Data as a service (DaaS) is a cloud-based software tool 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).[1] Like all "as a service" (aaS) technology, DaaS builds on the concept that its data product can be provided to the user on demand,[2] regardless of geographic or organizational separation between provider and consumer. Service-oriented architecture (SOA) and the widespread use of APIs have rendered the platform on which the data resides as irrelevant.[3]
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.[4]
Overview
DaaS began primarily in Web mashups and, since 2015, has been increasingly employed both commercially, and within organizations such as the United Nations.[5]
Traditionally, most organisations have used data stored in a self-contained repository, for which software was specifically developed to access and present the data in a human-readable form. One result of this paradigm is the bundling of both the data and the software needed to interpret it into a single package, sold as a consumer product. As the number of bundled software with data packages proliferated, and required interaction among one another, another layer of interface was required. These interfaces, collectively known as enterprise application integration (EAI), often tended to encourage vendor lock-in, as it is generally easy to integrate applications that are built upon the same foundation technology.[6]
The result of the combined software/data consumer package and required EAI middleware has been an increased amount of software for organizations to manage and maintain, simply for the use of particular data. In addition to routine maintenance costs, a cascading amount of software updates are required as the format of the data changes. The existence of this situation contributes to the attractiveness of DaaS to data consumers, because it allows for the separation of data cost and of data usage from the cost of a specific software environment or platform. Sensing as a Service[7][8] (S2aaS) is a business model that integrates Internet of Things data to create data trading marketplaces.
Vendors, such as MuleSoft, Oracle Cloud and Microsoft Azure, undertake development of DaaS that more rapidly computes large volumes of data; integrates and analyzes that data; and publish it in real-time, using Web service APIs that adhere to its REST architectural constraints (RESTful API).
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.[9]
This data is being used to increment internal companies' data to improve business processes and decision making, for AI training and for supplementing organization’s services or products.[10] Wherein, external DaaS uses data licensed from a vendor, which is supplied to a customer on demand. For example, Clearbit uses internal customers data (CRM, MAP and ABM), so they could provide more personalized services, and also enriches their systems with real-time data to improve lead routing, to enhance segmentation, and provide intelligence across their entire stack.[11]
Usually, the data is delivered via network which is typically cloud-based. "To this end, organizations may 'buy, sell, or trade' soft-copy data as a DaaS service".[12]
Typically, DaaS business is based on subscriptions and customers pay for a package of services or definite services. At the same time, investors must make sure that the revenue generated exceeds initial and operational costs of running the business. The pricing model is usually classified into 2 categories:
quantity-based pricing model and pay-per-call (PPCall)
Since the customers only get access to the data stream delivered by DaaS vendors when they need it, this eliminates the need to store data within a company and the corresponding costs, which makes the business more flexible.[14]
One of this business model parts is regulation in the field of user data turnover. There are a number of regulations that require vendors to comply with specific customer service requirements. In particular, the website that collects the data must notify visitors about what kind of data is being collected and obtain consent to these actions. Sites should also promptly notify visitors if any of their personal data stored on the site has been breached. In addition, an assessment of the security of website data and ensuring their protection is required.[15] The General Data Protection Regulation[16] has become the model for many national laws outside the EU, including the United Kingdom, Turkey, Mauritius, Chile, Japan, Brazil, South Korea, Argentina and Kenya, and formed the basis for the California Consumer Privacy Act.[17][18]
Big Data Business Models
Another component of the business model is about ensuring that the customers may receive and use data to improve their own value propositions (products, services). In this business model, data provides value as a support mechanism or a tool for creating other value propositions, that's why the revenue stream is typically quite a bit lower.[19]
In turn, Data as a Service is one of 3 categories of big data business models based on their value propositions and customers:
Answers as a Service;
Information as a Service;
Data as a Service.
Use of DaaS business model in different areas
Data as a Service vendors use different types of data to provide services in different areas of business. For example, People Data Labs is collecting public data about people for their customers could empower recruiting platforms, create AI models, custom audiences, etc.[20][21]
In the field of financial technologies consumers’ financial and behavior data are being collected and aggregated to help organizations to make better decisions to increase profitability and reduce risk in lending, to provide services to business, government and individuals.[22][23][24][25]
In another segment, DaaS vendors assemble mobile operators’ data to provide various types of services. For example, oneFactor platform where other businesses (telecoms, banks, retailers, payment systems, etc.) may monetize their own data by processing and enriching it with additional information, building machine learning models and launching them in production.[26][27]
In the realm of business data, DaaS vendors such as Enigma Technologies aggregate and provide insights into various aspects of business operations, such as revenue profiling, market analysis, business onboarding, and competitive benchmarking. These services enable businesses to optimize their strategies, identify market opportunities, and enhance operational efficiency. For instance, some DaaS providers focus on collecting and analyzing data from retail locations to help companies understand revenue patterns, customer behavior, and market trends, thereby informing site selection and expansion strategies.
