Business Intelligence

Introduction

In today’s competitive business environment, every organization has to be in the forefront to achieve the highest level of excellence across all the domains. To understand better ongoing on the market and in an organization we need to have a good level of business intelligence. Today we can use the power of data in business intelligence to study the ongoing business process and thus have better insights of it. It is capable of handling a large set of structured and unstructured data to create optimum strategies in the business development process.

Suppose during the new product development in a particular company – “What is the first factor we should look at?” The answer is the customer perspective. We should analyse the needs and requirements based on customer’s point of view. To do so we apply business intelligence. It can be used to support a wide range of business decisions – starting from product development to deployment and so forth.

Definition

Business Intelligence (BI) can be defined as a process of transforming raw data into a useful one by applying statistical and computational models for business decision-making purposes. There are various techniques we used during the implementation of business intelligence like data preparation, data visualization, data mining, statistical significance, etc. It has achieved great success because of advancement in technologies, such as computing power, data storage, computational analytics, reporting and even networking.

How to introduce Business Intelligence (BI) in an organization?

  1. Understand the problem – We should first understand the business problem with regards to different domains (sales, production, marketing, etc.). Here we
      • Defined the project objective
      • Defined the goal to achieve
      • Identify the data source
  2. Data exploration – Here we mainly focus on the collection of data, describing and exploring data for gaining initial insights about the data. We use descriptive statistics and perform data visualization. We try to find the relationship between the dependent and independent variables. In simple terms, we collect data from various sources and try to find the relationship between the variables by visualization of data.
  3. Data mining – It is a process of converting messy or inconsistent data to a useful one for further analysis. It is a crucial part of the BI process. Without proper data mining, we couldn’t gain better insights into a process. It uses methods like classification, clustering, association, etc. It would be helpful in business decision making processes.
  4. Optimization – Here we will determine the optimal solution from the alternatives available which gives maximum value by minimizing costs. Optimization can be used for best decision making instead of just showing the insights of models.
  5. Decision making – This is the final phase where we have to make a decision. We implement the solution based on the above steps results. Sometimes when BI methodologies are successfully adopted, the decision makers may also take advantage of unstructured information available to adapt and modify the original results.

Some statistical methods used in Business Intelligence (BI)

  1. Hypothesis testing – It is a procedure of drawing inferences about the population based on sampling data. The process of collecting a sample from a large set of data (population) for analysis is known as sampling. To determine whether the results are statistically significant or not – “We use Hypothesis Testing”. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole. Also called a statistical significance test.
  2. Correlation analysis – Correlation is the statistical tool which is used to know the relationship between two or more variables i.e. the degree to which the variables are associated with each other. In simpler words, it measures the closeness of the relationship. For example, price and supply, demand and supply, income and expenditure are correlated.
  3. Regression analysis – Regression analysis is a powerful technique in the field of statistical analysis used in prediction of the value of an unknown variable from a known variable, in predicting the value of one variable, given the value of another variable, when those variables are correlated to each other. Basically, regression analysis is used to predict an outcome based on historical data. It is also called a predictive analysis.
  4. Graphical analysis – Mainly during the data visualization process we use graphical analysis. Here, the data are presented in the form of graphs or diagrams. When we presented data through diagrams and graphs – it looks more convincing & appealing. Thus provide the meaningful outlook of a data. Some of the popular graphical tools used are

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