Data Management Glossary
Data Quality
What is data quality?
Data quality is the degree to which data is fit for its intended use. It is measured across a set of dimensions: accuracy, completeness, consistency, timeliness, validity and uniqueness. High-quality data is correct, current, free of duplicates and described well enough that people and systems can find it, trust it and use it.
Poor data quality is expensive. Gartner estimates that poor data quality costs organizations an average of $12.9 million a year.
Source: Gartner, Data Quality: Why It Matters and How to Achieve It
Why does data quality matter for AI?
AI multiplies whatever it is given. A model or agent fed duplicates, outdated drafts and contradictory versions returns wrong or inconsistent answers, the classic garbage in, garbage out problem, and a leading cause of AI hallucination in retrieval augmented generation (RAG).
Data quality is now the main gap between AI ambition and AI results. In a Komprise survey of 500 IT and data leaders, 68% said their organizations were maturing or mature in AI, yet only 28% on average had quantified ROI from AI. Just 7% had 70% or more of their data cleaned, prepared and classified for AI. Nearly half named finding the right data and cleaning up low-quality, duplicate or redundant data as their largest readiness gaps.
Source: Komprise Survey of Unstructured Data and AI, October 2026
Poor quality also costs money on every AI call. Inferencing is billed by the token, so every duplicate or irrelevant file that reaches a model adds cost without adding value. Komprise COO Krishna Subramanian notes that metadata enrichment, which lets teams filter out irrelevant and duplicate content before inference, cuts AI compute and storage costs by 80% or more.
Source: Komprise, Unstructured Data Quality’s Role in AI Inferencing Costs
How is data quality different for unstructured data?
Traditional data quality tools check rows and columns against a schema: a missing field, a malformed date, a duplicate customer record. Unstructured data, which IDC and Gartner estimate makes up 80% to 90% of enterprise data, has no schema to check against. Documents, images, video, sensor output, research files and logs are spread across NAS, cloud and object storage, often in the billions of files. Quality has to be judged from the metadata and context around each file.
The standard dimensions still apply, but they look different for files:
- Accuracy: is this the current, approved version, or an outdated draft that contradicts it?
- Completeness: does the file carry the tags, owner, project and classification needed to find and govern it?
- Consistency: do names, paths and tags follow the same conventions across shares, sites and clouds?
- Timeliness: when was the file last modified or accessed, and is it still relevant?
- Uniqueness: how many copies exist across storage silos?
- Validity: is the file in a usable format, readable and uncorrupted?
- Appropriateness: does it contain PII or confidential content that should not reach an AI tool?
Visibility is the first obstacle. In a Cloud Security Alliance study, 56% of IT and security professionals said they have only partial visibility into where their data is stored, and 32% use 11 or more tools to manage unstructured data.
Source: Cloud Security Alliance, March 31, 2026
What are the most common unstructured data quality problems?
- Duplicates: the same files copied across shares, sites, clouds and AI staging areas. See Deduplication.
- Stale and superseded versions: old drafts that contradict current documents and confuse RAG retrieval.
- ROT data: redundant, obsolete and trivial files that add noise and cost.
- Orphaned data: files whose owner has left, with no one to vouch for accuracy.
- Missing or inconsistent metadata: files with no tags or business context, so they cannot be found or filtered.
- Sensitive content in the wrong place: PII or confidential data mixed into general shares.
- Noisy data: logs, temporary files and machine output that drown out the signal.
How do you improve the quality of unstructured data?
- Index metadata across every silo. Quality cannot be managed without visibility. Build one searchable index of file and object metadata across NAS, cloud and object storage.
- Profile the data. Query for duplicates, files untouched for a year or more, orphaned files, unsupported formats and sensitive content.
- Enrich metadata. Add tags for project, owner, sensitivity and business context, so files can be found, filtered and governed. See Metadata Enrichment.
- Detect sensitive data. Tag PII and confidential content before any data moves to AI. See Sensitive Data Detection.
- Ring-fence ROT. Move redundant, obsolete and trivial files out of active folders for owner review. The data owner makes the final decision on deletion.
- Curate by use case. Assemble only the current, relevant, approved files each AI or analytics use case needs. See AI Data Curation.
- Make it continuous. Run the same queries and policies on a schedule, because new data lands every day.
How does Komprise improve unstructured data quality?
