FREMONT, CA: Big Data can be a tough proposition for many companies as conventional tools, and on-premise techniques could be the wrong approach. A combination of big data and cloud—Data Analytics as a Service —can ease the adoption of analytics capabilities for firms. This DAaaS platform is tailored to be extensible to handle various potential use cases. Analytics as a Service (AaaS), as a general analytics solution has many potential use cases in multiple sectors.
The significant benefit of the AaaS is to lower the barrier of entry to advanced analytical capabilities, without demanding that the user commits to large infrastructures and human resources. Using AaaS may not be direct as using other SaaS software. A systematic process requires processes including exploring initial data, defining analytical procedures, implementing and validating results using test data, and optimizing the data as a new data centre. AaaS solution minimizes technical complexity if it is appropriately designed to manage a hybrid cloud model.
The whole host of data professionals are still not clear how analytics is different from analysis. Many analytics operations have gone into disarray without a clear vision behind them. With AaaS, firms can gain maturity overtime to understand its need for leadership in line with the business. The analytics space is not an extension of the traditional IT role. Analytics on premises would also call for more servers and other hardware requirements with robust IT infrastructure in place. AaaS removes all the need pertaining to what, how, and how much to build with short and long term considerations while giving a pleasant analytical experience.
However, currently, the big data is undoubtedly a business changing trend. Getting capabilities like AaaS can be, applied to multiple use cases. AaaS puts analytics as a first level element component in a new vision of enterprise computing, which enables extensive usage of the advantages of cloud technologies.