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Data as a Service (DaaS): What, Why, How, Use cases & Tools in '24

Updated on Jun 14
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Data as a Service (DaaS): What, Why, How, Use cases & Tools in '24Data as a Service (DaaS): What, Why, How, Use cases & Tools in '24

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Data as a service solutions help companies expose their data internally or externally. These solutions help companies monetize their data or democratize access to data and support analytics efforts

What is Data-as-a-Service?

Data-as-a-Service (DaaS) providers provide requested data to their consumers via m2m (machine-to-machine) interfaces.

There are 2 forms of DaaS services:

  1. DaaS provider: A company providing requested data to another company via m2m interfaces
  2. Technology provider for DaaS provider: A tech vendor enabling another company to provide its data as a service. DaaS can be provided to other companies or used internally

In both of its forms, DaaS is a cloud data management strategy that offers data accessibility from a variety of sources to drive new applications and digital systems. DaaS removes the need to install and manage software on-premise. It enables organizations to outsource data storage, integration, processing operations and analytics services in the cloud.

Wikipedia definition is:

“DaaS 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. Service-oriented architecture(SOA), and the widespread use of API, has rendered the platform on which the data resides as irrelevant.”

DaaS is an architectural construct rather than a single vendor technology. Therefore it offers a variety of ways to deliver, collect and process data from various sources in different formats. Technologies included in the DaaS category are: 

  • information lifecycle management solutions
  • data modeling/quality/replication/transformation
  • content management

Though DaaS platforms typically have volume-based pricing method, some vendors may also offer data type-based subscriptions.

What are the use cases?


Data as a service is a useful tool when you want to compare your organization’s performance against peers. With DaaS, organizations can access global data and create benchmarking reports that may include financial performance, turnover, leadership effectiveness with percentile breakdowns. For example, Workday is a vendor that provides such capabilities with Data as a service benchmarking product.

Benchmarking report of Workday's DaaS tool
Source: GlobeNewswire

Data marketplaces

Finding the right data quickly is essential in the age of self-service analytics. Data scientists may not have full visibility into available data sets, the content of these data sets and the quality of each. Data marketplaces give data seekers the services needed to find data sets and evaluate their fit by reading the reviews of others. These platforms make data sets reusable for new audiences.

Business intelligence

Companies can offer their data as a service to internal users facilitating business intelligence. DaaS streamlines data standardization, unifying different sources of data, data virtualization and automation of analytics. Data scientists can access data in real-time so that they can perform any necessary transformations and integrations of data dynamically and interpret data for decision making. 

What are example case studies?


AI startup, AMPLYFI, needed to collect and process a large amount of online data for machine learning. However, an in-house solution at enterprise scale would have been difficult to achieve.

They partnered with BrightPlanet to solve the problem with a DaaS solution. BrightPlanet DaaS platform crawls a large volume of web data including Deep Web data and makes it available for AMPLYFI. BrightPlanet engineers handled the harvesting and curation of data along with identification of web contents’:

  • Actual date of publication
  • Actual location of publication
  • Industry-specific keywords


ClosingCorp is a provider of residential real estate closing cost data. Closing cost includes expenses, over and above the price of the property, that buyers and sellers normally incur to complete a real estate transaction. ClosingCorp partnered with ATTOM to obtain tax accessor and county recorder data so that they can provide accurate closing costs in real-time. After ATTOM launched its DaaS platform, ClosingCorp integrated the solution to have access to daily updates without the need for managing a massive amount of bulk data transfer.

Why is it important now?

Expectations for Data as a Service are high in Gartner's hype cycle

According to Gartner’s hype cycle, DaaS is still 5-10 years away from reaching its plateau of productivity. It is expected to be more impactful than most other data-related advancements since DaaS has the potential to become the center of analytics/big data.

Increasing importance of data and analytics is driving the importance of data as a service. External DaaS services enable companies to easily access external data. Internal DaaS services make it easier for companies to democratize analytics and empower their business users.

Though companies realized the importance & potential of analytics, organizations still face challenges during the implementation process as indicated below. Data as a Service platforms can help organizations overcome some of the technical challenges stated below.

