With Big data, big data analytics has become one of the important research frontiers. Big data analytics, the emerging technology of Big data making big changes in the way e-commerce and e-services operate and make traditional data analytics and business analytics, bring new big opportunities for enterprises. Big data analytics has become a mainstream market adopted technology broadly across industries, organizations, and geographic regions and individuals that facilitate data-driven decision making for the business.

On the other hand, Big data analytics Service Oriented architecture or BaSOA is an architecture that supports business decision making with big data analytics services. It is based on the theory of big data analytics providers, brokers, and requestors, which facilitates the understanding and development of business decision making and BI. For example, from a deep analysis of the BaSOA, an enterprise and its CEO can know who the best big data analytics providers and brokers can improve their business, market performance, and competition.

What is Big data Analytics?

Big data analytics is an integration of data analytics and web analytics related to big data. It collects processes that include data collection, organizing the collected data, and analyzing the same to discover patterns, intelligence, knowledge, and information within the big data. Similarly, big data analytics can be defined as techniques used to analyze and acquire knowledge and intelligence from big data. Big data analytics is an emerging technology and science which involves many things like –

• the multidisciplinary state-of-art information
• communication technology (ICT)
• mathematics,
• operations research (OR)
• machine learning (ML)
• decision sciences for big data

The main components of big data analytics include –

Big data descriptive analytics

Big data descriptive analytics is a kind of descriptive analytics that helps discover and explain the characteristics and relationships of entities within the existing big data. It answers the data-related queries such as what happened on data in the past and what is happening in the present and timing, i.e., when. For example, pay-per-click web analytics or email marketing data belongs to big data descriptive analytics.

Big data predictive analytics

Big data predictive analytics focuses on forecasting trends. It addresses the problems that may happen or likely to happen in the future and the reason that is why it will happen. Big data predictive analytics creates models to predict future events based on the existing big data.

Big data prescriptive analytics

Big data prescriptive analytics addresses problems such as what we should do, why we should do it, and what should happen with the best outcome under uncertainty. For example, with big data prescriptive analytics, an optimal marketing strategy for e-commerce can be provided.

To explain more, big data analytics combines –

Big data analytics = Big data+ data analytics+ Big data descriptive analytics+ big data predictive analytics+ big data prescriptive analytics

As the core parts of big data analytics, this is called the ontology of big data analytics, which means the interrelated fundamental concepts for a particular domain. Not only this, but the fundamentals also involve mathematics, statistics, engineering, human interface, computer science, and information technology, along with different modeling techniques—big data analytics, which involves current as well as historical data and their visualization.

If we dig more, this analysis and visualization include data mining (DM) and data warehousing (DW) to make a decision model. In other sens, this finds the possible relationships, anomalies, and patterns to discover information on decision making.

Not to mention, Big data analytics also uses statistical modeling (SM) to support decision making. Visualization is an important part of big data analytics, makes patterns and information for decision making in the form of a table, figure, or multimedia. So, more elaborately, big data analytics as below.

Big data analytics = Big data + data analytics + DW + DM + SM + ML+ Visualization+ optimization

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Big data analytics as a Service-oriented architecture (BaSOA)

For a long time, data and associated analytics have been structured as a single canonical form that includes massive single schemas to encompass everything. This is a rigid and monolithic approach and targets to standardize everything across the enterprise. On the contrary, SOA breaks this rigidity and monolithic approaches to systems and segregates vital components down into chunks of services. Now, considering the business's present needs, this enables data to be available for analytics as soon as it's get generated. Besides, it is unnecessary to have everything standardized across the entire enterprise; instead, only those shared data segments that matter. Additionally, SOA structure enables end-users to build their own interfaces flexibly to access the information they need without waiting for IT to build it for them. This makes sense that Big Data be available as service-oriented architecture or BaSOA as well.

Components of BaSOA Architecture

There are three main players in Big data analytics Service-Oriented Architecture –

• Big data analytics service broker
• Big data analytics service requestor
• Big data analytics service provider

Big data analytics service brokers are the entities that facilitate the development of big data analytics services, which include

• popular presses
• social media
• traditional media
• consulting companies
• university students
• scholars

and so on.

Using various methods and techniques helps to improve a better understanding of big data analytics services in general. It also provides an overview of data analytics, business analytics, web analytics, and services.

Big data analytics service requestors are the entities which include

• organizations
• governments
• All level business decision-makers such as CEO, CIO, and CFO as well as managers.
• business information systems
• E-commerce systems.

Big data analytics service requestors require big data analytics services that include

• information analytics services
• knowledge analytics services
• business analytics services

Along with visualization techniques, the above-mentioned services provide knowledge patterns and information for decision making. Big data analytics service requestors generally include decision-makers to acquire information based on analytical reports provided by big data analytics service providers.

Big data analytics service providers include

• analytics developers
• analytics vendors
• analytics systems or software and other intermediaries
• web analytics service (WAS) providers

The above-mentioned entities can provide analytics services. This can then facilitate their strategic business decision making. Analytics developers provide analytic tools with extensive data extraction, analytics, and reporting functionality. Google is a search engine provider, and a WAS vendor because Google provides Google Analytics, a big data analytics, with good tracking tools. For example, Mobile App Analytics, a part of Google Analytics, is also a mobile big data analytics services provider that helps smartphone customers to discover new and relevant users through traffic source reports. The big data analytics services providers on the Web include Amazon, Google, and Microsoft.