With more and more organizations addressing the cloud to modernize their infrastructure and workloads, data as a service, or DaaS, is becoming an increasingly popular and indispensable solution for data integration, management, storage, and analytics

Let’s go with the first question, What exactly is data as a service?

Data as a service or DaaS to be more precise, is a data management strategy that utilises cloud via network connection for delivering data storage, integration, processing, and analytics services. DaaS remains analogous to software as a service or popularly known as SaaS which is a cloud computing strategy that involves delivering applications to end-users over the network replacing the need to run applications locally on their devices. DaaS outsources most of the operations such as data storage, integration, and processing operations to the cloud, similar to SaaS removing the need of installing and configuring software locally.

The primary aim of DaaS is to leverage data as a business asset for better business agility. Just as any other ‘As a Service’ model, DaaS also helps in managing large amounts of data that every organization generates each day and delivers all the valuable information and enables such organizations to make data-driven decisions.

While the SaaS model has been popular for more than a decade now, DaaS is a concept that is only now beginning to see widespread adoption. That is partially due to the fact that generic cloud computing services weren't initially designed for handling massive data workloads; instead, they catered to application hosting and basic data storage (as against processes of data integration, analytics, and processing). Processing large data sets via the network was also difficult within the earlier days of cloud computing, when bandwidth was often limited.

However, with the birth of low-cost cloud storage and bandwidth, combined with cloud-based platforms designed specifically for fast, large-scale data management and processing, has made DaaS popular and beneficial as SaaS in the present era. Additionally, the Data as a Service architecture includes a range of data management technologies like data services, self-service analytics, data visualization and data cataloguing.

Who & When of DaaS

As discussed, DaaS is one of the most convenient and cost-effective solutions for customer- and client-oriented enterprises. As an example, Fidelitone, a major supply-chain and logistics management company, employed ARI's DataStream DaaS solution to deploy parts catalogs into the customer channel. It is often employed by various business teams and departments to enhance processes, including the following:

Also, a few other samples of DaaS providers include Urban Mapping, a geography data service, provides data for patrons to embed into their own websites and applications.

This case-study is a see-through live example of how Data turns out to be gold-mines in the DaaS industry.

Besides being a beneficial tool for the companies to improve the agility of knowledge workloads, reducing time-to-insight, and increasing the reliability of data and compared to on-premises data storage and management, DaaS provides several key advantages.

The following are the benefits of DaaS:

Minimal setup time: Organizations can begin storing and processing data soon employing a DaaS solution.
Greater flexibility: DaaS is more scalable and versatile than the on-premises alternative, since more resources are often allocated to cloud workloads instantaneously.
Automated maintenance: The tools and services on DaaS platforms are automatically managed and kept up-to-date by the DaaS provider, eliminating the necessity for end-users to manage the tools themselves.
Smaller staff requirements: When employing a DaaS platform, organizations don't need to maintain in-house staff who concentrate on data tools or rely on data management solutions. These tasks are handled by the DaaS provider.
Improved functionality: Cloud infrastructure is a smaller amount likely to fail, making DaaS workloads less susceptible to downtime or disruptions.
Cost-saving: Data management and processing costs are easier to optimize with a DaaS solution. Companies can allocate just the proper amount of resources to their data workloads within the cloud and increase or decrease those allocations as needs change.

While DaaS offers many benefits, it also gives birth to a few challenges

Unique security considerations: Because DaaS requires organizations to maneuver data into cloud infrastructure and transfer data over the network, it can create security risks that might not exist if data remained on local, behind-the-firewall infrastructure. These challenges are often mitigated using encryption for data in transit.
Additional compliance steps: For a few organizations, compliance challenges can also arise when sensitive data is moved into a cloud environment. This doesn't mean that data can’t be integrated or managed within the cloud, but simply that companies subject to special data compliance requirements must make sure that they meet those requirements with their DaaS solution. For instance, they'll have to host their DaaS on cloud servers located during a specific country so as to stay compliant.
Potentially limited capabilities: In some cases, DaaS platforms may limit the amount of tools available for working with data. Users are ready to work only with the tools that are hosted on or compatible with their DaaS platform, instead of having the ability to use any tools of their choice to find out their own data-processing solutions. Choosing a DaaS solution that gives maximum flexibility in choosing tools mitigates this challenge.
Data transfer timing: Transferring large volumes of knowledge into a DaaS platform can take time thanks to network bandwidth limitations. Counting on how frequently your organization must move data into a DaaS platform, this might or might not pose a significant challenge. If data bandwidth is restricted, data compression and edge computing strategies can help to accelerate transfer speeds.

How DaaS Providers Simplify Complexity

Analytical analysis doesn’t just sound complex, it's actually complex, which is perhaps why numerous people prefer to keep their heads within the sand about the way to wrangle all the info that’s available to them. The more data you've got at your disposal, the more important advanced analytical analysis becomes. And yet, interpreting data is simply one challenge. Before businesses can even get thereto obstacle, there are other hurdles they need to overcome first.

The recent study by Retail Systems Research surveyed retailers worldwide and found that a lot of organizations struggled to tap into the info sets at their disposal. the rationale wasn’t solely that they didn’t have the expertise (although that was the case for 46% of respondents). 56% of respondents said it had been because they didn’t have the bandwidth, 38% said their analytical engines couldn’t handle everything coming in and 25% didn’t have anywhere to place the info once it had been collected. Those aren’t analytical hurdles but are hurdles in operations and infrastructure which are surprisingly common among companies striving to gather and crunch data independently.

Also, considering the increasingly sophisticated nature of data security threats of today, it is essential that security must become the top priority for any DaaS implementation. In return, this would also ensure proper data governance, privacy and other data quality controls extensively with the new components. So keeping this in mind, every data asset must be well-documented and located in the system.

Also, here’s another absorbing write-up on Data Apps for you to look through and savor!!

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