Business Analytics Definition and Overview

Today several products and solutions are driven by analytics. For instance, Amazon GO, recommendations systems, predictive keyboards used on smartphones and chatbots are a few examples of analytics-driven solutions.

What is Business Analytics?

For many years in the past, many corporations used to make decisions based mostly on opinions rather than decisions primarily based on correct data analysis. Opinion-based decision-making may be dangerous and often results in an incorrect decision.

In God we trust;all others must bring Data.

-Edwards Deming

The main goal of business analytics is to enhance the standard of decision-making utilizing data analysis.

Statistical and operations analysis methods have been in use for several decades by many organizations; however, since the 21st century, organizations that use analytics have elevated exponentially. The main purpose of this increase in analytics is the theory of bounded rationality proposed by Herbert Simon(1972).

According to Herbert Simon, the growing complexity of enterprise issues, the existence of various options, and the restricted time out there for decision-making demand an extremely structured decision-making process utilizing past data to manage organizations effectively.

Business Analytics Definition

The business analytics definition is as below,

Business Analytics is a set of statistical and operations analysis methods, artificial intelligence, information technology and management strategies used to frame a business problem, collect data, and analyse the data to create value for organizations.

Technology

Information Technology is used for data capture, storage, preparation, evaluation and sharing. Today, a lot of the data are unstructured data – not within the form of a matrix(rows and columns). Images, texts, voice, video, and click streams are a few examples of unstructured data.

To analyse these data, you simply want to use software such as R, Python, SAS, SPSS, Tableau, and so forth.

Data Science

Data Science is likely one of the essential parts of analytics. It consists of statistical and operations analysis methods, machine learning and deep learning algorithm. The goal of data science is to identify the most appropriate statistical model/machine learning algorithms that can be used.

Business analytics is grouped into three varieties: descriptive, predictive, and prescriptive.

Descriptive Analytics

Descriptive analytics is the simplest type that primarily uses simple descriptive statistics, data visualization techniques, and business-related queries to understand previous data.

“If the statistics are boring, then you have got the wrong numbers.”

– Edward R. Tufte

The main goal of descriptive analytics is to give you innovative methods of data summarization. Descriptive analytics using visualization identifies trends within the data, and you will be able to attach the dots to gain insights about associated businesses. Additionally, descriptive analytics uses descriptive statistics and queries to gain insights from the data.

Predictive Analytics

“If you torture the data long enough, it will confess”

Ronald Coase

Predictive analytics aims to predict the chance of occurrence of a future event, such as forecasting demand for products and services, loan defaults, fraudulent transactions, insurance coverage claims and inventory market fluctuations.

While descriptive analytics is used for finding what has occurred previously, predictive analytics is used for predicting what’s more likely to occur sooner or later.

Prescriptive Analytics

“Every decision has a consequence”.

– Damon Darrel

Prescriptive analytics is the highest stage for selecting optimum actions as soon as an organization gains insights through descriptive and predictive analytics.

Prescriptive analytics will help you find the optimum answer to an issue or make the correct selection amongst several alternate options.

Techniques used in Descriptive, Predictive and Perspective Analytics

The methods used for descriptive analytics are Mean, median, mode, variance, Standard deviation, Skewness and Kurtosis.

Some commonly used methods in predictive analytics are Regression, Logistic Regression, Classification Trees, Forecasting Techniques, K-nearest neighbours, Markov Chains, Random forest, boosting and neural networks.

The Tools used in Perspective analytics are linear programming, integer programming, multi-criteria decision-making models such as goal programming and analytical hierarchy process, combinatorial optimizations, non-linear programming and meta-heuristics.

Machine Learning Algorithms

Machine learning algorithms are part of Artificial Intelligence that imitates human learning. Human learns through many experiences to carry out a job. In the identical approach, many models are developed using machine learning algorithms, and every model equals experience.

Read Beginners Guide to Machine Learning for free.

Mitchell (2006) outlined machine learning as follows:

“Machine learns to a particular task T, perform metrics O following experience E, if the system reliably improves its performance P at task T, following experience E.”

It assumes task T to be a classification problem that considers a buyer’s propensity to purchase a product.

The performance P can be measured using metrics such as general accuracy, sensitivity and area under the receiver operating characteristics curve(AUC).

Classification of Machine Learning

Machine Learning algorithms are categorised into four classes.

Supervised Learning Algorithms

Supervised Learning Algorithms are used if the training dataset comprises predictor (input) and outcome (output) variables. The learning is supervised because predictors(X) and the outcome (Y) are the model’s variables. Regression, logistic regression, decision trees, and random forests are the methods used for supervised learning algorithms.

Unsupervised learning Algorithms

Unsupervised Learning Algorithms are used when the training dataset has predictor (input) variables (X) solely, however, not the outcome variable (Y). Techniques such as K-means clustering and Hierarchical clustering are some examples of unsupervised learning algorithms.

Reinforcement Learning Algorithms

If some instances, the input variable (X) and the output variable (Y) are unsure. One instance can be predictive keyboards or spell checks used in your smartphones. In these instances, Reinforcement Learning Algorithms are used. The algorithms are additionally utilized in sequential decision-making situations, such as dynamic programming and the Markov decision process.

Evolutionary Learning Algorithms

These are algorithms that imitate the human learning process. They are used frequently to solve prescriptive analytics issues. The methods used are genetic algorithms and abt colony optimization.

Conclusion

To conclude, business analytics is an integral part of a corporation, and nearly all decisions are made using previous data. Innovation is an important thing to the success factor in analytics.