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The continued growth of Cloud Computing has led to a host of IT-related service offerings derived and consumed at a cloud level. Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS) are some of the most popular products – and acronyms – emerging out of this newer trend.
Of course, it stands to follow that data service providers are beginning to offer the advantages of cloud-based secure data centers to all sorts of enterprises. Enter Data as a Service (DaaS), although in some respects all the “as a Service” concepts are merely extensions or commercializations of Service Oriented Architecture (SOA).
Forward thinking enterprise integration architects now leverage DaaS as a valuable option on their tool belt. DaaS can be used to access a company’s own data externally through a service layer as well commercially available data like address information or business data through a provider like Hoovers.
Additionally, companies who maintain datasets with commercial value to other firms are able to wrap a DaaS layer around that data, providing easy access for customers and a new revenue source for the company itself.
As a note, sometimes the commercial offering of infrastructure services to support access to databases and their servers is referred to a Data Center as a Service (DCaaS). Apparently, there is always room for a new ingredient in the technology acronym alphabet soup!
Advantages of DaaS
Many of the advantages of Data as a Service in addition to the PaaS, IaaS, and SaaS offerings also parallel those of Service Oriented Architecture – the security of an offsite, managed infrastructure, for instance. Some benefits, however, are specific to data, especially the internal kind.
Maintaining Data Quality becomes an easier practice when using a DaaS because of the control provided by a single, managed interface to the data. The Data as a Service approach needs to be a consideration when analyzing the technical aspects of a Data Governance or Master Data Management program implementation.
Business in these days of Agile methodologies moves at a high velocity. Enterprises might not have the resources to fully manage the technical aspects of their data investment, like models and metadata. A DaaS interface can allow non-technical users to easily make minor structural changes to data or reports, quickly meeting business requirement changes.
An additional benefit also found in other “as a Service” architectures is the cost savings achieved when enterprises focus on what they do well — run their business. Money does not need to be misspent building an internal Data Management team, if that team is not an important requirement of doing business. On the other hand, companies needing to access terabytes of their own data probably don’t want to do it over the Cloud.
Simplified access to data lies at the core of most benefits achieved through the use of Data as a Service. Achieving the proverbial “single point of truth” when talking about data quality can be easier with a DaaS-based architecture.
Pricing Models for Data as a Service
A wide variety of pricing models exist to support DaaS platform offerings for both private and commercially available data. These models are relevant whether a firm is a consumer of DaaS or one who provides data to others through a service.
Tiered access to data appears to be a popular component for Data as a Service pricing models. The tiers fall in to two major categories: volume-based pricing and data type pricing.
Volume-based pricing normally includes options for both pay by each instance of data access as well pay by the quantity of data consumed. A lower-tier pay by the instance option is generally a better choice for companies with smaller data needs. Pay by quantity allows for a certain volume of data per day, with overage charges coming into play if that daily limit is exceeded. Higher tiers with unlimited data options are also available.
The data type pricing model features tiers essentially based on the number of fields returned in a query. For example, a DaaS offering would charge more for business data that included a company’s location data, a list of officers and historical stock prices versus one that only provided the location data.
Some more complex pricing models also combine both data type and volume-based features. Ultimately, it depends on the complexity of the underlying data model as well as the level of demand for the data itself.
Infochimps Leverages Big Data and the Cloud in their Service Offering
Infochimps continues to garner a reputation as a leader in the Data as a Service space. They offer a highly scalable cloud-based platform that combines a customer’s own data sources with the Infochimps Data Marketplace which offers information from an array of public and proprietary data sources.
The company builds their solutions using a combination of their own internally-developed software and open source apps tied together with what they promise to be a revolutionary approach. They are experts in Hadoop and MapReduce, offering little problem in scaling customer solutions to Big Data levels.
In addition to ingesting and managing massive amounts of data, Infochimps’ software provides state of the art analytical capabilities offering their clients the ability to make data-driven business decisions in real time.
Infochimps offers a robust documentation repository for their API, allowing clients to come up to speed quickly. Their current customer base includes eCommerce pricing house Black Locus, the shopper analytics firm, Runa, and the mobile coupon shop, Koupon Media.
IO Looks to Become a Leading DCaaS Provider
As mentioned earlier, sometimes DaaS offerings, especially the ones focused on infrastructure, are called Data Center as a Service. IO is a company poised to become a leader in providing DCaaS to customers.
They recently announced the awarding of a contract by insurance wholesaler Myron F. Steves and Company to provide IO’s DCaaS product delivered out of their Phoenix-based data center. The company also is providing disaster recovery services, with a VMware-based virtual solution, serving as a secondary data center for the insurance company:
“We made a major shift in our disaster recovery strategy, moving from an outsourced service to a VMware solution where our production systems failover to a backup location,” said Tim Moudry, Associate Director of IT, Myron F. Steves and Company. “We are confident with IO that our secondary site will always be on – so if we need to use our disaster recovery systems, we will have the data center infrastructure ready to support our business.”
IO’s Phoenix data center is the largest in the United States that holds the Uptime Institute’s Tier III Design Certification. The company is able to rapidly implement deliverable DCaaS solutions to consolidate data centers, provide Cloud Computing, as well as Myron F. Steves’ disaster recovery program enhancement.
Hoover’s Remains a Progenitor in Commercial DaaS
When looking at commercially provided Data as a Service products, Hoover’s stands out as one of the first in the industry. The company is a subsidiary of Dun and Bradstreet, and they have been offering corporate data to clients on the internet since 1993, which almost predate the World Wide Web itself.
Their service offerings include targeted lists and corporate datasheets used by salespersons worldwide, customer demographic data suitable for marketing companies, financial metrics combined with analytical information for the research community, and competitive market analysis desired by business entrepreneurs.
The company prides itself in data quality, and considering their status as “first in the industry,” their technical infrastructure supports their data management and stewardship initiatives. Hoover’s maintains data on nearly 70 million companies and almost 90 million individuals.
Being owned by Dun and Bradstreet gives Hoover’s access to D & B’s monthly feed of corporate data, so customers are able to feel secure with the timeliness and accuracy of Hoover’s DaaS product.
Data as a Service involves three main areas of interest: providing commercially available data through some form of interface, wrapping up a firm’s own data through a service layer, and data center infrastructure services offered as a product. Some DaaS systems even offer a mixture of the three. Whatever the flavor, expect this trend of data service commercialization to continue to grow in relevance to the data professional.