Business Intelligence and Data Analytics are both very important topics when it comes to business operations and management. Focusing on the ways a business should run based on its past performance, these two topics are considered essential when business is the talk of the town!
In this blog, I will be going through Business Intelligence and Data Analytics along with the difference between them.
Before we understand the true differences between these two topics, we should first understand the basics of these concepts.
Business Intelligence (BI) is a concept wherein business data is recorded, studied, and worked upon so as to excel in the future. Revolving around business data that usually explains the performance of the business in the past times and reflects on the overall growth of the operations.
BI promotes errorless operations with respect to data that is concrete and credible. It gives an insight into the records of the business that help business officials evaluate the journey of the business in terms of economic progress and in other aspects too.
To process BI, there are a set of tools that help one extract information about a business and its whereabouts till date. One of these tools is Dundas BI. You can look for Dundas BI features that help in formulating records of the business based on its past strengths and successes.
Such platforms are helpful in analyzing business history and studying the cases that have either disabled or enabled the business to succeed.
When it comes to Business Operations, BI is way more helpful than anything else as it renders an opportunity for the business officials to evaluate the company’s performance while introspecting about the failures that occluded the growth process.
Furthermore, it must be kept in mind that one should always keep a continuous check on the Business Intelligence tools that are being used instead of straightaway restricting some platforms.
This keeps the flow of information unstoppable yet unbeatable, only for betterment. A vital component of business operations, BI is indeed indispensable.
(Must check: Top 10 business intelligence trends)
Data Analytics means processing raw data into meaningful information, deriving patterns out of the information, evaluating these trends, and finally transforming their patterns to give a boost to business growth.
A process wherein business officials analyze the data and strive forward to bring in innovation, Data Analytics is rich in analysis that empowers a business to bring in unique changes and add them so as to raise the levels of success.
Data Analytics (DA), or Business Analytics also involves predictive analysis that is based on past patterns but reflects on future growth ahead. Although Data Analytics can be processed with the help of BI tools, there are numerous tools that specialize in Data Analytics that help business officials to adopt different strategies in the favor of the business.
As Data Analytics is helpful in converting raw data into meaningful information, it is certainly helpful in analyzing future trends as well. With the help of predictive patterns and technical tools, data analytics tools can help a business in burgeoning with the help of a set of algorithms.
Let me explain it better with the help of one of the data analytics examples. For instance, a manufacturing firm dealing in automobile goods is likely to analyze the production rate and costs in order to generate more profit.
Perhaps here is when Data Analytics leads the game. It helps in studying, recording, and analyzing data in order to adapt to more profitable yet progressive practices.
Business Intelligence and Data Analytics are two different concepts. Yet they can be confusing to understand. While a lot many confusions exist in this world, this one is surely a biggie! Revolving around business operations and data analysis, the two concepts have many differences which I will be discussing in this segment.
Differentiating factors between BI and data analytics
The first and foremost difference between Business Intelligence and Data Analytics is that the former revolves around operation while the latter is more inclined towards innovation.
As Business Intelligence is concerned with collecting raw data and evaluating the historic growth of a business, it revolves around business operations and might or might not pay emphasis on innovation.
On the other hand, Data Analytics is all about converting raw data and analyzing it so as to set future trends and patterns, which makes it sure for business officials to engage in their business operations innovatively.
Business Intelligence, as opposed to Data Analytics, records data in a raw format that has to be amalgamated into an algorithm that helps us extract core patterns.
While Business Intelligence lags behind in this aspect, Data Analytics covers this realm and offers an opportunity to business personnel to add innovation to their business operations management.
This difference demarcates the functionality of the two concepts which, as we have understood, is quite distinct. Furthermore, this difference also highlights the importance of the two concepts that are indispensable yet unbeatable.
Another difference that exists between the two concepts is that Business Intelligence is more inclined towards the past whereas Data Analytics is inclined towards the future. Business Intelligence, as opposed to Data Analytics, emphasizes studying data based on situations that have already occurred in business history.
However, Data Analytics tends to highlight future patterns that are likely to occur in the future. This also leads to a derivation that implies that Business Intelligence involves less risk in comparison to Data Analytics.
As Innovation involves risks, Data Intelligence is highly different from Business Intelligence in this sense. BI is more relevant when it comes to past patterns of the business operations that eventually lead to the formation of data for DA.
In addition, BI is highly inclined towards the historic records of a business that somehow differentiates it from DA, which involves implementing innovative trends in the future for better growth.
Business Intelligence is about being intelligent in terms of business and related operations.
On the other hand, Data Analytics includes analyzing the data and questioning trends that have been continuing over time. What this means is that the difference between BI and DA surrounds the idea of decision-making and questioning trends.
As the business personnel moves on to counter the trends that have been practiced in the business history, Data Analytics leads to decision-making processes that often engage.
This is one of the main differences that differentiate between the concepts of Business Intelligence and Data Analytics and certainly helps us to understand the concepts in a better manner.
The fourth difference is the types of tools and techniques that BI and DA incorporate in their processes. While Business Intelligence is all about decision-making powers that emerge from the raw data collected, Data Analytics is more about countering these decisions and trying to negate the ones that are more vulnerable to losses.
In light of this difference, there are various tools and techniques that promote the processes of Business Intelligence and Data Analytics respectively.
As Business Intelligence consists of tools that deal in data collection, producing scoreboards and reports, Data Analytics deals in tools and techniques that facilitate data analysis while converting data into meaningful chunks.
Moreover, Data Analytics especially incorporates technologically advanced tools to give way to efficient analysis. Data Analytics tools keep evolving over time as more and more tech-savvy tools and techniques get designed from time to time.
The last of all differences, yet one of the most important ones too, entraps our thought-process in the way the 2 concepts work.
This means that while Business Intelligence is more concerned with achieving goals that have already been a part of the business objectives, Data Analytics leads one to add goals for the progress of the business operations in accordance with the patterns that it must follow.
This difference of adding goals v/s achieving goals throws light on the different perspectives involved in these processes. Besides, Business Intelligence also focuses on applying quick implementation skills as compared to Business Analytics which incorporates long-term strategies to accelerate business operations.
Therefore, Business Intelligence and Business Analytics are different on the basis of the above-mentioned parameters that lay out a distant line between these concepts.
To wrap up, I would again like to highlight the significance of the two concepts. Essential in business operations, Business Intelligence and Data Analytics assist in collecting raw data and analyzing them for future operations.
Although the 2 concepts sound similar, there are major differences between the two. From achieving goals to the decision-making processes, Business Intelligence is all about studying business growth and patterns based on the data collected from past operations.
(Recommended read: Top Big data analytics tools)
On the other hand, Business Analytics deals in transforming raw data into meaningful material which eventually helps one to draw future trends on predictive grounds by questioning past patterns and strategies.
Lastly, the two concepts are highly distinct from each other yet are intertwined in such a way that Business Intelligence cannot do without Business Analytics and vice versa.
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
6 Major Branches of Artificial Intelligence (AI)READ MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE
What is the OpenAI GPT-3?READ MORE
7 Types of Activation Functions in Neural NetworkREAD MORE