Data is becoming increasingly important for organizations around the world, it is even called the new “oil” and organizations that can mine this resource are becoming the richest in the world! However, even if organizations can collect and analyze their data and obtain some insights, it would not tell them much as this data is in siloes. Assimilation of all the data from various sources at one place is difficult and giving access to such a data repository across all stake holders is equally challenging. In addition, organizations need as much data as possible to make relevant business decisions. The important question is where should the organization get the relevant data from?
That’s where Data as a Service (DaaS) comes in. DaaS eliminates redundancy and reduces associated expenditures by accommodating vital data in a single location, allowing data use or modification by multiple users via a single update point. What DaaS does is that it creates a cloud solution where in an organization can get as much data from all the sources such as e marketing, IOT devices, transactions etc. all at one place. It allows organizations to not only obtain the desired data but also improve data analytics, reduce the time to obtain data insights, and increase the reliability of their data.
What exactly is Data as a Service (DaaS)?
As per Techopedia, Data as a service (DaaS) is a cloud strategy used to facilitate the accessibility of business-critical data in a well-timed, protected, and affordable manner. DaaS depends on the principle that specified, useful data can be supplied to users on demand, irrespective of any organizational or geographical separation between consumers and providers.
DaaS is similar to Software as a Service (Saas), a cloud computing strategy that involves delivering applications to end-users over the network rather than having them run applications locally on their devices. Just as SaaS removes the need to install and manage software locally, DaaS outsources most data storage, integration, and processing operations to the cloud.
While the SaaS model has been popular for more than a decade, DaaS is a concept that is only now beginning to see extensive acceptance. That is due in part to the fact that generic cloud computing services were not initially designed for handling massive data workloads, instead they catered to application hosting and basic data storage. Processing large data sets via the network was also difficult in the earlier days of cloud computing, when bandwidth was often limited. Today, with the start of low-cost cloud storage and bandwidth combined with cloud-based platforms designed specifically for fast, large-scale data management and processing, DaaS is just as practical and beneficial as SaaS.
Architecture of Data-as-a-Service
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Benefits of DaaS
Compared to on-premises data storage and management, DaaS provides several key advantages which include-
- Ability to move data easily from one platform to another
- Reduced downtimes or disruptions as the Cloud infrastructure is less likely to fail
- Global accessibility
- Data management and processing costs are easier to optimize with a DaaS solution
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Finally, in terms of delivering value to consumers and to entities outside an organization, it is important that DaaS platforms are utilized by enterprises as these platforms provide immediate access to data and actionable insights which the previous generations of data analytics platforms could not provide. This level of access to all stakeholders in an enterprise is what has enabled DaaS to emerge as a key enabler for strategic decision making.