Data Management Glossary
Garbage In, Garbage Out (GIGO)
What Is Garbage In, Garbage Out (GIGO)?
Garbage in, garbage out is a computing principle stating that the quality of a system’s output is determined by the quality of its input. Feed a system bad data and it produces bad results, regardless of how sophisticated the processing is. The principle applies equally to a 1960s mainframe batch job and a large language model trained on petabytes of enterprise files.
The term originated in the early days of computing. George Fuechsel, an IBM programmer, is credited with coining it in the 1960s as shorthand for a simple truth: computers do exactly what they are told, and if the instructions or data are flawed, the output will be too. The phrase spread quickly because it captured something that every programmer had experienced firsthand.
For decades GIGO was treated as a programming problem. Write clean code, validate your inputs, and the principle becomes manageable. As computing moved into data-intensive applications, the problem shifted. The inputs were no longer just code. They were data, and data was growing in ways that made validation increasingly difficult.
Why GIGO Is More Relevant Now Than Ever
The enterprise AI era has made GIGO an infrastructure problem, not just a software problem. Organizations are feeding unstructured data, including documents, images, medical scans, genomic sequences, email archives, video files, and contracts, into AI models at a scale that was unimaginable when the term was coined.
The numbers make the problem concrete. In 2022, 90% of the data generated by organizations was unstructured, and only 10% was structured. Organizations globally generated 57,280 exabytes of unstructured data that year, a volume expected to grow 28% to over 73,000 exabytes in 2023. The Komprise 2026 State of Unstructured Data Management report, based on a survey of 300 enterprise IT directors, VPs, and C-level executives, found that 74% of enterprise IT leaders are now managing more than 5 petabytes of unstructured data, a 57% increase over 2024. Forty percent are managing more than 10 petabytes.
The challenge is that most of this data was never managed with AI readiness in mind. It accumulated on NAS environments, object stores, and cloud buckets over years or decades, without classification, without quality assessment, and without any mechanism to distinguish current, relevant data from outdated or redundant files. According to IDC research, only half of an organization’s unstructured data is analyzed to extract value from it, and only 58% of unstructured data is ever reused more than once after its initial use. When organizations feed this estate directly into an AI pipeline without first curating it, GIGO follows.
Gartner has quantified the consequence. In a February 2025 press release, Gartner predicted that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. The same research found that 63% of organizations either do not have or are unsure if they have the right data management practices for AI. Meanwhile, 85% of enterprise IT leaders in the Komprise 2026 survey are projecting an increase in data storage spend, and more than half are already spending over 30% of their total IT budget on data storage, backups, and disaster recovery. The bill for unmanaged unstructured data is rising on both sides: more to store it, and less value extracted from it.
- Source: IDC, “Untapped Value: What Every Executive Needs to Know About Unstructured Data,” August 2023, IDC #US51128223, sponsored by Box.
- Source: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 26, 2025.
- Source: Komprise 2026 State of Unstructured Data Management Report.
ROT Data: The Dominant Source of GIGO in the Enterprise
The most common source of GIGO in enterprise AI is ROT data. ROT stands for Redundant, Obsolete, and Trivial. It describes files that exist on enterprise storage but carry no current business value.
Redundant data includes duplicate files, multiple copies of the same document saved across shared drives, backup snapshots that have outlived their retention purpose, and identical files stored under different names. Obsolete data includes project files from completed work, superseded document versions, and files that were relevant at a point in time but no longer reflect current information. Trivial data includes log files, temp files, thumbnails, and other system-generated artifacts never intended for long-term retention.
IDC research puts a number on the problem. Twenty-two percent of unstructured data is unnecessarily replicated because organizations simply do not know what they have or how to find it. Forty-six percent of organizations say less than half of all their unstructured data is being analyzed to extract value, and 40% of the analysis that does happen is still mostly manual. This data costs money to store. More importantly for AI, it degrades model quality when included in training sets or retrieval pipelines. The Komprise 2026 survey reinforces the point from the IT side: 58% of enterprise IT leaders cite classifying data for AI as their top technical challenge.
ROT data is largely invisible without analysis. A NAS environment reports total capacity consumed but not what that capacity contains. Without classification at the file level, there is no way to distinguish a current contract from a 10-year-old draft, or a production medical image from a test file generated during system validation. The GIGO problem in enterprise AI starts not in the model, but in the storage environment that feeds it.
Why Unstructured Data Makes GIGO Harder to Solve
Structured data in databases has always been relatively tractable. Schemas enforce consistency. Validation rules catch bad inputs. Data quality and master data management tools for structured data are mature.
