UPDATED BY
Matthew Urwin | Oct 13, 2023

Data-as-a-Service (DaaS) is a data management method that uses cloud computing to provide information on-demand. Following the software-as-a-service model, where a central host delivers applications to end-users on a recurring subscription basis, DaaS organizes information from a range of sources into a system of convenient datasets made available through APIs. The key objective of DaaS is to simplify data access, which in turn facilitates informed, data-driven decision-making.

Data-as-a-Service definition

Data-as-a-Service is a cloud-based data management software tool that delivers data storage, integration, processing and analytics via a network. DaaS leverages data as a valuable business asset to inform data-driven decision-making.

DaaS platforms can store, catalog, integrate, process, analyze and deliver data in a range of intuitive interfaces as long as there’s a network connection — no hardware or installation necessary. This data architecture can be built in-house or outsourced to DaaS vendors.

Affordable bandwidth and ubiquitous cloud-based platforms have made DaaS extremely feasible for companies to adopt it into their business strategy.

“As major corporations recognize the vital role of data in shaping their business strategies, it becomes imperative to … leverage data insights for a competitive advantage,” Veena Nayak, the vice president of data strategy and solutions at the University of Phoenix, told Built In. “DaaS is a key approach in developing a scalable framework … that empowers organizations to extract value from their existing or acquired data.”

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Data-as-a-Service Benefits

Primarily, DaaS is a decision-making tool.

“Other than compliance or government functions, I cannot think of a data-as-a-service [user] that isn’t ultimately trying to make a decision,” Trevor Ewen, COO of QBench, a cloud-based information management system for laboratories, and former senior software engineer at Bloomberg, told Built In. “Being able to buy the data is one thing, but the best firms know how to mobilize the insights.”

Whether it’s to make a high-stakes, capital-intensive decision or simply beat out the competition, DaaS can help. The following lists several perks these platforms provide.

 

High-Quality Data

A DaaS provider’s sole task is to maintain the accuracy of integrated datasets. That way, the companies that hire them don’t have to. DaaS platforms use data governance practices, data validation techniques and quality control measures to deliver reliable, up-to-date data from various sources, said Rishabh Misra, a senior machine learning engineer at X (formerly Twitter). Algorithms and scripts automate revisions, reducing the possibility of human error.

 

Low Cost

DaaS reduces infrastructure, personnel and data storage system costs to zero. By outsourcing data management to a third-party provider, companies only have to pay for data services on a subscription or usage basis. By design, DaaS gets rid of ill-informed, “gut” decisions using things like predictive analysis, which means less waste in effort, resources and spending.

 

Flexibility, Accessibility, Scalability

With DaaS, data can be accessed from anywhere and at any time from the cloud, as long as there is an internet connection. These platforms intake large volumes of machine-readable data, then display the information onto user-friendly interfaces. The flexible nature of DaaS — easily mutable via a software update — yields scalability, Misra said, which accommodates large volumes of data within a framework that’s built to grow along with data requirements.

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Democratization

“Breaking down data silos and providing teams with the data they need is a significant challenge for today’s businesses,” said Paolo Platter, the chief technology officer at data engineering firm Agile Lab. “DaaS ensures that organizations can provide integrated data from a growing list of data sources, fostering a data-driven culture and democratizing the use of data in day-to-day processes.”

 

Fast Speeds

Without the need for extensive data preparation and management, users have valuable data readily available at their fingertips. This aspect alone expedites time-to-insight and decision-making processes, Misra said.

“When reliable data is delivered to different departments and teams that need it, ideas based on that data have a greater chance of gaining approval from other areas of the company and ultimately succeeding once implemented,” Platter noted. “Ideas can take off faster with access to data that inspire new initiatives and drive growth.”

 

Return Focus on Core Competencies

Outsourcing data management to DaaS providers means end-user companies can refocus on what they do best. By employing DaaS, Nayak said, they can still “tap into the power of data [while] eliminating the burdensome complexities of managing intricate data infrastructures.”

 

Challenges of Data-as-a-Service

Despite the obvious advantages DaaS platforms bring to the table, companies need to be aware of the challenges. 
 

Data Security

More accessible data means more vulnerable data. Companies need to bolster their cybersecurity measures and adapt their strategies to account for the security vulnerabilities that come with storing data in the cloud. 

 

Data Privacy

If a company has sensitive information stored on the cloud, it may be visible to parties that shouldn’t have access to it. Organizations may want to adopt data encryption practices and other methods to make files harder to view. Establishing different levels of access can also limit who can view information and when, adding an extra layer of privacy. 

 

Data Tools

Teams must use tools that are compatible with their DaaS platform, which may limit their options. Even if a DaaS platform works well with a range of tools and software, companies may be restricted to using fewer data tools than before. 

 

Data Governance

Transferring data to a centralized, cloud-based location poses unique difficulties for companies with strict compliance rules. Organizations need to be aware of any privacy laws, data quality best practices and other data policies they’ve agreed to before switching to a DaaS platform.  

 

Data Complexity

Gathering data across different departments and bringing it into one location can be tedious, especially if an organization has massive amounts of data that have been siloed. Compiling this data requires collaboration across various teams, so it may take some time to sort through large volumes of complex data.   

 

Data-as-a-Service Examples

While fairly new, DaaS is used by businesses every day. Marketing teams use DaaS to create campaigns and outreach strategies tailored to their target audience, for example, in the same way financial data companies can provide real-time insights on consumer spending patterns and risk analysis. Thanks to DaaS, applications that rely on geospatial data can relay location services, weather forecasts and mapping information.

Here are some other ways DaaS is applied today:

 

Credit Agencies

Experian, TransUnion and Equifax are examples of companies that compute credit scores based on data provided by its users. Salaries, the amount of money borrowed and frequency at which a loan is repaid are all bits of data that are collected, then sold to companies of interest. Luckily, best practices exercised by DaaS platforms include privacy and security, so while these companies share sophisticated insights, sensitive information stays protected within the database.

 

Transportation and City Planning

DaaS providers like Streetlight Data track traffic flows of city streets by collecting GPS and cellular data points from anonymized phone records and government sources in order to build a model of how people move throughout a city. By recording the flow and frequency of pedestrians, bikes and vehicles, this data can be used to inform better transit system routes and plan new public works projects, without any need to deploy a sensor network, manually count passersby or collect roadside surveys.

 

Web Scraping

HIRinfotech and BrightPlanet are DaaS platforms that harvest data from the web, then curate the information and develop useful insights. Using readily available information online, these providers employ web scraping and data mining to gather real data from product or business directories when building custom solutions for their clients.

 

Marketing, Sales and Administration

Services like Informatica and Oracle use data integration software to automate tasks such as maintaining a digital rolodex of contacts. These DaaS platforms combine artificial intelligence with data verification and data enrichment, double-checking phone numbers and addresses, cross referencing contact lists with the National Do-Not-Call databases and importing updated information.

 

Frequently Asked Questions

What is data-as-a-service?

Data-as-a-Service (DaaS) is when data is compiled and stored on a centralized cloud location, allowing team members to access that data anywhere, anytime via APIs. This way, even members with less technical knowledge can still access and use complex data.

What is an example of data-as-a-service?

An example of data-as-a-service is credit agencies like Experian and Equifax collecting various data points from users to determine individual credit scores.

Difference between SaaS and DaaS?

While both provide tools and software, the main difference between SaaS and DaaS is that DaaS offers many applications that users can access information through. SaaS platforms may only present one or two applications teams can use to access their data.

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