What is data as a service (DaaS)?
Data as a service (DaaS) is an information provision and distribution model in which data files -- including text, images, sounds and videos -- are made available to customers over a network, typically the internet. The model uses a cloud-based underlying technology that supports web services and service-oriented architecture (SOA). DaaS information is stored in the cloud and is accessible through different devices. The service also offloads the downsides of data management to the cloud provider.
DaaS enables the separation of data cost and usage from software or platform cost and usage. Hundreds of DaaS vendors, with various pricing models, exist worldwide. Pricing can be volume-based, which is a fixed cost per megabyte of data in the entire repository, or format-based; for example, a fixed price per text file and another fixed price per image file.
High-speed internet service has become increasingly available to support user access from more areas around the world, making DaaS an attractive option for a wider audience. Similarly, organizations with an excess of data might have a difficult and expensive time maintaining that data, making DaaS a popular option. The evolution of SOA has greatly reduced the relevance of the particular platform on which data resides.
How does data as a service work?
DaaS works in the following ways:
- Data is sourced. The DaaS provider gathers data from several sources, including public data sets, proprietary data sets, integrations with third parties and even web scraping.
- Data is cleaned up and normalized. Pulling from many sources means that data arrives in many different formats and with various levels of suitability for use. The provider cleans, validates, and, if necessary, enriches the data, then standardizes it for use.
- Data is stored and managed. Once cleaned and normalized, data is stored in a scalable cloud infrastructure, such as a database, data warehouse or data lake. Then it's moved to a secure storage system.
- Data is delivered. Consumers can access the data through APIs, downloadable files, real-time streaming feeds and portals.
- Data is consumed. Clients access the provider's desired data and feed it into their own customer relationship management or enterprise resource planning platforms, analytics dashboards and real-time operational systems.
What are some use cases for data as a service?
As mentioned above, DaaS can be a great resource for providing data for a variety of important uses, including the following:
- Business intelligence (BI). Companies use DaaS to ingest external data sets for business processes and applications that perform analysis or provide analytical reporting.
- Data enrichment. If a company has its own internal analytics, DaaS feeds can enhance those analytics with external data.
- Data sharing. Often, companies share their data with a partner company through DaaS.
- Real-time applications. Many companies embed DaaS feeds into their apps when real-time data is required.
- Machine learning. DaaS can provide large amounts of data for AI and machine learning training purposes
The convenience and cost-effectiveness of DaaS make it attractive across a wide range of industries and diverse use cases within each. The following are some prominent examples:
- Retail and e-commerce. DaaS can be used for price optimization, customer segmentation, personalization and supply chain updates. Use case: An e-commerce site adjusts pricing based on competitors' DaaS feeds.
- Healthcare. DaaS can provide a broad range of demographics, medical research data sets, environmental, and epidemiological data for public health applications. Use case: A pharmaceutical company uses several DaaS providers to obtain data for clinical trial support.
- Finance and banking. Market feeds of all kinds get their data from DaaS providers; many companies use DaaS to obtain credit information and for fraud detection. Use case: A stock trading company uses DaaS for up-to-the-minute market movement data.
- Marketing and sales. DaaS feeds can provide customer data for consumer behavior analysis and for marketing campaign planning, for audience targeting. Use case: An advertising firm uses DaaS-based customer data for targeting purposes.
- Insurance. DaaS feeds can support risk assessment processes using property records, vehicle data for use metrics, and data for fraud detection analyses. Use case: A property insurer uses DaaS-based historical data to anticipate damage and claims resulting from an impending storm.
- Transportation and logistics. DaaS feeds can provide weather and traffic data for route optimization; global shipping data is available. Use case: A trucking fleet uses DaaS weather and traffic feeds to reroute its vehicles around accidents and road construction.
- Energy. Internet of things sensor-based energy usage data is stored in a DaaS system for aggregation and distribution; commodity pricing data for energy markets can be accessed; and weather data is readily available. Use case: A utility company predicts the rise and fall in energy demand based on aggregated DaaS smart meter data.
What are the benefits of data as a service?
Benefits of DaaS include the following:
- Enables data to be easily moved from one platform to another.
- Prevents confusion that can occur when multiple versions of supposedly the same data exist in different locations.
- Reduces maintenance and delivery costs due to outsourcing of the presentation layer.
- Preserves data integrity by implementing access control measures such as strong passwords and encryption.
- Avoids vendor lock-in.
- Eases administration and collaboration.
- Provides compatibility among diverse platforms.
- Delivers global accessibility.
- Provides automatic updates.
What are the challenges of data as a service?
Challenges to DaaS include concerns with privacy, security and data governance. Privacy challenges revolve around the fact that the data shared might often include information pertaining to mission-critical applications. Concerning security, the data for mission-critical applications might be left vulnerable if the DaaS vendor's security is not up to par. It might also be difficult to ensure data governance between a DaaS environment and an organization.
DaaS providers
DaaS offers convenient and cost-effective options for customer- and client-oriented enterprises. Examples of DaaS providers include the following:
- GapMaps, Safegraph and Urban Mapping are geography data services that provide data for customers to embed into their own websites and applications.
- Bloomberg, S&P Global and Xignite are companies that make financial data available to customers through cloud services.
- Equifax, Experian and TransUnion provide credit data and analytics services for credit reporting and risk assessment.
- Acxiom, NielsenIQ and ZoomInfo provide customer data and other BI for marketing and sales and consumer insights.
What is the future of data as a service?
Information management specialists believe that as more companies figure out which data assets they can rent for competitive advantage, the DaaS market will continue to expand. DaaS is expected to be a launching point for both BI and big data analytics markets, according to Gartner.
Gartner also sees the DaaS market growing as more organizations start seeing it as a fitting way to manage mission-critical data. Grand View Research forecasts the DaaS market will grow to $76.80 billion by 2030, and Market Research Future predicts it will grow to $75.2 billion by 2032. Whatever the actual numbers turn out to be, DaaS is expected to proliferate substantially.
DaaS is closely related to storage as a service and software as a service, and can be integrated with one or both of these provision models. As is the case with these and other cloud computing technologies, DaaS adoption could be hampered by concerns about security, privacy and proprietary issues.
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