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artificial intelligence machine learning big data automation the coming up of Big Data has brought great changes in the digital landscape and organizational operations. By the increased analytics the rise of Big Data problems were successfully alleviated by it. With the coming up of new trends in the market, the technologies are getting more refined. Automation of Big Data is the most troublesome technology which is totally changing the dominion. The main element of Big Data lies in finding out the patterns which actually have some projecting values. It is then developed by automated processes that help in identification of some particular ‘data features’. These features then assist in building a predictive investigation of the database.

Statement by different researchers:

As stated by the researchers from MIT Big Data Analytics test was conducted. It was done by eliminating the human aspect from its dispensation. The first of its kind called Data Science Machine was put into practice in quite a few Data Science contests where this automation actually performed way better than its human competitors. The accuracy level which was found in the test was 96%. Amusingly, where humans took a lot of time actually months for decoding their prediction algorithms, the machines just did the same process in few hours. This research basically focuses on automating the Big Data Analysis which consisted of preparing specified data and identification of problems which could be solved by the analysis.

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What are the benefits of Big Data Automation?

From the automation of Big Data Analysis, a great number of organizations have achieved benefits. According to the technology applied any amount of Big Data can be processed, analyzed and understood within few weeks. In all these aspects, automation has provided additional benefits such as reduction of the operational costs, improving operational competence, improved self-service modules, and increased the scalability of Big Data technologies. For example in the e-commerce business, it can work as a numerical identifier booming across the data tables. Even to find the features which have linked values, it looks out for categorical data.

What function does Automation Play?

An International Conference on Data Science and Advanced Analytics were conducted at the Institute of Electrical and Electronics Engineers (IEEE) in which this model was focused. Observations over there were made through time-varying data. These predicted observations will be used for futuristic predictions. Considering this extensively, the role which is to be played by automation heavily depends on the following four things:

1. Study of Time-Varying Big Data:

The conducted study should actually be working on a basic framework for finding any volume of data over a considerable period of time. The categorization of Analytics into diverse segments sheds light on a pragmatic approach. These segments are actually labeling of data, its separation according to the appropriate time periods etc. Even the recognition of data features is addressed in it.

2. Function in Data Preparation:

For Predictive Analytics, the time taken is actually reduced by the automation. According to the Data Scientists who are working on this process, it is a complex challenged. Thus it needs a robust language that makes the identification of prediction problems easier and rationalizes the analysis process. Also, a tailored framework is required which can automatically work with diverse specifications for analogous acts of classification and data labeling.

3. Finding out the Prediction Features and indicating them:

The representation of data is the key role to be played by automation in a measurable format. Towards the enablement of analysts, it can work as a big leap which will help in finding out the main prediction problems in a uniform format. This will help in its sharing and analysis. Due to this, there will be an increase in collaborations between Data Analysts and domain expert. Even for the specification the problems, the expert will be able to lead and bring to use the language which is used for automotive forecasted analysis. Even there will be an increase in accuracy in the process.

4. Self-Service Model to be produced:

For every business owner, this actually has a direct link to the openness of automated Analytics. There is deep insight of data in real time due to growing influence of Cloud Computing. It leads to a reduction of cost by making easy the access of conventional Business Intelligence and Cognitive Computing Analytics. The planning support which is present in the form of Data Lakes and data preparation platforms also hold the self-service movement. Nevertheless, the use of automated data should only be done through the secure platforms. If the policies are reinforced by using Semantic data processing it will help the governance at the time of syncing data with business-critical information. The security should focus on covering the aspects of authentication, control, audit, and architecture.

Summary:

In the road to improve Data Science in the looming time, the automation of Big data Analytics is quite a giant step. It has actually facilitated the businessmen in leveraging its numerous factors without going into complexed zones as it is a self-service model.  The Big Data has actually turned out to be more usable and cost-effective. To the more, it even helps the Data Scientist to focus on their deep competencies in spite of getting involved in time-consuming acts of the data analysis.

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