OPEN DATA PRODUCT SPECIFICATION 2.0

Production version

The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and “OPTIONAL” in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.

The specification is shared under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license. Copyright is held by the creators of the specification: Jarkko Moilanen and Jussi Niilahti.

VERSION 2.0 - PUBLISHED 1st APRIL 2023

Version source:

https://github.com/Open-Data-Product-Initiative/open-data-product-spec-2.0

Editors:

Participate:

Introduction

The Open Data Product Specification is a vendor-neutral, open-source machine-readable data product metadata model. It defines the objects and attributes as well as the structure of digital data products. The work is based on existing standards (schema.org), best practices and emerging concepts like Data Mesh. The reasoning is that we reuse and proudly copy instead of reinventing the wheel. More detailed information of the origin can be found from the Open Data Product Specification homepage.

Open Data Product Specification 2.0 (ODPS) changes the data product metadata model towards a standalone model, which helps to decouple data product from the systems often directly associated with it. With help of the ODPS 2.0 data product can be presented and described to the customer also as such without any need for marketplace or other systems.

Development of the standard is coordinated in Open Data Product Initiative (ODPI) which was established in July 2022 to make it possible for the specification to grow and become institutionlized. The ODPI was taken under the wings of open source chapter of Open Collective.

odps-features

Specification aims and aspects

Specification aims:

enable interoperability between organizations, data platforms, marketplaces, and tools.
reduce data product metadata conversions and errors between systems and organizations,
increase the speed of designing, testing, and implementing data products.
speed up tools development around data product design, development and management.
enable creation of automated data product deployment with standard methods (DataOps)

Note! In the "Open Data Product" focus is on the latter words and the prefix 'open' refers to the openness of the standard. Any kind of connotations to open data (a different thing) are not intentional, intended, or desirable.

The specification has been designed with four major aspects of the data product in mind: 1) technical (infrastructure & access), 2) business (pricing & plans), 3) legal (licensing & IPR), and 4) ethical (privacy & mydata). The four aspects are described in 6 elements, which contain attributes and other elements.

odps-model

If you see something missing, described inaccurately or plain wrong, or you want to comment the specification, click the button below and proceed forward.

Document structure

LEFT COLUMN: Navigation

The left column is navigation which enables fluent and easy movement around the specification.

MIDDLE COLUMN: Principles and components

The middle column contains detailed information about the included components and related options. This is the theory part.

Note! Mandatory elements and attributes are listed separately in the definition tables. This enables user to construct minimum viable specification more easily and fast. https://schema.org provided ready-made definitions are applied when ever possible instead of re-inventing the wheel.

RIGHT COLUMN: Examples

The right column contains JSON formatted examples of how the specification is used. In the future other output formats are added on request basis.

Example of JSON formatted snippet from the Open Data Product specification:

"monitoring":{
  "url":"https://monitoring.com"
}

Document level attributes

Here's the list of attributes which can occur at the document root level. In the following description, if a field is not explicitly REQUIRED or described with a MUST or SHALL, it can be considered OPTIONAL. Optional attributes are listed in own table and examples are given on the right column.

Mandatory attributes

Example of document level attribute usage and structure:

"product":{
  "en":{
    "name":"Pets of the year",
    "productID":"123456are",
    "visibility":"private",
    "status":"draft",
    "type":"dataset"
   },
   "fi":{
    "name":"Vuoden suosituimmat lemmikit",
    "productID":"123456are",
    "visibility":"private",
    "status":"draft",
    "type":"dataset"
  }
}

Element name	Type	Options	Description
en	element	ISO 639-1 defined 2-letter codes	REQUIRED - NOTE! This is a dynamic element! This element binds together other product attributes and expresses the langugage used. In the example this is "en", which indicates that product details are in English. If you would like to use French details, then name the element "fr". The naming of this element follows options (language codes) listed in ISO 639-1 standard. You can have product details in multiple languages simply by adding similar sets like the example - just change the binding element name to matching language code. The pattern to implement multilanguage support for data products was adopted from de facto UI translation practices. The attributes inside this element are commonly rendered in the UI for the consumer and providing a simple way to implement that was the driving reasoning. See for example JSON - Multi Language
name	string	max length 256 chars	REQUIRED The name of the product.
productID	string	max length 256 chars	REQUIRED Product identifier.
visibility	one of	one of: private, organisation, public	REQUIRED The publicity level eg who can see this product. Private - just the creator. Organisation - visible to all in your organisation. Public - visible to all publicly.
status	one of	one of: announcement, draft, development, testing, acceptance, production, sunset, retired	REQUIRED The status of the product. Lifecycle model discussed in details in here (link).
type	one of	Options: raw data, derived data, dataset, reports, analytic view, 3D visualisation, algorithm, decision support, automated decision-making, data-enhanced product, data-driven service, data-enabled performance, bi-directional.	REQUIRED The type of the product. Options are derived from examples and lists found from academic literature.

Optional attributes

RecommendedDataProducts OBJECT contains an array of data products which offers means to attach related data products to the data product at hand. The source of the recommended data product might be from the same marketplace/catalog or an external one. Recommended object offers method to extend the reach and promotion escpecially when data product is treated as an independent entity much like described in Data Mesh. Also when data product is published in a marketplace, the Recommended object offers means to promote other than just the data products from the given data marketplace. In short, tis object is mainly for discovery and reach purposes.

RecommendedUseCases OBJECT is an array which contains offers method to attach usefull usecases to the data product. Usecases are informatiove for the the data customer and exemplify how the data product can create value.