Unstructured data is different. There is no schema. A file system does not know whether a document is a duplicate, whether it is current, or whether it contains sensitive information that should not be included in an AI training set. The system metadata that file systems do track, including filename, size, and modification date, is insufficient to make quality determinations at the content level.
The AI imperative makes the gap more acute. The IDC research found that only 3% of organizations are not considering generative AI deployment. Yet the number one roadblock to GenAI adoption, cited by 49% of organizations in the same IDC report, is concern about releasing proprietary content into the large language models of GenAI technology providers. A close second, cited by 47%, is lack of clarity about intellectual property rights around the content used to train those models. Both concerns trace back to the same root cause: organizations do not have sufficient visibility into what their unstructured data contains, where it lives, or whether it is appropriate for AI use. You cannot govern what you have not classified.
A 2025 Komprise enterprise AI survey found that nearly 80% of organizations have experienced negative data incidences with generative AI – with 13% resulting in financial, customer or reputational damage. 90% are concerned about shadow AI from a privacy and security standpoint, with 46% reporting that they are “extremely worried.
Organizations cannot solve the GIGO problem by buying more storage or deploying a new AI platform. They need to analyze their unstructured data estate, classify files by content and context, identify and remove ROT data, and enrich the files that remain with the metadata that AI workflows require.
How Komprise Addresses GIGO in Unstructured Data
Komprise addresses the GIGO problem at the infrastructure level, before bad data enters an AI pipeline.
Komprise Analysis scans the full unstructured data estate across NAS, object storage, and cloud without requiring a migration. It surfaces the composition of that estate: how much data is cold, how much is duplicated, which files have not been accessed in years, and how storage consumption is growing over time. This gives IT and data teams the visibility to understand the scale of the ROT problem before addressing it. IDC found that 92% of organizations say a unified, governed platform for unstructured data would have a moderate to extremely positive impact on innovation and costs. Komprise delivers that visibility across existing storage infrastructure without replacing it.
Komprise Deep Analytics queries the Global Metadatabase, the centralized metadata index that Komprise continuously builds across every storage environment, to filter and curate data by file type, owner, age, location, and Komprise tags. Deep Analytics does not open files or scan content. Its power is precision: it reduces a petabyte-scale data estate to the specific subset that matters, before any content processing begins.
Smart Data Workflows operate on the curated datasets that Deep Analytics identifies. They crack open files, scanning content for PII using 68 built-in content scanners, searching for custom patterns using regular expressions, and executing KAPPA functions against file headers to extract embedded metadata. When sensitive data is found, Smart Data Workflows can tag the file, confine it to a protected admin area with the full original folder structure preserved, and allow IT to sanitize or review it before any further action. The 75% of organizations in the Komprise 2025 AI Survey who plan to use data management technologies to address shadow AI risk are describing exactly this capability.
KAPPA data services (Komprise AI Preparation and Process Automation) address the enrichment side of the GIGO problem. Once ROT and sensitive data are identified and removed from the working dataset, KAPPA extracts embedded metadata from remaining files, including clinical headers from DICOM files, sequencer metadata from FASTQ files, and custom attributes from many other file format, using serverless Python execution at petabyte scale. The extracted metadata loads directly into the Global Metadatabase, making the remaining data precisely searchable and AI-ready.
Once the data is clean and enriched, the Intelligent AI Ingest capability makes a high-speed copy of the curated dataset to the target AI environment. Only the right data moves. Only the right data trains the model.
The Komprise 2026 survey found that future requirements for unstructured data management are led by data classification and tagging (61%), analytics and reporting (60%), and sensitive data detection (57%). These three capabilities map directly to the GIGO problem. Classification and tagging identify what should not enter an AI pipeline. Analytics and reporting maintain visibility into what is accumulating. Sensitive data detection prevents governance failures before they happen. Gartner predicts that by 2030, 50% of organizations will use autonomous AI agents to interpret governance policies into machine-verifiable data contracts, a future that only becomes viable when the underlying data has been classified and governed at the file level today.
Source: Komprise 2025 AI Survey: AI, Data and Enterprise Risk.
Source: Komprise 2026 State of Unstructured Data Management Report.
Source: Gartner, “Top Predictions for Data and Analytics in 2026,” March 11, 2026.
How Do Data Lakes, Data Lakehouses, and Data Fabric Relate to GIGO?
The architectures organizations use to manage large-scale data, including data lakes, data lakehouses, and data fabric, all share a foundational dependency on the quality of the unstructured data they hold. GIGO applies to each of them.
