The process by which data is processed for deep analysis is called Data Analytics. In other words, Data Analytics is an automated process that allows different organizations to derive meaningful insights out of data for increasing their revenues and optimizing their growth. With the expansion of technological resources, the data sources are also subjected to constant expansion. As such, the data generated by these data sources is increasing by leaps and bounds. The objective of Data Analytics is to not only analyze this data and process it for structured output but also to visualize it in a manner so that the audience can interpret it.
Data sharing by data fabric
The platform that enables the sharing of data in a seamless manner is called a data fabric. One of the most important features of data fabric is that it allows the integration of data across a distributed network. This platform also serves as a customized channel for providing reusable data services. In other words, data fabric is similar to a data pipeline that plays the role of orchestrating data in a spontaneous manner. In this way, data fabric functions similar to an application programming interface and serves as a crucial link between the data warehouse and a data analyst.
Another important feature of data fabric is that it enables us to access various types of segregated data sets using a single environment. This also is helpful as it helps us in eliminating data loss. Other important advantages of a data fabric include maintenance of data quality and elimination of multiple data tools.
Real-time intelligence or continuous intelligence
Continuous intelligence or real-time intelligence is becoming increasingly popular in the last few years. The popularity of continuous intelligence is due to the integration of technologies like cloud computing, big data analytics, and machine learning. This is supplemented by seamless data integration with the help of the internet of things. Continuous intelligence enables us to mine huge data sets in a short span of time and derives valuable insights. This is especially useful when we have to deal with various types of business analytics courses. After we are able to derive insights from the data pipeline, we can make useful decisions and forecast various trends. Thus, real-time intelligence or continuous intelligence is pivotal for decision sciences.
Continuous intelligence or real-time intelligence finds applications in logistic sectors like airlines, roadways, and railways. It also finds applications in other domains where data is subjected to large fluctuations. This is true for sectors like stock markets where buying and selling of shares need inputs from continuous intelligence.
Blockchain technology
Blockchain technology is known to the general public due to cryptocurrencies that are becoming popular. However, blockchain technology is not limited to cryptocurrencies alone. It has other applications as well. For instance, blockchain technology can be put to use for the long-term maintenance of land records. It can serve as a ledger of records for fragmented pieces of land. In addition to this, periodic updates are also easily possible in the ledger of records with the help of blockchain technology. Not only is a blockchain maintained record difficult to tamper with but it is also less susceptible to any malfunctioning. Blockchain also helps in enhancing our predictive analytics abilities and prevents the circulation of false information. If a miscreant attempts to tamper with any of the records maintained by blockchain, he will have to make corrections in all the blocks from the very beginning.
The integration of blockchain with data science is pivotal for a large number of applications. These applications not only add to our analytics courses but also help us to derive useful insights in the long run.
Augmented analytics
Augmented analytics is a more structured and programming way of deriving insights from unstructured and segregated data sets. Not only does augmented analytics promise a lot of automation capabilities but also helps in deriving actionable insights. Augmented analytics has not only supplemented Artificial Intelligence and machine learning techniques but has also aided the process of business intelligence. In one word, the need for augmented analytics in data science has become extremely crucial in the last few years.
Concluding remarks
There is no doubt in the fact that the entire gamut of Big Data Analytics has undergone a paradigm shift in the last few years. This can be attributed to the various technological breakthroughs that we have witnessed in this field. Nevertheless, continuous research can prompt us for further technological development in the future.
