Data-as-a-Service (DaaS) models grant more flexibility for data dependent enterprises. Because traditionally data storage, management and analytics software were packaged together, businesses were subject to vendor-lock-in. To overcome this and allow access to locked up data, businesses began bridging their disparate systems using enterprise application integrations (EAI). Quickly, the realization that if data, which can be formatted to be interoperable, was separated from the analytical function, tremendous efficiencies could be had by the data using enterprises.
Since data collection has become a standard business function, DaaS development has led to the functionality of buying, selling, and even trading soft-copy data. Analytical software can work on commodity data, extracting actionable insights separately from actual data storage. This has connected DaaS to an interesting field called Data Marketplaces, because data can be valuable to more than those who collect it. In these cases, data can be sold or “exchanged” for other valuable data.
Today, this capability has become useful for enterprises that know what data sets they need. For example, consumer sentiments demand more personalization from brands, but consumers also don’t want to disclose their information. Enterprises can tap into data marketplaces and find the data they need, such as behavioral or demographic data, to augment the data in their systems. In fact, many DaaS companies target and only sell to other technology companies, universities, and nonprofits.
DaaS also serves as the foundation of a Big Data Business Model. Conceptually, data is intended to inform insights. Moving up the Big Data Business Model pyramid demonstrates the increasing value and effort going into data to first extract “informational awareness” from data, and then ultimately “answer awareness” which provides specific answers to questions, such as predictive analytics.