Example of document level attribute usage and structure:

"product":{
  "en":{
  "name":"Pets of the year"
  "productID":"123456are",
  "valueProposition":"Design a customised petstore using a data product that describes pets with their habits, preferences and characteristics.",
  "description":"This is an example of a Petstore product.",
  "productSeries":"Lovely pets data products",
  "visibility":"private",
  "status":"draft",
  "version":"0.1",
  "versionNotes":"New version with additional details such more accurate pet details",
  "categories":["pets"],
  "standards":["ISO 24631-6"],
  "tags":["pet"],
  "brandSlogan":"Passion for the data monetization",
  "type":"dataset",
  "contentSample":"https://download.com/pets.json", 
  "logoURL":"https://data-product-business.github.io/open-data-product-spec/images/logo-dps-ebd5a97d.png",
  "OutputFileFormats":[ 
            "JSON",
            "XML",
            "CSV",
            "ZIP",
            "PDF"
         ],
  "useCases":[
         {
            "useCase":{
               "useCaseTitle":"Build attractive and lucrative petstore!",
               "useCaseDescription":"Use case description how succesfull petstore chain was established in Abu Dhabi",
               "useCaseURL":"https://marketplace.com/usecase1"
            }
         }
      ],
      "recommendedDataProducts":[
          "https://marketplace.com/dataproduct.json",
          "https://marketplace.com/dataproduct-another.json"
          ]
  }
}

Element name	Type	Options	Description
valueProposition	string	text content, max length 512 chars	This is the product's value proposition. Often one or two sentences and crystallizes the value for the customer.
description	string	-	The description of the product. Text only.
productSeries	string	-	A group of products in the product mix which are associated with each other and they can be obtained for the same type of customers or they are marketable for the same type of market place.
categories	array	-	Comma separates array of categories.
standards	array	-	Comma separates array of standards related e.g. to data content or quality, such as ISO 8000 or ISO 19131.
tags	array	-	Comma separates array of tags.
version	string	The versioning scheme is major.minor.. Examples: 1.0, 2.1, 3.15	The version of the data product. Applies for ODPS metadata as well.
versionNotes	string	-	Additional information about the version
contentSample	URL	Valid URL. See more from RFC 3986.	Sample content of the data product, for example JSON/XML output. This sample should match the actual data product output and give the data consumer an idea what to expect. Obviously if the data product is pure service for example dashboard or algorithm, then consider providing preview version or alike
logoURL	URL	Valid URL	Valid URL of the logo. See more from RFC 3986.
outputFileFormats	string	-	Output file formats for data product
brandSlogan	string	-	Brand related slogan like Nike has just do it.
useCases	element	array	Contains list of related use cases with description information and link to details. NOTE! These examples are expected to use same language as defined previously in the data product details content binding element.
useCaseTitle	string	string	Title of the usecase.
useCaseDescription	string	string	Brief description of the usecase.
useCaseURL	URL	Valid URL, RFC 3986	Valid URL of the more detailed usecase description.
recommendedDataProducts	array	Array of valid URLs (RFC 3986)	Data products to recommend use next to this data product or even as replacement (for comparison). The URL provided MUST reference a description of a data product following this same standard

Data Pricing Plans

Pricing is the process whereby a business sets the price at which it will sell its products and services. Pricing OBJECT consists of mandatory and optional attributes. This element contains pricing plans related data to be used for example in displaying the items in a marketplace. If needed the standard metadata is converted to marketplace internal format. We encourage all data product owners to enforce usage of this standard.

Mandatory attributes are listed in separate table and marked with bolded names and asterix *. Next to the mandatory attributes is an example.

The same logic applies to the optional attributes as well. Optional attributes are listed in own table and an example is given in the right column.

Supported pricing models include:

recurring time period based (day, week, month, year) plans
one time payments plans
pay-as-you-go plans
revenue sharing plans
data volume plan
dynamic pricing (high and low limits for automated pricing)
Pay what you want plans
Freemium
Open data

Mandatory attributes and elements

Example of Pricing component usage with manadatory elements and attributes. Example language is english.:


 "pricingPlans": {
    "en": [
        {
            "name": "Premium subscription 1 year",
            "priceCurrency": "EUR",
            "price": "50.00",
            "billingDuration": "year",
            "unit": "recurring",
            "maxTransactionQuantity": "unlimited"
        },
        {
            "name": "Premium Package Monthly",
            "priceCurrency": "EUR",
            "price": "5.00",
            "billingDuration": "month",
            "unit": "recurring",
            "maxTransactionQuantity": 10000
        },
        {
            "name": "Freemium Package",
            "priceCurrency": "EUR",
            "price": "0.00",
            "billingDuration": "month",
            "unit": "recurring",
            "maxTransactionQuantity": 1000
        },
        {
            "name": "Revenue sharing",
            "priceCurrency": "percentage",
            "price": "5.50",
            "billingDuration": "month",
            "unit": "revenue-sharing",
            "maxTransactionQuantity": 20000
        }
    ]
}

Element name	Type	Options	Description
en	element	ISO 639-1 defined 2-letter codes	REQUIRED - NOTE! This is a dynamic element! This element binds together other product pricing plan attributes and expresses the langugage used. In the example this is "en", which indicates that pricing plan details are in English. If you would like to use French details, then name the element "fr". The naming of this element follows options (language codes) listed in ISO 639-1 standard. You can have product pricing plan details in multiple languages simply by adding similar sets like the example - just change the binding element name to matching language code. The pattern to implement multilanguage support for data products was adopted from de facto UI translation practices. The attributes inside this element are commonly rendered in the UI for the consumer and providing a simple way to implement that was the driving reasoning. See for example JSON - Multi Language
priceCurrency	string	Use standard formats: ISO 4217 currency format e.g. "USD"; Ticker symbol for cryptocurrencies e.g. "BTC"	REQUIRED The primary currency used in pricing. Platforms are assumed to use this as primary currency if currency conversions are used to display product pricing in different locations for various currencies. If the unit is revenue-sharing, then this attribute value MUST be percentage.
price	string	-	REQUIRED The offer price of a product, or of a price component, or revenue-sharing percentage. If the unit of pricing is revenue-sharing, then this price attribute value is percentage value. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols. With data-volume the price is for each 1GB of data.
billingDuration	string	options: instant, day, week, month, year	REQUIRED Specifies for how long this price (or price component) will be billed. Can be used, for example, to model the contractual duration of a subscription or payment plan.
unit	string	One of: one-time-payment, pay-per-use, recurring, revenue-sharing, data-volume , pay-what-you-want, freemium, open-data	REQUIRED One-time-payment is for single time purchase purposes, further purchaces are not intended to continue under same agreement. Pay-per-use is intended for continuous usage and price set is for each successful usage action. Recurrring is intended for continuous time period plans. Revenue sharing is a performance-based income model. An effective revenue sharing deal structure is offering your expertise to a business owner to help them grow their business. In return, you get paid a percentage of the revenue as a royalty fee. Freemium is for free access. Use this option also for open data. Data-volume is for data amount based pricing in which customer pays based on the served data amount. The price is always for 1GB of data. pay-what-you-want is a pricing system where buyers pay any desired amount for a given commodity, sometimes including zero. In some cases, a minimum (floor) price may be set, and/or a suggested price may be indicated as guidance for the buyer. The buyer can also select an amount higher than the standard price for the commodity. If the floor price is set, use minPrice attribute. open-data is an explicit pricing plan category for open data.
maxTransactionQuantity	Integer	Integer	REQUIRED The maximum transaction quantity for the given billing duration. Use this to define for example monthly (or any other period) request limit to the data product. Note! If you want to set unlimited use, value must be 0 (zero).
offering	string	array	REQUIRED The element that contains pricing plan content as array of strings. Think of this as the list of what is included in the pricing plan and what you offer in return to the price asked. Use the language defined in the plan