There are companies in the DaaS market which offer weather forecasting services based on meteorological data collected around the world.[28]
Data as a service operates on the premise that data quality can occur in a centralized place, cleansing and enriching data and offering it to different systems, applications, or users, irrespective of where they were in the organization, or on the network.[3] DaaS undertakes to provide the following advantages:
Agility – users can move quickly, due to the simplicity of data access, and not needing extensive knowledge of the underlying data. Data structures and location-specific requirements can be modified to meet user needs.
Cost-effectiveness – providers can build the base with the data experts and outsource the presentation layer, which makes for very cost-effective user interfaces and makes change requests at the presentation layer much more feasible.
Data quality – data access is controlled through data services, which tends to improve data quality, as there is a single point for updates. Once those services are tested, only regression testing is needed, if they remain unchanged for the next deployment.
Criticism
The drawbacks of DaaS are generally similar to those associated with any type of cloud computing, such as the reliance of the user on the service provider's ability to avoid server downtime from terrorist attacks, power outages or natural disasters. A common criticism specific to the DaaS model is that when compared to traditional data delivery, the consumer is merely "renting" the data, and using it to produce analytics or insights, and, generally, the original data is not available for download.[29]
The pitfall of using Data as a Service business model is the problem of data piracy and leaks of sensitive data.[30][31][32][33] Typically, all DaaS business operators develop and use a licensing agreement to protect the intellectual property rights of the data they sell, process or analyze in order to protect the data from any type of copyright infringement, subscription violation, or use violation[34]
Despite the fact that DaaS providers sell anonymized data to customers, in some cases the cleaning process leaves a lot of data available to customers and may allow exposing the people included in the dataset.[35]
There is also a problem with user consent to the collection, processing and storage of data. Mobile application developers may sell data from users' smartphones, at the same time, the application users may not always be aware of what information is being tracked by the application.[36][37][38]
Publishers of public data like LinkedIn may consider scraping their public websites for reselling directly or as analytical products not desirable. However, they have had limited success in the courts.[39] There is an argument that scraping public data and making it available either free of charge or as commercial products has economic and social benefits like challenging data monopolies or helping journalism.[40]
Customer relationship management (CRM) is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information.
Enterprise application integration (EAI) is the use of software and computer systems' architectural principles to integrate a set of enterprise computer applications.
Retail software is computer software, typically installed on PC-type computers post 2005, delivered via the Internet. Traditionally this software was delivered via physical data storage media sold to end consumer but very few companies still provide their software using physical media. The software is typically sold under restricted licenses or in the case of cloud-based software sold as a Software-as-a-Service (SaaS) model.
Software as a service is a form of cloud computing in which the provider offers the use of application software to a client and manages all the physical and software resources used by the application. The distinguishing feature of SaaS compared to other software delivery models is that it separates "the possession and ownership of software from its use". SaaS began around the turn of the twenty-first century and became the main form of software application deployment by 2023.
Software multitenancy is a software architecture in which a single instance of software runs on a server and serves multiple tenants. Systems designed in such manner are "shared". A tenant is a group of users who share a common access with specific privileges to the software instance. With a multitenant architecture, a software application is designed to provide every tenant a dedicated share of the instance—including its data, configuration, user management, tenant individual functionality and non-functional properties. Multitenancy contrasts with multi-instance architectures, where separate software instances operate on behalf of different tenants.
A vendor management system (VMS) is an Internet-enabled, often Web-based application that acts as a mechanism for business to manage and procure staffing services – temporary, and, in some cases, permanent placement services – as well as outside contract or contingent labor. Typical features of a VMS application include order distribution, consolidated billing and significant enhancements in reporting capability that outperforms manual systems and processes.
On-premises software is installed and runs on computers on the premises of the person or organization using the software, rather than at a remote facility such as a server farm or cloud. On-premises software is sometimes referred to as "shrinkwrap" software, and off-premises software is commonly called "software as a service" ("SaaS") or "cloud computing".
