Let’s talk about an emerging trend called Data-as-a-Service (DaaS). DaaS is yet one of the latest addition to the *aaS model. Along with SaaS, PaaS and IaaS.
The big picture idea behind the DaaS model is all about offloading the risks and burdens of Data Management. Traditionally, companies and agencies housed and managed their own data within a self-contained storage system. The problem with this traditional model is that as data becomes more complex, it can be increasingly difficult and expensive to maintain. With the DaaS computing model, data is readily accessible through a service-based platform. Simply put, DaaS is a new way of accessing business-critical data within an existing datacenter. Within the DaaS environment, information can be delivered to a user regardless of organizational or geographical barriers. Some of the most common business applications powered by DaaS technology includes Customer Resource Management (CRM) and Enterprise Resource Planning (ERP) applications and nearly any host of data rich environments like health systems and several other government systems as well.
Imagine a government system which housed all of its data in a common data model and then allowed for “subscriptions” to their data as a service. A practical example may be a third party that has a software solution that needs to check someone’s driver’s license data. If the driver’s license agency has a common data model for that D/L information an allows for a subscription, then the third party always has the most accurate data. Similar to the way Google Maps allows an API to gather data on map point; that is DaaS that Google is providing. The DaaS approach also creates the path for that data to be “ledgerized” and blockchained (which we talked about in a previous blog).
According to the popular IT research firm Gartner, the Data-as-a-Service model is expected to serve as a launching pad for the Business Intelligence (BI) and Big Data analytics markets. Right now, the BI market is fairly limited to what Gartner refers to as a “build-driven” business model. That is, enterprise organizations merely license software so that they can build analytics on top of that software. The DaaS phenomenon will allow companies to subscribe to data services that bundle BI and analytics applications into the software license. Like as part of BI package including health dimensions from DaaS providers. Beyond the world of basic Business Intelligence, like many other industries, the healthcare industry is rapidly adopting Big Data. As a result, the components needed to effectively manage Big Data greatly benefit from the adoption of Data-as-a-Service architecture. Some of these components include everything from Data Governance to data integrity to data storage innovations to agile information delivery architecture. The next generation of healthcare-centric data architectures will rely on a robust view of the DaaS space.
As with any new “aaS” solution, there is some convincing that needs to happen before a full-scale DaaS adoption can take place. For starters, every organization must be convinced of any DaaS provider’s inherent value from the top down. While the benefits of DaaS adoption are wide and deep, the criticism of Cloud-based data services (privacy, security and data governance) are concerning to say the least. To look at it from another angle, it’s definitely true that most IT processes can and should be measured in ROI. However, in the DaaS space, quantifying ROI can be difficult. Due to the nature of Cloud-based data sharing requires a re-imagining of IT to some degree. This is largely because, in the DaaS environment, Data Management shifts from an IT capability to a collaborative Data Management effort that moves data capability far beyond the supporting applications. This means that attempting to quantify value of DaaS based on money-savings and ROI is incredibly difficult, if not impossible.
The bottom line is that as the need for dynamic Data Management solutions increases, more and more organizations will start to consider DaaS as a viable option for managing mission-critical data in the Cloud. Any solutions that streamlines the Data Management process by synchronizing enterprise data with all internal applications, business processes, and analytical tools positions itself as a viable resource that will improve operational efficiency, while boosting the quality of reporting and data-driven decision making. Again, the future of DaaS adoption is less dependent on the technical efficiency of the Cloud computing model, and more dependent on organizational alignment.