The emergence of Big Data largely changed the digital landscape and organizational operations. The disparity of data was effectively mitigated by its intensified analytics. With the evolution of newer trends in this realm, the technologies are getting polished. Automation of Big Data is the most disruptive technology changing its dominion completely. The power of Big Data lies in finding the patterns having some predictive values. It is further enhanced by automated processes that help in identification of some specific ‘data features’. These features help in making a predictive analysis of the database.
What Research Has To Say About This?
Recently, the researchers from MIT conducted the test of Big Data Analytics by removing the human factor from its processing. The prototype called Data Science Machine was implemented in several Data Science contests where this automation performed better than its human competitors. Its level of accuracy was up to 96% in this test. Interestingly, while humans took months for decoding their prediction algorithms, the machines did the same in just few hours. This research aimed at automating the Big Data Analysis includes preparation of specified data and identification of problems that can be resolved through this analysis.
Benefits of Automation
Leading organizations have benefitted from the automation of Big Data Analysis. Depending on the technology applied, it can take only a few weeks to process, analyze, and understand any amount of Big Data. In all these regards, automation has added benefits like reducing the operational costs, improving operational efficiency, enhanced self-service modules, and increased the scalability of Big Data technologies. For instance, it can function as a numerical identifier thriving across the data tables in e-commerce business. Also, it looks for categorical data to generate the set of features having interrelated values.
What Role Automation Can Play?
At the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Data Science and Advanced Analytics, this model focused on making observations through time-varying data. These observations were anticipated to be used for futuristic predictions. Broadly speaking, the role to be played by automation heavily relies on the following four things:
Analysis of Time-Varying Big Data
The automated Analytics should ideally focus on a basic framework for analyzing any volume of data over a period of time. The categorization of Analytics into different segments reflects a pragmatic approach. These segments are labeling of data, its division according to the relevant time periods, and identification of data features to be addressed.
Role in Data Preparation
This automation should reduce the time taken for Predictive Analytics. It is a complex challenge faced by the Data Scientists working on such projects. Hence, it requires a robust language that simplifies the identification of prediction problems and streamlines the analysis process. Also, it entails a tailored framework that can automatically work with varied specifications for analogous acts of categorization and labeling of the data.
Detecting the Prediction Features and Representing Them
The representation of data in a measurable format is the key role to be played by automation. It can work as a big leap towards enablement of analysts in identifying the main prediction problems in a standardized format. This will facilitate its sharing and analysis. As a result, collaborations between Data Analysts and domain experts will increase. The experts will be enabled to learn and use the language used for automated predictive analysis for specification of their problems. It will bring more precision in the process.
Proliferation of Self-Service Model
It is directly related to the accessibility of automated Analytics for every business owner. The growing influence of Cloud Computing offers deeper insights of data in real-time. It reduces the costs by facilitating the access of traditional Business Intelligence and Cognitive Computing Analytics. The architecture support in the form of Data Lakes and data preparation platforms also support the self-service movement. However, any access to automated data should be granted only through the secure platforms. Reinforcing policies by using Semantic data processing can also facilitate governance at the time of syncing data with business-critical information. The security should be granular and layered to cover the aspects of authentication, control, audit, and architecture.
The automation of Big Data Analytics is a huge step in the direction of improving Data Science in the imminent times. The self-service model has facilitated the business owners in leveraging its various factors without digging deeper into its complexities. The Big Data has become more accessible and cost-effective. Moreover, it allows the Data Scientists to concentrate on their core competencies instead of indulging in time-consuming acts of data analysis.