Cloud-based technology is becoming increasingly complex, and so the as-a-service (aaS) space has, is, and will become increasingly crowded. In fact, it’s getting harder and harder for data professionals to keep track of each Cloud computing model, and how they all differentiate from one another. The reality is that this isn’t as much of a problem as it is an opportunity for data professionals to educate themselves and adapt to new technologies that really make life easier on the Data Management level.
Arguably, Data-as-a-Service (DaaS) is one of the few new kids on the Cloud computing model block to actually deliver on the promise to make life easier. In this article we’ll take a look at the DaaS model, and how it is making an impact.
The big picture idea behind the DaaS model is all about offloading the risks and burdens of Data Management to a third-party Cloud-based provider. Traditionally, companies 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 Cloud computing model, data is readily accessible through a Cloud-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.
Benefits of Data-as-a-Service
The same benefits that come with any major Cloud-computing platform also apply to the Data-as-a-Service space. The main exception for DaaS providers is that their benefits reach for and are deep into the world of Data Management.
- Agility: Due to the fact that the majority of today’s DaaS providers are based on Service Oriented Architecture (SOA), there is a great deal of flexibility attached to accessing business-critical data in a DaaS-powered Cloud service. Data can be accessed quickly because the architecture where the data is located is fairly simplistic. This also means that as the data structure needs shift, or geographical needs arise, the changes to data are incredibly easy to implement.
- High Quality Data: One major benefit has to do with improved Data Quality. This is largely due to the fact that the bulk of data access is primarily controlled through the data service itself. This adds a robust layer of security and improved data quality.
- Cost Effectiveness: Just like any Cloud-based offering, providers in the DaaS space can easily deploy their data delivering applications in a way that off sets many of the costs associated with managing and housing these complex data sets in-house. For instance, one way providers help organizations save money is on the presentation layer of their interfaces and applications. They can build them in such a way that makes it easy to change location-based and organizational assets in a fluid way.
- Privacy: Privacy is among the most common criticisms of DaaS providers. The idea is that as soon as any organization chooses to share and deploy data outside of the walls of their own organization, privacy quickly moves to the forefront of any conversation surrounding Data Management. In a DaaS environment, privacy concerns are significant because the data that is being shared is deep, and more often than not, tied to mission-critical applications that directly affect key business processes within an organization.
- Security: Security has always been a deal breaker for many industry analysts. These same security concerns apply to the DaaS space. While from an operations perspective the easy access of data in a DaaS environment is a good thing, from a security perspective this can potentially leave business-critical datasets open to a variety of vulnerabilities. As more and more organizations move to adopt DaaS as a viable option, robust security plans will need to be put in place to avoid major security breaches.
- Data Governance Issues: In theory, due to the nature of DaaS clouds any integrated datasets get a boost in quality. But Data Governance must rely on more than the nature of a Cloud platform to ensure high levels of Data Governance. The integrity of data in the DaaS environment must be tested and checked to ensure that it harmonizes with any other data. Verification on this level can be tough to implement, but it’s an essential component needed to ensure that you’re meeting data quality standards within your organization.
The Future of DaaS: Business Intelligence & Healthcare
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.
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 Cloud-based solution, there is some convincing that needs to happen before a full-scale DaaS adoption can take place. For starters, every organization from the top down must be convinced of any DaaS provider’s inherent value. 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. This hinges on whether or not the value of DaaS solutions can be clearly communicated and understood throughout your organization.