Data Analytics vs. Business Intelligence: Elaborating the Differences
In this section, we shall undertake a comparative analysis on the issue of Business Intelligence vs. Big Data Analytics.
- Big Data Analytics vs. Business Intelligence: Difference in Scope
In highlighting the difference between the two ideas of Business Intelligence and Data Analytics, you should remember that in the first place, the scope of the two fields is different. BI revolves around operation and is more concerned with acquiring operational insights. On the other hand, Data Analytics is focussed on conducting different kinds of analyses and is more concerned with innovation. With BI, you build performance dashboards and present results; however, Data Analytics digs deeper and tries to find the causal factors which might have influenced those results. Thus, in terms of scope, while Business Intelligence provides a panoramic picture of business conditions; Data Analytics provides more intricate details of the existing condition.
- Business Intelligence vs. Data Analytics: Nature of Analysis
This line of argument is also summed up as the past vs. the future. In this respect, BI is seen as the discipline which focuses on the past; while Data Analytics is concerned with the future. Unlike Data Analytics, BI happens to be concerned with studying and comprehending data derived from events or situations which have already taken place in the past. Thus, Business Intelligence focuses on Descriptive Analytics. It helps in providing summaries of present and historical data, in order to give a sense of what happened or what is happening. Hence, it is concerned with questions of ‘what’ and ‘how’.
In contrast, Data Analytics, by way of discovering trends and correlations, tries to highlight patterns which are likely to be discovered in future. Thus, Data Analytics focuses on Predictive Modeling. It determines the probability of occurrence of future outcomes through making use of techniques like Machine Learning, Data Mining and so on. Hence, Data Analytics is concerned with the question of ‘why’ which helps it make precise predictions about the future.
- Business Intelligence vs. Big Data Analytics: Type and Quality of Data
BI is conducted on refined or structured data which is especially prepared for analysis through tools like Tableau and Power BI. Data Analytics deals with structured as well as raw unstructured data. Data Analysts can conduct data analytics on video, audio or text file formats. They often make use of libraries in order to extract structured as well as unstructured information from websites.
Given the type of data involved in both the cases, BI happens to be dependent on Data Warehousing for improving the quality of data. On the other hand, Data Analytics can directly make use of data collected from Data Lakes and other disparate sources. Thus, Data Wrangling is a core part of Data Analytics and not necessarily Business Intelligence.
- Data Analytics vs. Business Intelligence: Math and Statistics
Business Intelligence does not necessarily entail the requirement of core math skills like expertise in probability and linear algebra. Most of the BI tools have specialized features which carry out these commands; however, you will be required to learn platform dependent languages. In contrast, a Data Analyst is required to be proficient in these mathematical skills in order to be able to interpret and evaluate data which might not be possible through customized commands alone.
While Business Intelligence remain largely concerned with Descriptive Statistics in finding the mean, median and average; Data Analytics is all about making use of Descriptive as well as Inferential Statistics for understanding data as well as conducting Predictive Analytics.
- BI vs. Data Analytics: Issue of Coding
Business Intelligence and Data Analytics can be differentiated on the basis of coding requirements. The process of BI can be carried out even without coding as the different Business Intelligence Tools provide simple drag and drop functionality which can be utilized for producing attractive data visualizations. This is not the case with Data Analytics. It entails the usage of different programming languages for the purpose of conducting complex analyses.
- Data Analytics vs. BI: Objectives
On the basis of this argument, it is stated that while Data Analytics is about adding new objectives and goals for the organization to achieve; Business Intelligence is more about accomplishing the objectives and goals which have already been defined by the organization. Since BI is more concerned with past and present data, it is natural for it to lay emphasis on defined goals. On the other hand, Data Analytics, being concerned with future patterns, tries to add more possible achievable goals to the list of future plans of the business organization.
Business Intelligence vs. Big Data Analytics: Tabular Comparison
Business Intelligence |
Data Analytics |
The phrase was coined by the author Richard Miller Devens in his book in 1865 |
The historical roots of Data Analytics can be traced back to the 19th century. However, it gained public attention in the 1960s with the invention of computers |
The objective of BI is to facilitate data driven decision making and promote the growth of business |
The objective of Data Analytics is to collect, clean, analyze and interpret data in order to derive actionable insights |
Deals with structured data |
Deals with structured as well as unstructured data |
An attempt to understand the past |
An attempt to predict the future |
Makes use of Data Warehouses |
Makes use of Data Lakes |
It undertakes Descriptive Analytics |
It undertakes Predictive Analytics |
Visual Insights, Performance Management and Flexible Reporting |
Data Modeling, Statistical Analysis and Financial Forecasting |
Some of the popular BI tools are Tableau, Power BI, Excel, SQL |
Some of the popular Data Analytics tools are Python, R, Tableau, Power BI |
Business Intelligence vs. Data Analytics vs. Data Science
In this section, we shall undertake a tripartite comparison on the issue of Analytics vs. Business Intelligence vs. Data Science.
Business Intelligence |
Data Analytics |
Data Science |
BI is concerned with acquiring operational insights which could present an overview of the existing business conditions |
Data Analytics is concerned with providing answers to existing questions through analyzing information for deriving actionable insights |
Data Science tries to discover new questions which can push innovation |
Objective of BI is to promote business growth through building performance dashboards and presenting results |
Objective is to conduct routine analysis of data in order to derive insights and produce reports which could guide business decisions |
Objective is to design the model for the storage, manipulation, analysis and management of data |
Concerned with the past and the present |
Concerned with the future |
Concerned with the future |
Deals with structured data |
Deals with structured as well as unstructured data |
Deals with structured as well as unstructured data |
Deals with questions of ‘what’ and ‘how’ |
Deals with questions of ‘why’ |
Deals with questions of ‘what will happen’ and ‘what if’ |
The emphasis of the discipline is on the IT systems which process analytics data |
The discipline has its roots in the fundamental elements of analytics, including statistics and advanced mathematics, and data mining |
The discipline probes into more technical aspects of computer engineering, computer programming and computer science |