Optional attributes and elements

Example of Pricing component usage with some of the optional elements and attributes:

   "pricingPlans": {
      "en": [
         {
            "name": "Premium subscription 1 year",
            "priceCurrency": "EUR",
            "price": "10.00",
            "minPrice": "5.00",
            "maxPrice": "15.000",
            "additionalPrice": 0.02
         },
         {
            "name": "Premium Package",
            "priceCurrency": "EUR",
            "price": "10.00",
            "maxPrice": "20.00",
            "valueAddedTaxIncluded": false
         }
      ]
   }

Element name	Type	Options	Description
minPrice	string	-	The lowest price if the price is a range. If dynamic pricing is used with this product, this is the lowest price allowed. In dynamic pricing businesses are able to change prices based on algorithms that take into account competitor pricing, supply and demand, and other external factors in the market. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols.
maxPrice	string	-	The highest price if the price is a range. If dynamic pricing is used with this product, this is the highest price allowed. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols.
valueAddedTaxIncluded	boolean	true/false	Specifies whether the applicable value-added tax (VAT) is included in the price specification or not.
valueAddedTaxPercentage	Integer	Number percentage value, range 0-100	Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols.
validFrom	DateTime	A combination of date and time in ISO 8601 format yyyy-MM-dd'T'HH:mm:ss.SSSZ.	The date when the item becomes valid.
validTo	DateTime	A combination of date and time in ISO 8601 format yyyy-MM-dd'T'HH:mm:ss.SSSZ.	The date after when the item is not valid.
additionalPrice	string	-	This is used to define fees for usage which exceeds the defined max transaction quantity. This value is for each additional transaction. Use '.' (Unicode 'FULL STOP' (U+002E)) rather than ',' to indicate a decimal point. Avoid using these symbols as a readability separator. Use values from 0123456789 (Unicode 'DIGIT ZERO' (U+0030) to 'DIGIT NINE' (U+0039)) rather than superficially similiar Unicode symbols.
maxDataQuantity	Integer	-	The maximum amount of data transferred during the billing duration. Unit is GB.

DataOps

DataOps is a process whereby a data product pipeline deployment method is defined. Usually the deployment script contains the logic of the individual steps as well as the code chaining the steps together.

DataOps OBJECT describes building, deploying, and running data product's code, and storing and giving access to data and metadata. This principle has been adopted from the Data Mesh.

Example of DataOps component usage:


"dataOps": {
  "infrastructure":{
    "platform":"Azure",
    "storageTechnology":"Azure SQL",
    "storageType":"sql",
    "containerTool":"helm",
    "format":"yaml",
    "status":"development",
    "schemaLocationURL":"http://http://192.168.10.1/schemas/2016/petshopML-2.3/schema/petstore.xsd",
    "scriptURL":"http://192.168.10.1/rundatapipeline.yml",
    "deploymentDocumentationURL":"http://192.168.10.1/datapipeline",
    "dataLineageTool":"Collibra",
    "dataLineageOutput":"http://192.168.10.1/lineage.json",
    "hashType":"SHA-2",
    "checksum":"7b7444ab8f5832e9ae8f54834782af995d0a83b4a1d77a75833eda7e19b4c921"
  }
}

Element name	Type	Options	Description
infrastructure	element	-	Infrastructure is a process whereby a data product pipeline deployment method is defined.
platform	string	any	Platform infrastructure, such as AWS, GCP, Azure.
storageTechnology	string	any	Describes the internal storage area technology, such as Amazon S3, Google Cloud Storage, Azure Blob Storage, Azure SQL.
storageType	string	any	Describes the internal storage type, such as files, sql, events, MQTT.
containerTool	string	any	A name of the package manager, container or infrastructure as code tool.
format	string	any	Type of script language.
status	string	Options: announcement, draft, development, testing, acceptance, production, sunset, retired.	Development status.
schemaLocationURL	URL	Valid URL	The URL of the data product schema, such as XSD, XML or JSON Schema.
scriptURL	URL	Valid URL	The URL of the deployment script. Script can be used for implementing the data product. In a Data Mesh -model it can be used to define, for example, one or more outputs which take the data from source systems or other data products.
deploymentDocumentationURL	URL	Valid URL	The URL of the deployment documentation.
datalineageTool	URL	Valid URL	A tool to view the data lineage.
datalineageOutput	URL	Valid URL	The URL of the data lineage output. Data lineage output shows the mapping of source data to target output on a metadata level
hashType	string	One of: SHA-1, SHA-2, SHA-3	Type of secure hash algorithm for checksum.
checksum	string	any	Script checksum.

