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The depth of data fabric: Deriving deep insights from small and large data sets with scalable AI

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Amid the grave consequences of the covid-19 pandemic, there have been some positives as well. The race for rapid digitization can be considered as an indirect consequence of the covid-19 pandemic. This race for rapid digitization has proved beneficial for online services,  e-commerce, and retail firms. Needless to mention, the boom in online services and digitization processes has changed the way data is processed and analyzed. Credit should be given to the larger contribution from data science that has significantly contributed to the process of analytics. In addition to this, data science courses online have brought the process of analytics to the workstation of common businesses. Thus, it has become possible to derive deep and scalable insights even from localized data at a small level. The depth of data fabric: Deriving deep insights from small and large data sets with scalable AI

The depth of data fabric

The large streams of data have led to the formation of a composite data fabric whose complexity is bound to increase in the near future. The cohesion of structured and unstructured data, the existing data lakes, and other data warehouses form critical components of the data fabric. Data fabric provides a single interface to carry out analytics of the highest level. It enables the provisioning of various data sets, thereby revolutionizing the process of data governance.

Scalable AI

Scalable artificial intelligence has made the process of data analytics very easy and intriguing. Artificial intelligence facilitates analytics from very large data sets and even the smaller ones. The goal is to analyze customer behavior and appropriate various kinds of customer recommendations on the basis of customer activity. It is an established fact that accurate data analytics can be carried out only when we are provided with very large data sets. At a localized level, the process of analytics is difficult due to data anomalies arising out of missing values in certain attributes. With the help of artificial intelligence, such anomalies can be addressed and we may be able to carry out analytics even with smaller data sets with a high degree of precision.

The storage scenario

The scenario of data storage became problematic for large businesses during the first decade of the 21st century. With the advances in cloud computing, the horizons of big data analytics were enlarged. Due to the addressing of the storage concerns in the cloud computing environment, the process of analytics grooved at a rapid pace. A Gartner report predicts that more than 89% of the analytics services will be carried out through the cloud computing environment by the end of 2022. This would give a new lease of life to the process of augmented analytics in the times to come.

The road ahead

In the future, the process of analytics would continue to grow with the expansion of the data fabric. As well as the popularization of cloud computing services. In the age of big data, concerns like data privacy and data governance will have to be addressed. To allow analytics to chalk out its own course of action.


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