The Line of Demarcation Between Analytics And Analysis: Exploring The Spectrum Of Research In Business Intelligence

The Line of Demarcation Between Analytics And Analysis: Exploring The Spectrum Of Research In Business Intelligence

Demarcation Between Analytics


There is no single definition of business analytics. There are many of them. One of the most prominent definitions of business analytics defines it as the convergence of all mechanisms that help in deriving business insights from unstructured data for better decision making. According to the journal of business analytics, business analytics is defined as the combination of descriptive, diagnostic, and predictive analytics of data to provide holistic coverage of the overall framework of a business structure.


The process of using complex mathematical-statistical machine learning and decision-making tools to enable solving of problems in the business domain is the aim of most business analyst courses and processes. Appropriately, the major goal of any business analyst course is to convert the unevenness of data processes into knowledge discovery processes for optimizing business performance. Other domains which are covered by business analytics and related tracks include customer intelligence, web mining, web analytics, and data computation.

Analytics and analysis

There is a prominent difference between analytics and analysis. Carrying out the analysis of a process means the critical examination of all the substructures at the granular level. Analysis is usually used for a system that does not follow a particular pattern and is difficult to simplify or decompose into substructures. On the other hand, carrying out analytics means using different types of methodologies, techniques, and tools for solving complex tasks.

Rise of analytics

The process of analytics has revolutionized the business circles in the current times. No matter what the type of business is, the role of analytics in one form or the other is bound to be there. Ranging from evidence-based decision making to probabilistic management, analytics is playing its role in businesses in one or the other form. Various types of micro, small and medium enterprises are using business analytics to develop efficient, effective and competitive products. The reliability of data and the recent advances in business technology is leading to the automation of various business processes. Of late, there has been a constant shift towards analytical and evidence-based business management. The intuition driven decision-making process has been fully replaced by data-based process. This shift from qualitative to quantitative data research has created a snowball effect in the business world.

Taxonomy of business intelligence and analytics research

Business intelligence and analytical research methods are classified into two categories. The first category is related to empirical and metrological research. The second category is related to behavioral and theoretical research. There are three broad categories that fall in the empirical research domain. The first sub-category is the development of new research methods and related methodologies. The second sub-category is the development of creative solutions for challenging problems. The third sub-category is the framework of efficient and accurate algorithms. The sub-categories in the domain of theoretical and behavioral research include the validation of theories with large data sets and analytical methods as well as the development of new theories based on these data sets. All above research methods have great applications in any business analyst course. In fact, the most popular business courses have incorporated these methods in their training programs to attract people from major research disciplines. This is a positive sign for both the academic and the industrial domain.

The umbrella of continuous research

Previous research in the domain of Business analytics has used causal explanation and typical models to derive inference from the underlying hypothesis. These modeling techniques and problem-solving methods have fared well in the past but at the same time, they have restricted researchers from working on emerging and challenging business problems. In addition to this, the focus of academic research has mostly been theoretical modeling and testing without the urge to use analytics in the application domain. The need of the hour is to equip business researchers with an arsenal of new research methods and tools.

Conclusion and future scope In the future, research methods would expand from the current domain of theoretical research to application oriented business research. In order to achieve this goal, the development of new and improved algorithms needs to be undertaken. The search for new applications and solutions for business development purposes cannot be ignored for too long. The use of quantitative methods like statistics and econometrics is what the business market forecasting is craving for. Network science, which also has numerous applications for business analytics, needs to be roped in at the earliest.

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