Komprise improves data quality at the metadata layer, across every storage system, before data reaches AI:
- Global Metadatabase: one index of file and object metadata across NetApp, Dell PowerScale, Everpure, Windows file servers, cloud and object storage
- Deep Analytics: queries that find duplicates, stale and orphaned files and no-value file types across vendors and sites; the Potential Duplicates and Orphaned Data reports put a size and cost on them
- KAPPA data services: metadata enrichment that adds business context and tags, managed centrally in the Komprise tag library
- Smart Data Workflows: PII, keyword and regex detection that tags sensitive files and keeps them out of AI pipelines
- Confine: Deep Analytics can use a saved query as the input for a Confine plan that ring-fences ROT on the same share, secure and available for review
- Komprise Intelligent AI Ingest: delivers only curated, current data to AI services, with 2X faster data flows to AI
- Transparent File Tables: export curated file metadata to Apache Iceberg or Parquet so data teams query it in lakehouse tools
- Komprise Universal File MCP: filters ROT from AI assistant results and sends metadata first, so agents work with smaller, cleaner context
Unstructured Data Quality: Manual Approach vs Komprise
| Evaluation Criteria | Without Unstructured Data Management | With Komprise |
|---|---|---|
| Visibility | Quality checked one share or one array at a time | One Global Metadatabase across NetApp, Dell PowerScale, Everpure, Windows, cloud and object storage |
| Finding duplicates | Unknown copies spread across silos and AI staging areas | Potential Duplicates report and Deep Analytics queries across the whole estate |
| Stale and orphaned files | Old drafts and files from former employees mixed with current data | Queries by last access and owner; Orphaned Data report puts a size and cost on them |
| Metadata completeness | Files carry little more than a name and a date | KAPPA data services enrich files with business context; tags managed in one tag library |
| Sensitive content | PII discovered after it reaches an AI tool | Smart Data Workflows detect and tag PII before data moves |
| Handling low-quality data | Delete and hope, or leave it in place | Confine ring-fences ROT for owner review; the customer controls final deletion |
| Feeding AI | Whole shares copied, then cleaned, if at all | Komprise Intelligent AI Ingest sends only curated data, with 2X faster data flows to AI |
| AI token costs | Duplicates and outdated files inflate every call | Filter out ROT; send metadata first through Komprise Universal File MCP |
| Ongoing control | One-time cleanup projects that decay | Saved queries and workflows run on a schedule as data ages |
Read: AI First Mile GAP
Data Quality FAQs
What is data quality?
Data quality is how well data fits its intended use, measured by accuracy, completeness, consistency, timeliness, validity and uniqueness. For unstructured data, it also means knowing which file is the current version, how many copies exist, who owns it and whether it contains sensitive content.
What are the dimensions of data quality?
The six most widely used dimensions are accuracy, completeness, consistency, timeliness, validity and uniqueness. For AI use cases involving unstructured data, add appropriateness: whether a file contains PII or confidential content that should not reach a model.
How do you measure the quality of unstructured data?
Measure it through metadata. Count duplicate copies, the share of data not accessed in a year or more, files with no resolvable owner, files missing required tags, unsupported or corrupt formats and files containing PII. Tracking these figures by share and department over time shows whether quality is improving.
Why does data quality matter for AI and RAG?
AI retrieves and repeats what it is given. Outdated drafts, duplicates and conflicting versions lead to wrong answers and hallucinations, and every extra file costs tokens. In an October 2026 Komprise survey, only 28% of organizations on average had quantified ROI from AI, and nearly half named cleaning up low-quality, duplicate or redundant data as a top readiness gap.
How does poor data quality increase AI costs?
Models are billed by the token, so duplicates, stale files and irrelevant content raise the cost of every call and add to MCP bloat when agents pull back padded results. Filtering and enriching data before inference cuts AI compute and storage costs by 80% or more. One healthcare system that sent only relevant, recent files to the cloud cut cloud storage costs 96%.
Should you clean unstructured data before or after AI ingestion?
Before. Ingesting whole shares and cleaning afterward pays to copy, store, embed and index data that should never have been there, and lets poor-quality files influence results. Curate first with metadata, then send only the relevant set to the AI service.
What is the difference between data quality and data observability?
Data quality describes the condition of the data itself. Data observability is the continuous monitoring that detects when quality, freshness or volume changes. For unstructured data, continuous metadata indexing provides both: a current view of quality and an alert when it drifts.
How does data quality relate to ROT data and data minimization?
ROT data is one of the biggest sources of low-quality unstructured data. Data minimization keeps only the data each purpose needs. Ring-fencing ROT and curating what reaches AI improves quality and cuts storage and AI costs at the same time.
How does Komprise improve data quality across storage vendors?
Komprise indexes metadata from NetApp, Dell PowerScale, Everpure, Windows file servers, cloud and object storage in the Global Metadatabase. Deep Analytics finds duplicates, stale and orphaned files across all of them, KAPPA data services and Smart Data Workflows enrich and tag data, and Komprise Intelligent AI Ingest delivers only curated data to AI.
Which AI Data Platform layers does data quality map to?
Data quality spans layer 2, Metadata and Discovery, layer 3, Classification and Governance, and layer 4, Enrichment and Curation, of the AI Data Platform. These are the layers where Komprise operates.