What Is Data as a Service? Definition, Examples and Machine-Readable Data

1. What Is Data as a Service?

Data as a Service, commonly abbreviated as DaaS, is a model in which machine-readable data is made available to another organization or user as a product or service. Access may be provided through an API, a downloadable dataset, a cloud platform, a scheduled export, or a recurring data feed. The recipient may pay money, exchange data, or provide another form of value in return.¹

In a DaaS arrangement, the primary product is the data rather than only the software used to access or analyze it. Providers may collect, structure, update, aggregate, or distribute datasets such as financial prices, company information, weather observations, geographic records, or product catalogues.¹

DaaS is commonly delivered on a recurring or on-demand basis, although the exact commercial model can include subscriptions, usage-based access, scheduled datasets, or individual data purchases. What the arrangements share is that the data is transferred in a form the recipient's software can consume directly, rather than being locked inside an application built to display it.

2. What "Machine-Readable Data" Means

The core requirement of DaaS is that data can be consumed by software without a person manually retyping or reformatting it. The definition used across U.S. federal guidance, and recorded in the NIST Computer Security Resource Center glossary, describes machine-readable data as data in a format a computer can easily process without human intervention, while preserving its semantic meaning.²

Machine-readable is not the same as digital. A scanned PDF or a photograph of a table is a digital file, but software cannot reliably extract structured values from it without additional processing. Machine-readability requires a predictable structure that a parser can rely on.

Common machine-readable formats include:

• CSV — a plain-text, comma-separated table format, widely supported by spreadsheet and database tools.
• JSON — a lightweight, nested format commonly used by web APIs.
• JSONL (JSON Lines) — one JSON object per line, suited to streaming or very large datasets.
• XML — a tagged, hierarchical format often used in enterprise and government data exchange.
• Excel files (XLSX) — spreadsheet files holding structured tables, though less portable across systems than CSV.
• APIs — programmatic endpoints returning data on request, usually in JSON or XML.
• Database exports — dumps or snapshots taken directly from a database, such as SQL exports.

Because these formats follow a defined structure, software can query, filter, transform, and combine the data automatically. This is what makes the data usable at scale.

3. DaaS versus SaaS

DaaS is often introduced alongside Software as a Service (SaaS). NIST defines SaaS as a model in which the consumer uses the provider's applications running on a cloud infrastructure, without managing or controlling the underlying infrastructure or, in most cases, the application's own capabilities.³ The consumer's relationship is with the application.

The two models frequently overlap in practice. A SaaS product may be built on top of a proprietary dataset, and a DaaS provider may expose its data through a software interface. The distinction concerns what the customer is ultimately paying for: functionality, or the underlying information.

It is worth noting that NIST's cloud taxonomy formally recognises three service models — SaaS, PaaS and IaaS — and does not include DaaS among them.³ DaaS is an industry term rather than a standardised category.

4. Common Delivery Methods

DaaS providers deliver data through several mechanisms, often in combination:

• REST APIs — the most common method for real-time or on-demand access, allowing a recipient's software to request specific records as needed.
• Bulk CSV or JSON downloads — a full or partial dataset provided as a downloadable file, often refreshed on a schedule.
• Cloud storage — data placed in a shared cloud bucket or workspace that the recipient can pull from.
• Scheduled exports — automated, recurring deliveries via file transfer or email.
• Data feeds — continuous or near-continuous streams, common in financial market pricing.
• Browser-based exports — data retrieved directly from an application interface, sometimes under user authorization, rather than through a dedicated feed.

5. Examples of Data as a Service

DaaS spans a wide range of industries and data types. Broad categories include:

• Financial market data — pricing, indices, and trading information distributed to financial institutions and analytics platforms.
• Business and company information — registries, firmographic details, and company-level datasets used for research, compliance, or commercial analysis.
• Weather information — forecasts and historical observations supplied to logistics, agriculture, insurance, and consumer applications.
• Mapping and geolocation datasets — coordinates, boundaries, and routing data used by navigation and location-based services.
• Product catalogues — structured product data such as pricing, availability, and specifications, syndicated to marketplaces and comparison tools.
• User-authorized data exports — structured copies of contacts, messages, transactions, or activity records exported by users from services they already use.

6. Data Portability versus Data as a Service

The two concepts overlap but are not identical.

Data portability concerns a person's ability to retrieve their own data from a service and move it elsewhere. In the European Union this is established by Article 20 of the General Data Protection Regulation, which gives a data subject the right to receive personal data they provided to a controller in a structured, commonly used and machine-readable format, and to transmit it to another controller without hindrance.⁴ European Commission guidance confirms that the right applies where personal data was collected under a contract or on the basis of consent, and where it is processed by automated means.⁵ It does not extend to data the organization has inferred or derived.

Data as a Service, by contrast, describes data offered as a usable product or service to a third party. That third party is not necessarily the person the data describes, and the arrangement is not necessarily governed by any legal right of retrieval. A portability export could in principle feed a DaaS offering, but most DaaS arrangements involve data a provider has collected, cleaned, or aggregated specifically in order to license or sell it, independent of any individual's portability rights.

7. Risks and Limitations

Relying on external data introduces practical and legal considerations:

• Privacy — datasets containing personal information carry obligations under regimes such as the GDPR, regardless of who distributes them.
• Consent — data collected or exported without the knowledge or authorization of the people it describes raises legal and ethical concerns.
• Data ownership — legal ownership is often unclear, particularly for aggregated datasets. The OECD notes that data's replicability and imperfect excludability create a risk of loss of control, alongside risks to privacy and intellectual property.⁶
• Accuracy — data can be incomplete, outdated, or wrong, and downstream recipients usually have limited ability to verify it independently.
• Update frequency — infrequently refreshed feeds may not reflect current conditions, which matters more for pricing or weather than for static reference data.
• Platform terms — data sourced from a third-party platform remains subject to that platform's terms of service, which commonly restrict redistribution.
• Security — data in transit and at rest requires protection against unauthorized access, particularly where personal data is involved.
• Vendor lock-in — building processes around a single provider's schema or API raises the cost of switching later.

The OECD's data governance work frames these tensions directly: data's non-rivalrous nature favours openness, while its replicability creates control and privacy risks that require deliberate governance mechanisms.⁶

References

1. TechTarget, "Data as a Service (DaaS)".
2. National Institute of Standards and Technology, Computer Security Resource Center Glossary, "Machine-Readable", citing 44 U.S.C. § 3502.
3. Mell, P. and Grance, T. (2011), The NIST Definition of Cloud Computing, NIST Special Publication 800-145. doi.org/10.6028/NIST.SP.800-145
4. Regulation (EU) 2016/679 (General Data Protection Regulation), Article 20 — Right to data portability.
5. European Commission, "Can individuals ask to have their data transferred to another organisation?".
6. OECD, "Data governance".

Disclosure: Mastros develops browser-based data-export software. This article is intended as a general technical explanation and was prepared using the independent sources cited above.