Data Management Glossary
Synthetic Data
What Is Synthetic Data?
Synthetic data is artificially generated data that mirrors the statistical properties, structure, and patterns of real-world data without containing actual records from real individuals, systems, or events. It is created algorithmically, typically using generative models, rule-based systems, or statistical simulation, to produce datasets that behave like real data for purposes of AI model training, software testing, analytics development, and data sharing.
The appeal of synthetic data is straightforward. Real enterprise data is often sensitive, regulated, incomplete, or simply too difficult to move at the scale required for AI workflows. Synthetic data offers an alternative input that preserves the characteristics of the original dataset while removing the compliance burden of working with the real thing.
Synthetic data is not a new concept. Statistical agencies have used synthetic or perturbed microdata for decades to release public datasets without exposing individual records. What has changed is the scale of demand. As organizations race to train large language models, fine-tune domain-specific AI, and build retrieval-augmented generation pipelines, the need for large, representative, clean datasets has grown faster than the ability to source, classify, and govern real data at speed.
What Types of Synthetic Data Exist?
Synthetic data takes several forms depending on the use case and the underlying generation method.
Fully synthetic data contains no records derived directly from real observations. Every record is generated from a learned or specified statistical model. Partially synthetic data replaces sensitive or high-risk fields in a real dataset with synthetically generated values while preserving the structure of the remaining records. Augmented data uses synthetic generation to extend a real dataset, typically to address class imbalance in training sets or to simulate conditions that are rare in real-world observations.
In the context of unstructured data, synthetic generation extends to images, audio, video, documents, and domain-specific file formats. A synthetic DICOM file, for example, can carry realistic imaging characteristics and header metadata without containing any patient information. A synthetic FASTQ file can simulate genomic sequencing output with the statistical properties of real reads without exposing donor identities.
Synthetic Data Is a Response to an Unstructured Data Problem
The growing interest in synthetic data in enterprise AI is largely a symptom of an unresolved unstructured data management problem. Organizations are not turning to synthetic data because it is inherently preferable to real data. They are turning to it because their real unstructured data is not AI-ready.
According to Gartner, through 2026 organizations will abandon 60% of AI projects unsupported by AI-ready data, and 63% of organizations either do not have or are unsure if they have the right data management practices for AI. The Komprise 2025 AI Survey, which surveyed 200 IT directors and executives at U.S. enterprises with 1,000 or more employees, found that 54% cite finding and moving the right data to AI ingestion locations as their greatest challenge in preparing unstructured data for AI. Nearly 80% have already experienced negative data incidents with generative AI.
When real data cannot be found, classified, governed, or moved at the pace AI projects require, synthetic generation becomes the workaround. The underlying problem is not that real data does not exist. It exists in abundance. According to IDC research, 90% of the data generated by organizations is unstructured, and only half of it is analyzed to extract value. The barrier is not volume. It is manageability.
Synthetic data is a useful tool in specific circumstances, but it carries real limitations that organizations need to understand before treating it as a substitute for governed, curated real data.
The first limitation is distributional fidelity. Synthetic data is only as good as the model used to generate it. If that model was trained on a biased, incomplete, or unrepresentative sample of real data, the synthetic output will inherit those problems. Garbage in, garbage out applies to synthetic generation as directly as it applies to any AI pipeline.
The second limitation is domain specificity. Synthetic data generation for general text or tabular data is mature. Synthetic generation for complex domain-specific unstructured formats, including high-resolution medical imaging, genomic sequences, seismic well logs, and proprietary instrument output, is far less reliable. Models trained on synthetic versions of these data types may fail to generalize to real-world inputs.
The third limitation is regulatory uncertainty. In regulated industries including pharmaceutical, life sciences, and genomics, healthcare, and financial services, regulators are still determining what constitutes acceptable use of synthetic data for model training, validation, and reporting. Organizations that rely heavily on synthetic training data may face downstream compliance questions that real, governed data would not raise.
The fourth limitation is the opportunity cost of not managing real data. Every organization sitting on petabytes of real, domain-specific unstructured data has a competitive asset that no synthetic generation pipeline can replicate. Medical imaging archives built over decades, genomics repositories from completed trials, well log libraries from producing fields: these datasets carry signal that synthetic data cannot reproduce. The organizations that learn to govern and activate that data will have a durable AI advantage over those that default to synthetic substitutes.
Why Unstructured Data Management Is the Right Long-Term Answer
The case for investing in unstructured data management is not a rejection of synthetic data. Synthetic data has legitimate uses in testing, augmentation, and privacy-preserving data sharing. The argument is that treating synthetic data as the primary strategy for AI training datasets is a workaround, not a solution, and that the workaround becomes unnecessary when real unstructured data is properly managed.
Managing real unstructured data for AI requires four capabilities that synthetic data cannot provide and that standard storage infrastructure does not deliver.