Data Access

Data Access OBJECT describes the authorised ability to retrieve, edit, copy or transfer data from IT systems.

Example of Data Access component usage:


 "dataAccess":{
  "interface"{
    "outputPorttype":"API",
    "authenticationMethod":"OAuth",
    "specification":"OAS",
    "format":"GraphQL",
    "specsURL":"http://192.168.10.1/petshop.json",
    "documentationURL":"http://192.168.10.1/petshop"
  }
}

Element name	Type	Options	Description
interface	element	-	Reference to the ability to use data.
outputPorttype	string	any	Type of data access, such as API, SQL, sFTP, gRPC.
hashType	string	any	Type of secure hash algorithm, such as SHA-1, SHA-2, for checksum, when output is file(s).
checksum	string	any	File checksum.
authenticationMethod	string	any	Data access authentication method type, such as API key, HTTP Basic, OAuth, No authentication.
specification	string	any	Type of the data access specification, such as OAS, RAML, Slate.
format	string	any	Data access file format type, such as JSON, XML, GraphQL, plain text.
specsURL	URL	Valid URL	The URL of the data access documentation, preferably in a machine-readable format, such as OpenAPI specs.
documentationURL	URL	Valid URL	The URL of the separated data access documentation or guide. For example, it may contain instructions on how to create and manage api keys.

Data SLA

Data Service Level Agreement (SLA) Object** contains attributes which define the desired and promised quality of the data product.

No mandatory attributes at the moment. Optional attributes are listed in own table and an example is given in the right column.

Optional attributes and elements

Example of Quality component usage:

   "SLA": {
      "updateFrequency": 
      {
         "unit":"hours",
         "value":1
      },
      "uptime": 
      {
         "unit":"percentage",
         "value":99
      },
      "responseTime": 
      {
         "unit":"milliseconds",
         "value":200
      },
      "support": 
      {
         "company": 
         {
            "phoneNumber":"",
            "phoneServiceHours":""
            "chatURL":"",
            "chatServiceHours":"",
            "chatResponseTime":"",
            "email":"support@support.org",
            "emailServiceHours":"",
            "emailResponseTime":"",
            "documentationURL":"",
            "guidesURL":"",
         },
         "community": 
         {
            "stackoverflowURL":"",
            "forumURL":"" 
            "slackURL":"",
            "twitterURL":""
         }
      }
      "observability":
      {
         "logsURL":"https://logs.com"
         "dashboardURL":"https://dashboard.com",
         "uptimeURL":"https://uptime.com"
      } 
   }

Element name	Type	Options	Description
updateFrequency	element	Options for unit are: milliseconds, seconds, minutes, days, weeks, months, years, never, null. Value attribute is Integer.	Name of the quality attribute indicating the timely interval how often data is updated.
uptime	element	Options for unit are: percentage, string, null. The value attribute can be integer or string "best effort".	Uptime is the amount of time that a service is online available and operational. Guaranteed uptime is expressed as SLA level and is generally the most important metric to measure the quality of a hosting provider. An SLA level of 99.99% for example equates to 52 minutes and 36 seconds of downtime per year. in this context uptime is SLA.
responseTime	element	Unit options are: milliseconds, seconds, null. Value can be integer or null	Response time is the total amount of time it takes to respond to a request for service.
support	element	-	Support element describes how the customer can reach for help in case of difficulties in usage, billing, or otherwise. Support can be based on company provided support and community driven support.
phoneNumber	string	-	The support phone number
phoneServiceHours	string	-	Describes the service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm.
chatURL	URL	Valid URL	The URL of chat service to use. Service hours and response time defined in other attributes.
chatServiceHours	string	-	Describes the chat service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm.
chatResponseTime	string	-	Describes aimed maximum delay in responding to chat support requests. This doesn't normally guarantee a resolution to the problem.
email	string	-	Email information for support requests.
emailServiceHours	string	-	Describes the email service hours company provides. Contains information often in week level eg Mon-Fri at 8am - 4pm.
emailResponseTime	string	-	Describes aimed maximum delay in responding to email support requests. This doesn't normally guarantee a resolution to the problem.
documentationURL	URL	-	URL to the documentation of the product.
guidesURL	URL	Valid URL	URL to the guides offering more information and examples about how to use the data product. Guides might be platform specific.
community	Element	-	Element that contains community based support function information.
stackoverflowURL	URL	Valid URL	URL to the Stack Overflow. Could be for example list of resolved issues related to the product.
forumURL	URL	Valid URL	URL to the community forum in which product related support requests can be raised.
slackURL	URL	Valid URL	URL to the Slack workspace in which product related support requests can be raised.
twitterURL	URL	Valid URL	URL to the Twitter account for which product related support requests can be raised.
observability	element	-	Observability is a superset of monitoring. It provides not only high-level overviews of the system’s health but also highly granular insights into the implicit failure modes of the system. In addition, an observable system furnishes ample context about its inner workings, unlocking the ability to uncover deeper, systemic issues.
logsURL	URL	Valid URL	URL to service which offers access to event logs including errors, response times, call information.
dashboardURL	URL	Valid URL	URL to dashboard application which visualizes product behaviour. This service should support at least part of the given product quality indicators.
uptimeURL	URL	Valid URL	URL to service which shows uptime statistics as well as other statistical information. This service should support at least part of the given product quality indicators.

Data Quality

Data quality is essential for one main reason: You give customers the best experience when you make decisions using accurate data. A great customer experience leads to happy customers, brand loyalty, and higher revenue for your business. Information is only valuable if it is of high quality. How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.

The values of the QA attributes are given by the vendor. Should you trust in the values, is the choice made by the data consumer. If possbile utilize automatic checking of data quality against the source and update the values accordingly.

The QA object is general in nature and should be enough for common (80%) of the use cases. Note that you can make extensions to the standard with "x-" mechanism in order to fulfill any industry specific needs. The "Specification extensions" section provides details on how to use this feature.

