure continuous sales.
Governance and Compliance: Implementing a strong data governance framework is critical to ensure accountability and transparency while working on any AI-driven project. You can achieve this using data virtualization that allows you to track the source of data through the process of data lineage. A data virtualization tool like Denodo allows you to track all the transformations made to the virtual dataset, thereby facilitating version control. You can revert these changes anytime to reproduce the original data. A robust data governance architecture also enables compliance with data protection frameworks like GDPR to maintain data security.
Final Thoughts
As this article has shown, the combination of data virtualization and AI can bring a significant shift in how businesses can manage their data. By shifting from traditional, slow, and costly data integration methods, data virtualization provides real-time access to a unified data collected from disparate sources. Such centralized data is critical to train and develop AI and ML algorithms.
At the same time, AI has the capability to refine data virtualization by automating data discovery and processing for better data management at the enterprise level. Together, data virtualization and AI can create a mutually beneficial ecosystem that supports smarter and faster data operations.
The best approach to deploy AI-enabled data virtualization in your workflows is by first implementing it for a small use case. For example, banks can virtually gather transaction data using tools like Denodo or Tibco. After successful deployment at a smaller level, businesses can scale up their data virtualization operations across the whole organizational workflow.
The future of enterprise data management relies on integrating these efficient solutions into everyday workflows. In your business, you can explore how data virtualization can break down data silos while harnessing AI to extract maximum value from your data. Starting this journey is no longer optional but essential for maintaining a competitive advantage.
Top comments (0)