Guided selling is a process that helps potential buyers of products or services to choose the product best fulfilling their needs and hopefully guides the buyer to buy. It also helps vendors of products to actively guide their customers to a buying decision and thus increases their conversion rate.
Infrastructure as a service (IaaS) is a cloud computing service model by means of which computing resources are supplied by a cloud services provider. The IaaS vendor provides the storage, network, servers, and virtualization. This service enables users to free themselves from maintaining an on-premises data center. The IaaS provider is hosting these resources in either the public cloud, the private cloud, or the hybrid cloud.
Spotler CRM is a Cloud CRM provider, offering CRM systems to small and medium sized companies.
Platform as a service (PaaS) or application platform as a service (aPaaS) or platform-based service is a category of cloud computing services that allows customers to provision, instantiate, run, and manage a modular bundle comprising a computing platform and one or more applications, without the complexity of building and maintaining the infrastructure typically associated with developing and launching the application(s), and to allow developers to create, develop, and package such software bundles.
In information systems, applications architecture or application architecture is one of several architecture domains that form the pillars of an enterprise architecture (EA).
Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.
Network intelligence (NI) is a technology that builds on the concepts and capabilities of deep packet inspection (DPI), packet capture and business intelligence (BI). It examines, in real time, IP data packets that cross communications networks by identifying the protocols used and extracting packet content and metadata for rapid analysis of data relationships and communications patterns. Also, sometimes referred to as Network Acceleration or piracy.
HP Cloud was a set of cloud computing services available from Hewlett-Packard. It was the combination of the previous HP Converged Cloud business unit and HP Cloud Services, an OpenStack-based public cloud. It was marketed to enterprise organizations to combine public cloud services with internal IT resources to create hybrid clouds, or a mix of private and public cloud environments, from around 2011 to 2016.
Asigra Inc. is a company that produces a cloud backup and recovery software platform known as Asigra Cloud Backup. The company is privately held and is headquartered in Toronto, Canada.
"X as a service" is a phrasal template for any business model in which a product use is offered as a subscription-based service rather than as an artifact owned and maintained by the customer. Originating from the software as a service concept that appeared in the 2010s with the advent of cloud computing, the template has expanded to numerous offerings in the field of information technology and beyond it. The term XaaS can mean "anything as a service".
Software monetization is a strategy employed by software companies and device vendors to maximize the profitability of their software. The software licensing component of this strategy enables software companies and device vendors to simultaneously protect their applications and embedded software from unauthorized copying, distribution, and use, and capture new revenue streams through creative pricing and packaging models. Whether a software application is hosted in the cloud, embedded in hardware, or installed on premises, software monetization solutions can help businesses extract the most value from their software. Another way to achieve software monetization is through paid advertising and the various compensation methods available to software publishers. Pay-per-install (PPI), for example, generates revenue by bundling third-party applications, also known as adware, with either freeware or shareware applications.
Oracle Cloud is a cloud computing service offered by Oracle Corporation providing servers, storage, network, applications and services through a global network of Oracle Corporation managed data centers. The company allows these services to be provisioned on demand over the Internet.
A data management platform (DMP) is a software platform used for collecting and managing data. DMPs allow businesses to identify audience segments, which can be used to target specific users and contexts in online advertising campaigns. They may use big data and artificial intelligence algorithms to process and analyze large data sets about users from various sources. Advantages of using DMPs include data organization, increased insight on audiences and markets, and more effective advertisement budgeting. On the other hand, DMPs often have to deal with privacy concerns due to the integration of third-party software with private data. This technology is continuously being developed by global entities such as Nielsen and Oracle.
References
↑ Machan, Dyan (August 19, 2009). "DaaS:The New Information Goldmine". Wall Street Journal. Retrieved 2010-06-09. Unfortunately, the business world has given this baby a jargony name: data as a service, or its diminutive, DaaS.
↑ Olson, John A. (January 2010). "Data as a Service: Are We in the Clouds?". Journal of Map & Geography Libraries. 6 (1): 76–78. doi:10.1080/15420350903432739.
↑ Perera, Charith; Zaslavsky, Arkady; Christen, Peter; Georgakopoulos, Dimitrios (2014-01-01). "Sensing as a service model for smart cities supported by Internet of Things". Transactions on Emerging Telecommunications Technologies. 25 (1): 81–93. arXiv:1307.8198. doi:10.1002/ett.2704. ISSN2161-3915.
This page is based on this Wikipedia article Text is available under the CC BY-SA 4.0 license; additional terms may apply. Images, videos and audio are available under their respective licenses.