The first is cross-silo discovery. Real unstructured data is distributed across NAS environments, object stores, and cloud platforms accumulated over years. Without a unified metadata index spanning all of those environments, there is no way to identify which files are relevant to a given AI use case. You cannot curate what you cannot find.
The second is content-level classification. A file system knows filenames, sizes, and access dates. It does not know whether a file contains a social security number, whether it is a duplicate of another file, or whether it belongs to a completed project that should be retired. Classification at the content level is required before any file can safely enter an AI pipeline.
The third is modality-specific metadata enrichment. The embedded metadata in domain-specific file formats, including DICOM clinical headers, FASTQ sequencer metadata, LAS well log fields, and EXIF image attributes, is invisible to standard storage systems. Extracting and indexing that metadata is what makes real unstructured files searchable, filterable, and queryable by the criteria AI teams actually care about.Visit the KAPPA Data Services Library.
The fourth is governed delivery. Moving the right files to an AI platform, at the right time, with sensitive content removed and provenance intact, requires workflow automation that operates at file level across the entire data estate without requiring a full migration upfront.
How Komprise Supports Both Paths
Komprise Intelligent Data Management addresses the unstructured data management problem that drives organizations toward synthetic data in the first place. It also supports legitimate synthetic data workflows where real data needs to be validated, governed, or enriched before use as training input for synthetic generation models.
Komprise scans the full unstructured data estate across NAS, object storage, and cloud without requiring a migration, surfacing what data exists, where it lives, and what its usage patterns look like. This gives IT and data teams the visibility to identify the real unstructured datasets worth activating rather than defaulting to synthetic generation.
Komprise Deep Analytics queries the Komprise Global Metadatabase to filter and curate data by file type, owner, age, location, and Komprise tags. IT teams can identify the specific subset of real files relevant to an AI use case across petabyte-scale estates in minutes, without touching the underlying storage.
Smart Data Workflows process file content directly, scanning for PII and sensitive data using 68 built-in content scanners, custom regex patterns, and KAPPA-defined extraction functions. Files that should not enter an AI pipeline are tagged and confined before any data moves. This is the step that makes real data safe to use in AI workflows, removing the compliance concern that often drives the turn toward synthetic alternatives.
KAPPA data services (Komprise AI Preparation and Process Automation) extract embedded metadata from domain-specific file headers at scale, including clinical attributes from DICOM files, sequencer metadata from FASTQ files, subsurface fields from LAS well logs, and image attributes from EXIF-tagged files. The extracted metadata loads into the Global Metadatabase, making real unstructured files searchable and queryable by the domain-specific criteria that matter to the AI team.
Komprise Intelligent AI Ingest delivers the curated, governed, enriched real dataset to the target AI environment at high speed, via Komprise Transparent Move Technology, without a bulk migration and without unnecessary egress costs.
The Komprise 2026 State of Unstructured Data Management report found that data classification and tagging (61%), analytics and reporting (60%), and sensitive data detection (57%) are the top three unstructured data management priorities for enterprise IT in 2026. All three are preconditions for activating real unstructured data for AI, and all three make synthetic data substitution less necessary.
Frequently Asked Questions
What is synthetic data?
Synthetic data is artificially generated data that mirrors the statistical properties and patterns of real-world data without containing actual records from real individuals or systems. It is used in AI model training, software testing, analytics development, and privacy-preserving data sharing.
Why do enterprises use synthetic data?
Enterprises use synthetic data primarily when real data is unavailable, too sensitive to use directly, insufficient in volume, or too difficult to classify and govern at the pace AI projects require. In many cases the turn toward synthetic data reflects an unresolved unstructured data management problem rather than a genuine preference for artificial inputs.
What are the limitations of synthetic data for AI training?
Synthetic data inherits the biases and gaps of the models used to generate it. For complex domain-specific formats including medical imaging, genomics, and geoscience data, synthetic generation is less reliable than for general text or tabular data. Regulatory acceptance of synthetic training data is also still evolving in pharmaceutical, life sciences, and genomics, healthcare, and financial services industries.
Is synthetic data a replacement for real enterprise data?
No. Synthetic data is a useful tool for specific use cases including testing, augmentation, and privacy-preserving sharing, but it cannot replicate the signal in real, domain-specific unstructured data built up over years of operations. Organizations that govern and activate their real unstructured data have a competitive AI advantage that synthetic generation cannot substitute.
How does poor unstructured data management drive the use of synthetic data?
When real data cannot be found across distributed storage silos, classified for sensitivity, enriched with domain metadata, or moved to an AI platform without compliance risk, synthetic generation becomes the workaround. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. Investing in unstructured data management resolves the root cause that makes synthetic substitution attractive.
How does Komprise support AI data preparation compared to synthetic data generation?
The Komprise Intelligent Data Management Platform for AI provides the classification, enrichment, governance, and delivery capabilities that make real unstructured data AI-ready. Rather than replacing real data with synthetic alternatives, Komprise activates the real data organizations already hold, at scale, across existing storage infrastructure.