Data integrity is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle. That is why integrity is not in the attributes, but accuracy and consistency as well as completeness are.

Optional attributes and elements

Example of Data Quality component with some of the voluntary attributes:

   "dataQuality":
      {
         "accuracy":100,
         "completeness":100,
         "consistency":100,
         "timeliness":"high",
         "validity":100,
         "uniqueness":100,
         "dataQualityAssuranceMethods":"Data quality assurance suite of tools and methods include both data quality auditing (DQA) tools designed for use by external audit teams and routine data quality assessment (RDQA) tools designed for capacity building and self-assessment."
      }

Element name	Type	Options	Description
accuracy	integer	percentage	The term “accuracy” refers to the degree to which information accurately reflects an event or object described. For example, if a customer’s age is 32, but the system says she’s 34, that information is inaccurate.
completeness	integer	percentage	Data is considered “complete” when it fulfills expectations of comprehensiveness. Let’s say that you ask the customer to supply his or her name. You might make a customer’s middle name optional, but as long as you have the first and last name, the data is complete.
consistency	integer	percentage	At many companies, the same information may be stored in more than one place. If that information matches, it’s considered “consistent.” For example, if your human resources information systems say an employee doesn’t work there anymore, yet your payroll says he’s still receiving a check, that’s inconsistent.
timeliness	string	one of: low, medium, high	Is your information available right when it’s needed? That data quality dimension is called “timeliness.” Let’s say that you need financial information every quarter; if the data is ready when it’s supposed to be, it’s timely. The data quality dimension of timeliness is a user expectation.
validity	integer	percentage	Validity is a data quality dimension that refers to information that doesn’t conform to a specific format or doesn’t follow business rules. A popular example is birthdays – many systems ask you to enter your birthday in a specific format, and if you don’t, it’s invalid. To meet this data quality dimension, you must check if all of your information follows a specific format or business rules.
uniqueness	integer	percentage	“Unique” information means that there’s only one instance of it appearing in a database. As we know, data duplication is a frequent occurrence. “Daniel A. Robertson” and “Dan A. Robertson” may well be the same person. Meeting this data quality dimension involves reviewing your information to ensure that none of it is duplicated.
dataQualityAssuranceMethods	string	text content, max length 512 chars	Description of the data product quality assurance methods and tools used.

Data Licensing

The data product may be exploited e.g. by licensing its use and exploitation to third parties. Machine-readable license as part of the specification is implemented for this purpose. It can be used to conclude various agreements regarding data protection, processing and intellectual property rights (IPR). Data can be protected by one or more intellectual property rights. Principle is that when a third party (Data User) exploits the data, it must have a license or other right from Data Holder to exploit to the data.

Example of License Object usage:

    "license":{
        "scope":{
            "definition":"The purpose of this license is to determine the terms and conditions applicable to the licensing of the data product, whereby Data Holder grants Data User the right to use the data.",
            "language":"en-us",
            "restrictions":"Data User agrees not to, directly or indirectly, participate in the unauthorized use, disclosure or conversion of any confidential information.",
            "geographicalArea":[
                "EU",
                "US"
            ],
            "permanent":false,
            "exclusive":false,
            "rights":[
                "Reproduction",
                "Display",
                "Distribution",
                "Adaptation",
                "Reselling",
                "Sublicensing",
                "Transferring"
            ]
        },
        "privacy":{
            "containsPersonalData":true,
            "applicaplePrivacyLaws":[
                "General Data Protection Regulation",
                "Personal Information Protection and Electronic Documents Act (PIPEDA)",
                "California Consumer Privacy Act (CCPA)"
            ],
            "dpaURL":"http://192.168.10.1/dpaconditions"
        },
        "termination":{
            "terminationConditions":"Cancellation before 30 days. After the expiry of the right of use, the product and its derivatives must be removed.",
            "continuityConditions":"Expired license will automatically continued without written cancellation (termination) by Data Holder"
        },
        "governance":{
            "damages":"During the term of license, except for the force majeure or the Data Holders reasons, Data User is required to follow strictly in accordance with the license. If Data User wants to terminate the license early, it needs to pay a certain amount of liquidated damages.",
            "confidentiality":"Data User undertakes to maintain confidentiality as regards all information of a technical (such as, by way of a non-limiting example, drawings, tables, documentation, formulas and correspondence) and commercial nature (including contractual conditions, prices, payment conditions) gained during the performance of this license.",
            "applicableLaws":"This license shall be interpreted, construed and enforced in accordance with the law of Finland, Incl. Copyright Act 404/1961.",
            "warranties":"Data Holder makes no warranties, express or implied, guarantees or conditions with respect to your use of the data product. To the extent permitted under local law, Data Holder disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from Data User use of the data product.",
            "audit":"Data Holder will reasonably cooperate with Data User by providing available additional information concerning the data product. Each party will bear its own costs with respect to the audit procedures.",
            "forceMajeure":"Each party may suspend the fulfilment of its contractual obligations, when the said fulfilment is impossible or objectively too costly due to an unforeseeable impediment independent from the parties, such as for example: strike, boycott, lockout, fire, war (declared or not), civil war, riots and revolutions, requisitions, embargo, power blackouts, extraordinary breakage of machinery, delays in the delivery of components or raw materials."
        }
    }

