Today more and more organizations are turning to cloud integration to modernize their operational infrastructure and workloads, data analytics as a service, or DAaaS, is emerging as an increasingly popular solution for data integration, management, storage, and analytics. By embracing DAaaS, companies can enhance the agility of their data workloads, reduce time-to-insight, and boost the reliability and integrity of their data.
Let's explore what DAaaS means and why companies are leveraging DAaaS-based strategy for integration, storage, and management of their data.
Data Analytics-as-a-Service (DAaaS)
As the name suggests, data analytics as a service, or DAaaS, is a platform that enables organizations to process huge amounts of data. Incorporated with a cloud-based delivery model, DAaaS is available with cutting-edge tools for data analytics. It enables the users to modify or configure based on their unique objectives and requirements.
Considered a significant data management strategy, in Data analytics as a service (DAaaS), businesses can use the cloud to deliver data storage along with integration, processing, and analytics. Just like software as a service, or SaaS, DAaaS functions on a cloud computing strategy to deliver applications to end-users.
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Data Analytics as a Service (DAaaS): How Does it Work?
With organizations finding the perfect DAaaS, they are integrating their enterprise data into the platform. This enables them to organize this massive amount of information and access the analytical insights generated. It also helps in arranging the analytic data workflows established by employing a vast collection of services, a majority of which are based on machine learning.
The DAaaS approach helps businesses to move away from the ‘one-size-fits-all” approach and integrate a marketplace-based approach, empowering them to choose data analytics services based on their specific needs. DAaaS platforms also provide businesses with ample functionality to fit their requirements of more complex data analytics like data science.
What are the Challenges of Data Analytics as a Service (DAaaS)?
While choosing DAaaS has excellent features, it can sometimes pose challenges that can emerge as a hindrance for organizations, primarily due to the cloud environment.
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Security
Today, security remains one of the biggest challenges organizations must deal with. DAaaS mandates organizations to move data into cloud infrastructure and transfer data over the network. This could create security risks for data theft. It is due to this reason that some companies are hesitant to move their data to the cloud. But it depends on the DAaaS provider. Therefore, it is vital to choose a platform that offers high-quality and proven services. However, these challenges can be mitigated by employing encryption for data in transit.
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Accessibility & Scalability
Access to advanced analytical capabilities is emerging as a major concern for organizations as there is a rising need for major investments in software. Organizations are scaling their services to cater to the rising demand. This flexibility will help foster rapid responses to new opportunities, thereby scaling operations.
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Complexity
Data analytics plays a vital role in business success. While data analysis is complex, it requires the right expertise and knowledge of a professional. On the contrary, the absence of qualified data scientists in a business is a significant contributing factor to unsuccessful projects. And as a business, if you are planning to move to DAaaS, offering your employees internal training for analytics tools is something that should not be overlooked, even if it requires additional costs. Employee training will not only help in reducing expenses but also will ensure a higher return on investment.
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Data Management
The analytical workflow can often be demanding for any business. It comprises multiple steps requiring the right talent, from data acquisition to data modeling and visualization. While outsourcing these responsibilities, organizations should not underestimate the benefit of training their existing talent. Constant employee training will help contribute to long-term business success.
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Additional Compliance
For some organizations, compliance challenges are likely to arise when sensitive data is transferred into a cloud environment. But this does not imply that data cannot be integrated into the cloud. Companies can opt for special data compliance requirements to ensure that the selected DAaaS platform meets the requirements while staying compliant.
Data Analytics as a Service (DAaaS): Strategic Advantages
Many businesses are investing in data analytics engines heavily in sizeable, centralized infrastructures like storage repositories. Organizations are also employing highly qualified professionals to drive their data ecosystem. A DAaaS setup equips them to harness advanced analytics capabilities without cutting down on other expenditures. With a DAaaS approach, small and midscale businesses can leverage the potential of data analytics for informed decision-making. Let's uncover some of the strategic advantages offered by DAaaS platforms to businesses.
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Flexible Internal Infrastructure
Big data analytics installations often inflate the capital and operational expenditures of an organization. With the majority of organizations leveraging DAaaS platforms, they are banking on the platform provider without breaking a sweat on their return on investment (ROI). The sheer flexibility of DAaaS platforms helps in ensuring that companies do not have to bear hefty subscription, maintenance, or upgradation costs associated with data analytics service providers.
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Enhanced Decision Making
Many organizations today are testing the waters for data analytics services. They are embracing a steep learning curve to walk through. However, the time and effort required to overcome this learning curve can dilute the capacity of data experts to extract relevant insights. This is where the simplicity of DAaaS comes into the picture. DAaaS platforms offer organizations the freedom of choice based on the marketplace requirement, thereby enabling them to alleviate the barriers associated with learning. This further helps in fostering a better decision-making environment.
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Minimized Expertise
One of the most significant barriers for organizations seeking data analytics solutions is the lack of qualified data experts in the industry. DAaaS implementation demands a team of data experts to function, and they do not need to be experts in big data analytics operations.
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Real-Time Analytics
For many organizations, real-time analytics is still uncharted territory. The limitations of real-time analytics stem from different industry-specific challenges like the quality of data, the performance of applications, or even cloud subscription plans. DAaaS helps organizations tackle their data analytics operations with a ‘real-time ‘approach by integrating their operational engines with the cloud service providers. The lightweight nature of applications offered by the DAaaS platforms helps in achieving real-time analytics at a lower cost.
Top Domains that DAaaS can Empower
DAaaS is assisting businesses to leverage a subscription plan that presents them with fully customizable data analytics applications along with data that is easy to understand and gain insights. Some of the popular use cases that can make an impact on the industry type include:
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Predictive Maintenance
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Retail
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Manufacturing
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Finance
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Infrastructure Development
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Future of Data Analytics as a Service (DAaaS)
While there are many advantages of DAaaS platforms, enterprise application development companies, as well as data analytics service providers, are just getting started with integrating and developing them. DAaaS platforms demand an ideal combination of big data analytics along with cloud engineering expertise, thereby enabling organizations to develop and deploy cloud and data analytics applications.
Taking the Next Step on the Data Analytics as a Service (DAaaS) Journey
Compared to on-premises data solutions, data analytics as a service (DAaaS) offers organizations several benefits - from easier setup and integration of cost-optimization opportunities and reliability. However, DAaaS poses some challenges, but those can be mitigated and managed with the right framework.
Some companies are already leveraging DAaaS to speed up as well as simplify their process of generating insights from data and achieve better data governance. Today, these companies are in a position to maintain an edge over their competitors as well as streamline their operations through more improved data integrity.
With a presence in New York, San Francisco, Austin, Seattle, Toronto, London, Zurich, Pune, Bengaluru, and Hyderabad, SG Analytics, a pioneer in Research and Analytics, offers tailor-made services to enterprises worldwide.
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