A data lake is a centralized repository that stores raw data in its native format until it is needed for analysis. Data lakes were designed to hold large volumes of unstructured and semi-structured data that traditional data warehouses could not handle. In practice, many data lakes became difficult to govern. Without strong metadata management and classification, data accumulated faster than it could be cataloged or used, giving rise to the term “data swamp” to describe a data lake where data quality and discoverability had broken down. A data lake that accepts all data indiscriminately produces analytics and AI outputs that reflect the noise in the data, not the signal.
A data lakehouse combines the low-cost, high-volume storage of a data lake with the structure, governance, and query performance of a data warehouse. It applies schema, indexing, and transaction support directly to data stored in open formats such as Apache Parquet or Delta Lake, allowing analytics and AI workloads to run against raw data without first moving it to a structured system. The data lakehouse has become a common target architecture for organizations building AI data infrastructure. But the lakehouse assumes that incoming data has been curated. Feeding a data lakehouse with unclassified, unenriched unstructured files does not deliver the governance and query performance the architecture promises.
Data fabric is an architectural concept rather than a specific technology. It describes a design approach in which data management, governance, and integration capabilities are distributed across a hybrid environment, connecting data wherever it lives across on-premises systems, cloud platforms, and edge locations. A data fabric does not require data to be centralized. It applies consistent metadata, policy, and access controls across a distributed data estate. Without a unified metadata layer spanning that estate, a data fabric cannot deliver the discoverability it is designed to provide.
All three architectures depend on clean, classified, enriched unstructured data at the point of ingestion. Before data reaches a lake, lakehouse, or fabric, it needs to be analyzed to remove ROT, classified to flag sensitive content, and enriched with the embedded metadata that makes it queryable. Addressing GIGO at the source is what makes these architectures deliver on their promise.
Frequently Asked Questions
What does GIGO stand for?
GIGO stands for Garbage In, Garbage Out. It is a computing principle stating that the quality of a system’s output is determined by the quality of its input data.
Where did the term GIGO come from?
The term is generally credited to George Fuechsel, an IBM programmer who used it in the 1960s to describe the relationship between input data quality and output reliability in early mainframe computing.
Why is GIGO relevant to enterprise AI?
Enterprise AI models and retrieval pipelines depend on unstructured data as input. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. The Komprise 2025 AI Survey found that nearly 80% of enterprises have already experienced negative AI data incidents, with 13% reporting financial or reputational damage. Feeding an unmanaged, unclassified unstructured data estate into an AI pipeline without curation produces unreliable results, regardless of model sophistication.
Sources: Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 26, 2025.
What is ROT data?
ROT data stands for Redundant, Obsolete, and Trivial data. It describes files that exist on enterprise storage but have no current business value. ROT data is the primary source of GIGO in enterprise AI environments because it accumulates silently on NAS and object storage without classification, making it indistinguishable from current, valuable data without targeted analysis.
How does Komprise help solve the GIGO problem?
Komprise addresses GIGO across four steps. Komprise Analysis identifies the ROT data composition of the full data estate. Deep Analytics queries the Global Metadatabase to filter and curate the specific dataset to act on. Smart Data Workflows scan file content for PII, sensitive data, and custom patterns using regular expressions and built-in content scanners, tagging and confining files that should not enter an AI pipeline. KAPPA data services then extract embedded metadata from the clean, curated files and load it into the Global Metadatabase, producing an enriched, AI-ready dataset. The Intelligent AI Ingest capability delivers that curated dataset to the target AI environment at high speed.
What is the difference between GIGO in traditional computing and GIGO in AI?
In traditional computing, GIGO was primarily a programming problem addressed through input validation and code quality. In AI, GIGO is an infrastructure problem. The inputs are petabytes of unstructured files accumulated over years without quality controls, and the consequences include failed AI initiatives, wasted compute spend, and degraded model reliability. The Komprise 2026 State of Unstructured Data Management report found that 74% of enterprise IT leaders are now managing more than 5 petabytes of unstructured data, a 57% increase over 2024, making the scale of the risk larger than it has ever been.
What percentage of enterprise data is unstructured?
IDC research found that in 2022, 90% of the data generated by organizations was unstructured and only 10% was structured. Gartner predicts that by 2029, AI agents will generate 10 times more data from physical environments than from all digital AI applications combined, meaning the share of unstructured data in the enterprise will only grow.
Why is unstructured data hard to classify?
Unlike structured data in databases, unstructured data has no schema. A file system does not know whether a document is a duplicate, whether it is current, or whether it contains sensitive information. The metadata file systems do track, including filename, size, and modification date, is insufficient to make content-level quality determinations. The Komprise 2025 AI Survey found that 54% of IT leaders cite finding and moving the right data to AI ingestion locations as their top data preparation challenge. Solving that problem requires automated content scanning and metadata enrichment, not more storage capacity.