Element name	Type	Options	Description
scope	element	-	Extent, range, coverage, area or space of the license.
definition	string	text content, max length 512 chars	Background and purpose of the license.
language	string	ISO 639-1 standard language codes	License language.
restrictions	string	text content, max length 512 chars	Restrictions of the license.
geographicalArea	string	ISO 3166-1 alpha-2 codes	License right restricted to the geographical area.
permanent	boolean	true/false	License with no expiration date.
exclusive	boolean	true/false	The exclusive license holder is given complete control over the use of the data product, and no other person or organization is allowed to use it during the term of the license agreement.
rights	array	Options: Reproduction (rights to reproduce), Display (disclose data to others), Distribution (right to distribute), Adaptation (right for derivate work), Reselling (right to resell), Transferring (transferable data license), Sublicensing (license grant may include a right to sublicense)	Rights granted by the licence. The texts in brackets and italic are intended to describe rights.
privacy	element	-	Data privacy related attributes.
containsPersonalData	boolean	true/false	Data contains personal data.
dpaURL	URL	valid URL	The URL of the Data Processing Agreement (DPA).
termination	element	-	Licence termination and continuity related condititions.
terminationConditions	string	text content, max length 512 chars	Cancellation conditions of the license.
continuityConditions	string	text content, max length 512 chars	Continuity conditions of the license.
governance	element	-	Governance is the approach taken to ensure that the agreed outcomes are being fulfilled.
audit	string	text content, max length 512 chars	License auditing terms.
warranties	string	text content, max length 512 chars	License warranties.
damages	string	text content, max length 512 chars	Damages refers to the sum of money (i.e. indemnifications) for a breach of some duty or violation of license right.
confidentiality	string	text content, max length 512 chars	Restrictions and requirements imposed on the Data User regarding e.g. the use and disclosure of the Data Holder's confidential information.
applicableLaws	string	text content, max length 512 chars	Applicable laws not covered in applicaplePrivacyLaws, i.e local acts, degrees or law.
forceMajeure	string	text content, max length 512 chars	Force Majeure

Data Holder

DataHolder Object describes the Organization legally allowed to create, develop and publish data products.

Data holder means "a legal person, public body, international organisation, or a natural person who is not a data subject with respect to the specific data in question, which, in accordance with applicable Union or national law, has the right to grant access to or to share certain personal data or non-personal data." (Data Governance Act)

The data holder might not be the original IPR owner of the data used, but has rights operate with it. The contract or other agreement between Provider and possible data owner is not part of the standard as metadata or licence wise.

Mandatory attributes are listed in separate table and marked with REQUIRED text. Next to the mandatory attributes is an example.

The same logic applies to the optional attributes as well. Optional attributes are listed in own table and an example is given in the right column.

Mandatory attributes and elements

Example of Provider component mandatory attributes usage:

   "dataHolder":
      {
         "legalName":"MindMote Oy",
         "businessId":"12243434-12",
         "email":"contact@mindmote.fi"
      }

Element name	Type	Options	Description
legalName	string	text content, max length 256 chars	REQUIRED The official name of the organization, e.g. the registered company name.
businessID	string	As defined in RFC 5322	REQUIRED The business identifier code of the company. Often this is given to the company by authorized public sector organization managing register of businesses.
email	string	-	REQUIRED Email to be used in contacting the organization.

Optional attributes and elements

Example of Provider component with some of the voluntary attributes:

   "dataHolder":
      {
         "taxID":"12243434-12",
         "vatID":"12243434-12",
         "businessDomain":"Data Product Business",
         "logoURL":"https://mindmote.fi/logo.png",
         "description":"Digital Economy services and tools",
         "URL":"https://mindmote.fi",
         "telephone":"+35845 0232 2323",
         "streetAddress":"Koulukatu 1",
         "postalCode":"33100",
         "addressRegion":"Pirkanmaa",
         "addressLocality":"Tampere",
         "addressCountry":"Finland",
         "aggregateRating":"",
         "ratingCount":1245,
         "slogan":"",
         "parentOrganization":""
      }

Element name	Type	Options	Description
taxID	string	-	The Tax / Fiscal ID of the organization or person, e.g. the TIN in the US or the CIF/NIF in Spain.
vatID	string	-	The Value-added Tax ID of the organization or person.
businessDomain	string	-	In a data mesh architecture, data (or data product) ownership and management are distributed across self-contained business domains.
logoURL	URL	Valid URL. See more from RFC 3986.	The URL pointing to organisation logo.
description	string	Max length 512 chars	The introduction to the organization. Often contains information of what the organisation does and focuses on.
URL	URL	Valid URL. See more from RFC 3986.	The URL of the organization's website.
telephone	string	-	The telephone number.
streetAddress	string	-	The street address. For example, 1600 Amphitheatre Pkwy.
postalCode	string	-	The postal code. For example, 94043.
addressRegion	string	-	The region in which the locality is, and which is in the country. For example, California or another appropriate first-level Administrative division
addressLocality	string	-	The locality in which the street address is, and which is in the region. For example, Mountain View.
addressCountry	string	two-letter ISO 3166-1 alpha-2 country code	The country.
aggregateRating	string	-	The average rating based on multiple ratings or reviews.
ratingCount	integer	-	The amount of ratigns and reviews used in calculating the aggregateRating.
slogan	string	Max length 256 chars	The slogan of the organization. This is often related to showing the brand
parentOrganization	string	-	The larger organization that this organization is a subOrganization of, if any.

Specification extensions

While the Open Data Product Specification tries to accommodate most use cases, additional data can be added to extend the specification at certain points.

The extensions properties are implemented as patterned fields that are always prefixed by "x-". The extensions may or may not be supported by the available tooling, but those may be extended as well to add requested support (if tools are internal or open-sourced). Open Data Product Initiative Technical Steering Committee does not officially approve external extensions - they are fully independent. Popular extensions however are natural candidates for future additions of the standard.

Example of extension usage:

   "product": {
      "name":"Pets of the year",
      "productID":"123456are",
      "description":"",
      "x-internal-id":"foobar123"
}

Element name	Type	Options	Description
^x-	any		Allows extensions to the Open Data Product Schema. The field name MUST begin with x-, for example, x-internal-id. The value can be null, a primitive, an array or an object. Can have any valid JSON format value.

Hello world example

You'll find a complete machine-readbale example of a data product from the right column. It is imaginary data product Pets of the year which contains derived data about the most common pets in the world. The product has 4 pricing plans which are mostly based on recurring subscription model. Note! Not all voluntary attributes are used in the example and multilingualism has not been fully applied.