Leading challenges of implementing data analytics survey results
Source: Rocket Source

How does it work?

DaaS Infrastructure: DaaS platform is an intermediary between data and data tools
Source: Dremio

As a technology used internally at a company, data-as-a-Service platform is an end-to-end solution and can be considered as an enabler between various data sources and tools such as self-service reporting, BI, microservices, and applications. Once the platform is deployed, end-users can access data whenever they want using standard SQL over ODBC, JDBC, or REST.

Companies can also use external DaaS services to access data. Numerous companies provide DaaS services via simple APIs. For example, these are seme of the leading providers providing data on companies: Clearbit, Crunchbase etc.

What are its benefits?

  • Agility: DaaS increases the speed to access the necessary data by exposing the data in a flexible but simple way. Users can quickly take action without the need for a comprehensive understanding of where the data is stored or how it is indexed. Agility is the most important benefit of DaaS and it helps decrease time-to-market for DaaS users.
  • Financial flexibility: DaaS allows companies to trade-off between investment and operating expenses. Companies can use DaaS to launch services without investing in the systems and personnel to manage their data.
    • DaaS reduces the capacity on source systems, cutting costs for licensing, MIPS, and hardware.
    • DaaS also helps organizations save maintenance costs. DaaS users don’t need to work on constant testing and maintenance since DaaS vendors keep their tools updated for end-users.
  • Data quality: Users access data via the data service. Since data service is the single update point, tracking changes to data is easier which can lead to data quality improvements.
  • Cloud flexibility: Cloud offers more flexibility and scalability than on-premise data management alternatives.

What are its challenges?

  • Security: As the number of data breaches is increasing each year, cybersecurity measures should be taken seriously. If DaaS vendor’s security measures are not enough to prevent potential data breaches, your organization may lose millions and have its reputation harmed. Before deciding on a DaaS vendor, it is best to understand the vendors’ approach to data security. 
  • Privacy: Data shared may include confidential/personal information. Organizations need to ensure that DaaS companies are providing the necessary measures to ensure confidentiality of personal data.
  • Data set hygiene: When an organization works with a DaaS vendor, they may combine their internal data with the provider’s data set but vendors’ and the organization’s set of rules during data preparation may not match and this leads to dirty data. Organizations should ensure that the vendor understands how to cleanly sync with other data sets.

What are the leading tools to enable DaaS?

Technologies enabling data-as-a-service can be segmented into these categories:

Data integration

Data integration tools are able to select, prepare, extract, and transform data and transfer data from different sources to one centralized one. 

  • Talend Data Integration Software: An enterprise data integration software to connect, access, and transform any data across the cloud or on-premises.
  • Informatica Powercenter: Informatica Powercenter is a data integration tool that offers the capability to access & obtain data from different sources and processing of data.
  • Data Virtuality: Data Virtuality is a data integration and management platform for instant data access, easy data centralization and data governance.

Database Management Systems (DBMS)

DBMS is a complete software system to define, create, update, manage and query a database.

  • Microsoft SQL Server: A relational database management software to store and retrieve data used by other applications.
  • IBM Db2: AI-powered hybrid database management software to manage structured or unstructured data either on-premise or in the cloud. Db2 is built on an intelligent common SQL engine designed for scalability and flexibility.

Self-service data preparation

Self-service data preparation tools help organizations to democratize data. It empowers analytics capabilities to explore complex data at scale and have greater control over the end analytic output.

  • Pentaho 7.0: Pentaho provides open-source BI and data integration products that cross the divide between big data and data preparation.
  • Datawatch Managed Analytics Platform: The platform is designed as an enterprise solution for self-service data preparation and visual data discovery. Its data preparation capabilities are disparate data set manipulation, filtering, enrichment blending, and combining data.

Feel free to check our articles about other data&analytics solutions:

Data Integration Tools: In-depth Guide

Improving Understanding with Data Visualizations

ETL Tools: In-depth Guide

If you still have questions about DaaS solutions&vendors, don’t hesitate to contact us:

Find the Right Vendors
Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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