Demystifying the role of automation in Big Data

Companies are leveraging big data to transform their digital landscape and automate organizational operations. Data disparity has been effectively mitigated with the proliferation of robust analytics. As big data trends have set the pace for automation, companies have started jumping on the big data bandwagon with the help of data analysts.

Automation of Big Data is one of the most successful disruptive changes so far.  Big data is all about evaluating data patterns that can further help map out future business strategies. When big data combines with automated processes, the identification of specific data feature becomes more streamlined.

Research so far..

Recently, MIT researchers have performed the Big Data Analytics test that does not include the human factor in its processing. The contact of several data science contests with the prototype Data Science Machine resulted in the better performance of automation than its human competitors. The level of accuracy was 96 %. The most interesting part was that machines decoded prediction algorithms in a few hours, for which humans had taken months. The aim of this experiment was to automate Big Data Analysis to evaluate specified data and solve data specific problems.

Benefits of automation

The list of companies that have successfully made predictions using big data analysis is growing, as they have taken advantage of the data science course to kickstart the process of predictive analysis.

Depending on the technology you use, you can evaluate, analyze, and understand any amount of Big Data in no time. The benefits of automation are the increased use of Big Data technologies, low operational costs, higher operational efficiency, and improved self-service modules. Moreover, it can act as a numerical identifier working throughout the data tables in different industries. It even prefers categorical data to any other type of data to develop the set of features having values related to each other.

The role of automation in Data Sciences

This model made various observations using time-varying data at the Institute of Electrical and Electronics Engineers International Conference on Data Science and Advanced Analytics. Due to the high accuracy of these predictions, these observations were used to make futuristic predictions that were highly realistic. To put it simply, the role of automation depends on four things.

Lets see them one by one

Time-varying Big Data Analysis

The power of automated Analytics is worth considering as it focuses on a basic framework and can process large amounts of data in a short time. The approach is highly pragmatic to the categorization of Analytics into different segments.  The segments include the labelling of data, its division into time periods, and recognition of data features.

Prepare data for evaluation

Automation has reduced the time taken for Predictive Analytics to a great extent. It has eliminated the complexity of the process of predictive analytics; therefore, it has helped data scientists fasten the process of data analysis.  Automation requires a robust language to trigger the analysis process by simplifying the identification of prediction problems. It takes into account a tailored framework that can deal with different types of specifications for analogous acts of categorization and data labelling.

Finding prediction features

Automation plays a key role in the representation of data in a readable format. Moreover, it empowers analysts to find the main prediction problems and find solutions to them via predictive analytics. As automation favors data sharing and analysis, the association between Data Analysts and domain experts has increased greatly. Now, experts are in the position to use the language of automated predictive analysis for solving predictive problems. This has made the whole process of predictive analysis more precise.

Self-service models

It depends on the accessibility of automated Analytics to companies. Cloud Computing provides companies with deeper insights into data in real-time. As you get access to Cognitive Computing Analytics and traditional Business Intelligence, you will be able to evaluate data faster. Data Lakes and data preparation platforms have revolutionized predictive analytics. Moreover, the use of Semantic data processing has fastened the process of syncing data with other data.

Big Data Analytics automation impacts data science in many ways. While removing complexities, this self-service model has enabled business owners to dig deep into data. Even SMBs are aware of its power and have planned on rolling out new analytics strategies to make the most of predictive analytics.

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