Example of complete working Data Product specification instance:

{
   "product":{
      "en":{
         "name":"Pets of the year",
         "productID":"123456are",
         "valueProposition":"Design a customised petstore using a data product that describes pets with their habits, preferences and characteristics.",
         "description":"This is an example of a Petstore product.",
         "productSeries":"Lovely pets data products",
         "visibility":"private",
         "status":"draft",
         "version":"0.1",
         "categories":[
            "pets"
         ],
         "standards":[
            "ISO 24631-6"
         ],
         "tags":[
            "pet"
         ],
         "brandSlogan":"Passion for the data monetization",
         "type":"derived data",
         "logoURL":"https://data-product-business.github.io/open-data-product-spec/images/logo-dps-ebd5a97d.png",
         "OutputFileFormats":[
            "json",
            "xml",
            "csv",
            "zip"
         ],
         "useCases":[
            {
               "useCase":{
                  "useCaseTitle":"Build attractive and lucrative petstore!",
                  "useCaseDescription":"Use case description how succesfull petstore chain was established in Abu Dhabi",
                  "useCaseURL":"https://marketplace.com/usecase1"
               }
            }
         ]
      },
      "recommendedDataProducts":[
          "https://marketplace.com/dataproduct.json, https://marketplace.com/dataproduct-another.json"
          ],      
      "pricingPlans":{
         "en":[
            {
               "name":"Premium subscription 1 year",
               "priceCurrency":"EUR",
               "price":"50.00",
               "billingDuration":"year",
               "unit":"recurring",
               "maxTransactionQuantity":"unlimited"
            },
            {
               "name":"Premium Package Monthly",
               "priceCurrency":"EUR",
               "price":"5.00",
               "billingDuration":"month",
               "unit":"recurring",
               "maxTransactionQuantity":10000
            },
            {
               "name":"Freemium Package",
               "priceCurrency":"EUR",
               "price":"0.00",
               "billingDuration":"month",
               "unit":"recurring",
               "maxTransactionQuantity":1000
            },
            {
               "name":"Revenue sharing",
               "priceCurrency":"percentage",
               "price":"5.50",
               "billingDuration":"month",
               "unit":"revenue-sharing",
               "maxTransactionQuantity":20000
            }
         ]
      },
      "dataOps":{
         "infrastructure":{
            "platform":"Azure",
            "storageTechnology":"Azure SQL",
            "storageType":"sql",
            "containerTool":"helm",
            "format":"yaml",
            "status":"development",
            "schemaLocationURL":"http://http://192.168.10.1/schemas/2016/petshopML-2.3/schema/petstore.xsd",
            "scriptURL":"http://192.168.10.1/rundatapipeline.yml",
            "deploymentDocumentationURL":"http://192.168.10.1/datapipeline",
            "dataLineageTool":"Collibra",
            "dataLineageOutput":"http://192.168.10.1/lineage.json",
            "hashType":"SHA-2",
            "checksum":"7b7444ab8f5832e9ae8f54834782af995d0a83b4a1d77a75833eda7e19b4c921"
         }
      },
      "dataAccess":{
         "type":"API",
         "authenticationMethod":"OAuth",
         "specification":"OAS",
         "format":"JSON",
         "documentationURL":"https://swagger.com/petstore.json"
      },
      "dataQuality":{
         "accuracy":100,
         "completeness":100,
         "consistency":100,
         "timeliness":"high",
         "validity":100,
         "uniqueness":100,
         "dataQualityAssuranceMethods":"Data quality assurance suite of tools and methods include both data quality auditing (DQA) tools designed for use by external audit teams and routine data quality assessment (RDQA) tools designed for capacity building and self-assessment."
      },
      "SLA":{
         "updateFrequency":{
            "unit":"hours",
            "value":1
         },
         "uptime":{
            "unit":"percentage",
            "value":99
         },
         "responseTime":{
            "unit":"milliseconds",
            "value":200
         },
         "nullValues":{
            "unit":"percentage",
            "value":0.01
         },
         "support":{
            "company":{
               "phoneNumber":"",
               "phoneServiceHours":"",
               "chatURL":"",
               "chatServiceHours":"",
               "chatResponseTime":"",
               "email":"",
               "emailServiceHours":"",
               "emailResponseTime":"",
               "documentationURL":"",
               "guidesURL":""
            },
            "community":{
               "stackoverflowURL":"",
               "forumURL":"",
               "slackURL":"",
               "twitterURL":""
            }
         },
         "observability":{
            "logsURL":"https://logs.com",
            "dashboardURL":"https://dashboard.com",
            "uptimeURL":"https://uptime.com"
         }
      },
      "license":{
         "scope":{
            "definition":"The purpose of this license is to determine the terms and conditions applicable to the licensing of the data product, whereby Data Holder grants Data User the right to use the data.",
            "language":"en-us",
            "restrictions":"Data User agrees not to, directly or indirectly, participate in the unauthorized use, disclosure or conversion of any confidential information.",
            "geographicalArea":[
               "EU",
               "US"
            ],
            "permanent":false,
            "exclusive":false,
            "rights":[
               "Reproduction",
               "Display",
               "Distribution",
               "Adaptation",
               "Reselling",
               "Sublicensing",
               "Transferring"
            ]
         },
         "privacy":{
            "containsPersonalData":true,
            "applicaplePrivacyLaws":[
               "General Data Protection Regulation",
               "Personal Information Protection and Electronic Documents Act (PIPEDA)",
               "California Consumer Privacy Act (CCPA)"
            ],
            "dpaURL":"http://192.168.10.1/dpaconditions"
         },
         "termination":{
            "terminationConditions":"Cancellation before 30 days. After the expiry of the right of use, the product and its derivatives must be removed.",
            "continuityConditions":"Expired license will automatically continued without written cancellation (termination) by Data Holder"
         },
         "governance":{
            "damages":"During the term of license, except for the force majeure or the Data Holders reasons, Data User is required to follow strictly in accordance with the license. If Data User wants to terminate the license early, it needs to pay a certain amount of liquidated damages.",
            "confidentiality":"Data User undertakes to maintain confidentiality as regards all information of a technical (such as, by way of a non-limiting example, drawings, tables, documentation, formulas and correspondence) and commercial nature (including contractual conditions, prices, payment conditions) gained during the performance of this license.",
            "applicableLaws":"This license shall be interpreted, construed and enforced in accordance with the law of Finland, Incl. Copyright Act 404/1961.",
            "warranties":"Data Holder makes no warranties, express or implied, guarantees or conditions with respect to your use of the data product. To the extent permitted under local law, Data Holder disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from Data User use of the data product.",
            "audit":"Data Holder will reasonably cooperate with Data User by providing available additional information concerning the data product. Each party will bear its own costs with respect to the audit procedures.",
            "forceMajeure":"Each party may suspend the fulfilment of its contractual obligations, when the said fulfilment is impossible or objectively too costly due to an unforeseeable impediment independent from the parties, such as for example: strike, boycott, lockout, fire, war (declared or not), civil war, riots and revolutions, requisitions, embargo, power blackouts, extraordinary breakage of machinery, delays in the delivery of components or raw materials."
         }
      },
      "dataHolder":{
         "taxID":"12243434-12",
         "vatID":"12243434-12",
         "businessDomain":"Data Product Business",
         "logoURL":"https://mindmote.fi/logo.png",
         "description":"Digital Economy services and tools",
         "URL":"https://mindmote.fi",
         "telephone":"+358 45 232 2323",
         "streetAddress":"Koulukatu 1",
         "postalCode":"33100",
         "addressRegion":" Pirkanmaa",
         "addressLocality":"Tampere",
         "addressCountry":"Finland",
         "aggregateRating":"",
         "ratingCount":1245,
         "slogan":"",
         "parentOrganization":""
      }
   }
}

