The convergence of Big Data analytics as a service and the Cloud promises a whole new set of useful applications for enterprises across any industry. Cloud-native analytics solutions, for example, could be easily plugged into a business’ existing landscape, seamlessly applied on top of the dataflows, and insights garnered without a flinch. Available on a pay-per-usage model, this saves businesses from the pains of deploying standalone analytics from scratch and inefficient insights. No doubt, this is a big competitive edge.
This is why modern businesses are fast adopting Data Analytics as a Service (DAaaS) to embrace an advanced, scalable analytics platform that boasts cloud-driven intelligent capabilities. Such a platform provides the full functionality of an analytical solution, from data collection, aggregation and processing, and finally to insight generation and user-relevant reporting. The following blog highlights more.
Big Picture Benefits of Using DAaaS with Examples
Businesses can gain insights such as previously unseen patterns, unknown correlations, market trends and client preferences. In recent years, technological progress and new techniques have made big data analytics a viable option. Most firms that use data analytics platforms also invest in central infrastructure, including storage repositories. They also hire trained professionals to operate these systems. However, the advantage of a DAaaS approach is that businesses can take advantage of cutting-edge analytics without the prohibitive costs.
Consider smart cities, for instance. Imagine analytics for a wide variety of municipal-provided data sources, such as the sensor networks throughout the city. A DAaaS can make it easier to deliver real-time analytics in such a scenario, city-wide. This can help the municipality take decisions on critical management activities across the city such as traffic and vehicular administration, waste management and sanitation, utility management, etc.
DAaaS makes it possible for even moderately sized companies to tap into the value of data analytics for strategic planning and operational purposes. For instance, Oil & Gas companies can implement predictive maintenance solutions for device fleets at remote locations without implementing particularly complicated solutions in-house. In the manufacturing sector, a Data Analytics-as-a-Service can use the expanding avalanche of data from linked fabrication equipment to enable efficient production while reducing waste and redundant activities.
What makes up a DAaaS Platform?
DAaaS deployments pose a significant set of hurdles because of the inherent intricacies of analytical processes. They are more akin to Platforms as a Service (PaaS) solutions rather than the more fixed Software as a Service (SaaS) architecture.
Several factors influence the internal architecture of the DAaaS and make its design a difficult task, such as:
- The need for real-time and non-real-time processing
- The nature of analytics services
- The demand for data storage and modeling
- The cloud delivery models - private, public or hybrid
However, at an architectural level, a typical DAaaS solution will have the following components.
The Analytics Runtime Environment
The underlying system that allows for specific analytical processes to be executed is what powers the actual DAaaS solution. Usually, it works through an externally facing web services/messaging interface for both, gathering information and processing it. The platform is scalable up to petabytes of data, accommodating the breadth of nuances of data analytics as a service business model.
It is crucial to have the ability to perform distributed processing so that various processing tasks can be run in parallel. A processing layer such as this mediates between the data storage and the analytic services.
Data Analytics Frameworks
While essential analytics tasks are carried out by core data analysis programs, the ultimate business-focused capabilities are delivered through various analytics applications.
The Workbench Environment
Data analytics as a service (DAaaS) share similarities with PaaS. Eventually, the runtime environment and analytics apps have to be tailored to each user's requirements. This is why most DAaaS platforms will include an interface to customize the service extensively.
Leveraging DAaaS to Build your Business and Boost Profits
DAaaS has made it possible for businesses at all levels of the value chain to pool and exchange information about their customers, within the confines of privacy and data protection. These key players in the value chain can now monetize their data.
Data analytics platform as a service has provided a value system for businesses by improving cross-departmental data collaboration for various critical business processes. Product quality, for instance, impacts every stage of the value chain, from R&D to manufacturing to logistics and supply. DAaaS has made it possible to keep tabs on the quality of any product at any point in the supply chain in real-time and work together to improve it.
In the past, gaining customer insights required finding, accessing, and analyzing the appropriate data sets. With the help of DAaaS, these organizations gain access to data analytics that are the key to making decisions. Retailers, eCommerce, and product manufacturers are some of the beneficiaries who will gain the most as a result of DAaaS.
Embrace the Future with Intelligent Insights
Imagine having access to advanced business analytics to see and predict critical data points and key performance indicators. If you have the power to track every interaction with customers, every moving part in your supply chain, and every financial transaction — anywhere in the world, how would it empower your business? Let’s take this a step further. What could you do with a data architecture that lets you process all that information in real-time or near real-time?
With a DAaaS solution, it is now possible to react to events before they happen. Your business will be able to improve safety, predict the impact of business changes, and benefit from faster financial forecasting. Imagine how access to strategic business analytics can help you to drive business innovation, open new revenue streams and create products.
The DAaaS approach brings a single source of truth to empower insight, companywide. It enables a more predictive understanding of outcomes through consistent data sources, robust analytical filters, and powerful metrics.
Whether or not you incorporate cloud computing or analytics into your plan, you must accept that both are crucial in today's technological landscape. When combined, the creativity of your employees is unrestricted by a lack of means. By analyzing, interpreting, and making data business actionable, we open the door to new avenues of innovation and competitiveness. It’s time for you to embrace the future.
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