Data analytics engines and cloud-hosted applications drive industries to help them stay ahead of the game with business insights. But in a world where almost every business leverages these technologies to its fullest potential, the line that represents the competitive edge through data analytics may be fading. However, the transformation of data analytics into a platform-based setup may be its next step of evolution. Data Analytics as a Service (DAaaS) offers businesses a compact, highly customizable, and economical approach to data analytics, and here’s why it is the next big thing in data analytics.
Traditionally, operating data analytic processes required a sizable team of data experts (data engineers and scientists). The DAaaS approach takes business owners away from the ‘one-size-fits-all” approach and leads them to a marketplace-based approach where they can choose data analytics services based on their specific needs. Despite being simple, DAaaS platforms also provide ample functionality to fit the needs of more complex aspects of data analytics such as data science.
The Strategic Advantages of DAaaS
Most businesses utilizing data analytics engines heavily invest in sizeable centralized infrastructures, such as storage repositories. They also employ highly qualified professionals to drive the setup. A DAaaS setup enables organizations to harness advanced analytics capabilities without feeling the pinch of escalating expenditure.
In other words, the DAaaS approach enables small and midscale businesses to leverage the potential of data analytics to make business decisions. The following are the strategic advantages that DAaaS platforms provide to business owners.
Smaller Internal Infrastructure: Big data analytics installations usually inflate the capital and operational expenditures at the outset. All the organizations leveraging DAaaS platforms can bank on the platform provider completely without breaking a sweat about Return on Investment (ROI). The sheer flexibility, customizability of DAaaS platforms ensure that companies don’t have to incur hefty subscription, maintenance and upgradation costs associated with big data analytics service providers.
The Rent Advantage: Business organizations can explore the marketplace setup of DAaaS providers to pay for data analytics application services that they specifically require for a stipulated period. The convenience of choosing among specific analytics services enables business organizations to forecast costs by reviewing the service offering from the DAaaS provider catalog.
Improved Decision Making: More often than not, business organizations testing the waters for data analytics services have a steep learning curve to walk through. The time and effort required to overcome the learning curve often dilutes the capacity of data experts to extract relevant business insights, especially in the case of small-scale business organizations. The simplicity of DAaaS platforms and the freedom of choice they offer through the marketplace alleviates the barriers associated with learning which eventually leads to better decision-making.
Minimized Expertise: One of the most significant barriers of entry for business organizations seeking data analytics services is the lack of qualified data experts. While DAaaS implementation requires a team of data experts to function, they don’t need to be as many as a current big data analytics operation requires.
Real-Time Analytics: Currently real time analytics is an uncharted territory. The limitations of real time analytics arise from various industry specific challenges such as data quality, performance of applications, cloud subscription plans and more. DAaaS steers data analytics towards a ‘real-time ‘approach by optimizing the analytics engines with the cloud service providers. The resultant lightweight nature of applications on the DAaaS platforms enables businesses to achieve real-time analytics at a lower cost.
The Top 5 Domains that DAaaS Can Empower
DAaaS enables businesses to leverage a subscription plan that usually offers fully customizable analytics applications and presents data that are easy-to-understand even for non-IT professionals to gain insights.
Here are some popular use cases that are bound to make an impact on the industry type:
Predictive Maintenance: Business operations such as warehouse management, logistics, etc. utilize a plethora of in-service equipment that requires periodic maintenance. Preventive maintenance allows the convenient scheduling of maintenance procedures. DAaaS platforms serve as a powerful conduit to achieve predictive maintenance and specific short-term analytics, without the need to deploy complex in-house solutions.
Retail: Retail operations can reap benefits big time from analyzing customer data from their mobile devices, physical interactions (captured through surveys and opinion polls) and internet activities. DAaaS can extract valuable customer service insights from incoming customer data. It enables retail management organizations to understand consumer behavior and create customer personas to align products and services with specific customer demographics.
Manufacturing: To optimize production and employee productivity, it is imperative for business owners to tap into the right data stream and utilize the ideal data analytics application to derive insights. DAaaS platforms give manufacturing business owners the power of choice based on requirements, business types and budget for investments. DAaaS platforms can integrate seamlessly with fabrication machines, and production lines and create an ecosystem of optimal production.
Infrastructure Development Cost: DAaaS platform providers do most of the heavy lifting for data engineers by providing a variety of reporting and visualization tools with versatile connectors. All that the data engineers at a client organization must do is to align the infrastructure with the business goals. The extreme flexibility of DAaaS platforms significantly reduces the cost of setting up analytics operations.
Finance: In the world of finance, the pace of information and its accuracy are the two main ingredients of success. Achieving the expected analytics performance with the traditional approach to data analytics was an expensive affair with cumbersome one-time deployment commitment. DAaaS platforms reduce the effort required needed in establishing compact and high-performance business environments even for small-scale finance operations.
Conclusion
While the advantages of DAaaS platforms are many, enterprise application development companies and data analytics service providers are only getting started with developing them. DAaaS platforms require an ideal blend of big data analytics and cloud engineering expertise. At Nalashaa, we have over 10 years of experience in developing and deploying cloud and data analytics applications for our clients.