Terms used

Here's list of terms used and what we mean with them. The meaning of terms is mostly taken from existing knowledge eg articles and other trusted sources. The source is always linked to the term. In some rare cases term is defined for the specification purposes only.

Term	Description
Data point
Data product	As a strategic resource for companies, data is considered an asset that, like any other material good, has a financial value and whose management generates costs. Data created, collected or used in individual business processes can be sold to other organisations as raw or processed data, so that it no longer serves as an enabler of products, but is the product itself. This leads to the paradigm that data assets can be monetised by exchanging and trading data between organisations as data products and services. There are multiple definitions for data product. In an article authored by Jian Pei (2020), data products "refer to data sets as products and information services derived from data sets." Simon O'Regan's defines data product as a product whose primary objective is to use data to facilitate an end goal. From the academic literature we have found several subtypes of data products: raw data, derived data, data sets, reports, analytic views, 3D visualisations, algorithms, decision support (dashboards) and automated decision-making (Netflix product recommendations or Spotify’s Discover Weekly would be common examples). Typically raw data, derived data and algorithms have technical users. Most often they tend to be internal products in an organisation. If we dive in the data mesh world, this quote from Zhamak Dehghani’s book is key to understand the definition of data as a product: “Domain data teams must apply product thinking […] to the datasets that they provide; considering their data assets as their products and the rest of the organization’s data scientists, ML and data engineers as their customers.” While many of the standard Product Development Rules apply — solve a customer need, learn from feedback, prioritise relentlessly, etc. — data has different characteristics compared to tangible products that prevent the direct transfer of established processes and rules of trading goods, especially in terms of pricing mechanisms. In trading data, there is less willingness to pay. For example, data buyers often do not recognise the potential value of data items because it cannot be fully disclosed prior to purchase (known as the ‘Arrow paradox’). In addition, there is often a lack of notion that the creation, processing, storage and distribution of high-quality data is a major cost factor for the data provider. Another obstacle is the lack of trust and security causing potential data providers to fear that competitors could benefit from disclosure of in-house data. One of the aims of this specification is to tackle above mentioned issues which hinder the growth of data ecosystem and market volatility.
Data as a service	In computing, data as a service, or DaaS, is a term used to describe cloud-based software tools used for working with data, such as managing data in a data warehouse or analyzing data with business intelligence. It is enabled by software as a service (SaaS). DaaS like all "as a service" (aaS) technology, builds on the concept that its data product can be provided to the user on demand, regardless of geographic or organizational separation between provider and consumer. According to Daniel Newman from Forbes (2017) DaaS is essentially a data stream that subscribers can access on demand. Some people use the term data product in a meaning which contains also data commodities which have more service alike attributes than product attributes. In those cases we prefer to use the term data as a service and call the creation process as data servitization. The term productizement is reserved for the process which creates data products as end result.
Data as a service business model	Data as a service as a business model is a concept when two or more organizations buy, sell, or trade machine-readable data in exchange for something of value. Data as a service is a general term that encompasses data-related services. Now DaaS service providers are replacing traditional data analytics services or happily clustering with existing services to offer more value-addition to customers. The DaaS providers are curating, aggregating, analyzing multi-source data in order to provide additional more valuable analytical data or information. Typically, DaaS business is based on subscriptions and customers pay for a package of services or definite services.
Data pipeline	According to Aiswarya et al. the complex chain of interconnected activities or processes from data gen- eration through data reception constitutes a data pipeline. In other words, data pipelines are the connected chain of processes where the output of one or more processes becomes an input for another. It is a piece of software that removes many manual steps from the workflow and permits a streamlined, automated flow of data from one node to another. Moreover, it automates the operations involved in the selection, extraction, transformation, aggregation, validation, and loading of data for further analysis and visualization. It offers end to end speed by removing errors and resisting bottlenecks or delay. Data pipelines can process multiple streams of data simultaneously

Editors and contributors

This specification is openly developed and a lot of the work comes from community. We list all community contributors as a sign of appreciation. The editors (as initial creators of the the specification) are Jarkko Moilanen and Jussi Niilahti. Editors take the feedback and draft new candidate releases, which may become the versions of the specification.

List of community contributors

The work around the specification would not be possible without enormous help from the community. Here's list of contributors so far.

Toni Luhti
Topi Santakivi