Difference Between Big Data And Business Intelligence

Difference Between Big Data And Business Intelligence – Interested in exploring a field of work that deals with data, but still don’t know what the differences are between the professions of Business Intelligence, Data Science, Data Analysis and Business Analysis? You are in the right article to find concise and clear answers to the questions above! Come on, read the information until the end to get interesting information.

What is Business Intelligence and Examples

Business Intelligence (BI) is a technology-based process that collects, stores and analyzes data, to improve the quality of business decision making.

Business Intelligence can also be interpreted simply as a system for converting data into useful insights for making business decisions. All data processed in Business Intelligence is taken based on facts and historical data.

An example of the case of Business Intelligence (BI) and why data from BI is really needed is when a company wants to innovate. Let’s use the example of the ForCoffee company, which already has many customers and wants to create an application. (Difference Between Big Data And Business Intelligence)

Before deciding to start the application development process, the CEO of ForCoffe will definitely ask for data that can explain the urgency of “why ForCoffe has to create an application.

” So this is where the role of BI is to process historical data that records customer behavior when buying ForCoffe. Is ForCoffe mostly purchased through food delivery applications, or is it purchased more directly in stores. This data then becomes insight to improve the quality of decision making, so that the innovations made are truly effective.

There are 2 branches of Business Intelligence, namely Business Intelligence (BI) Analysis and Business Intelligence (BI) Engineering. What’s the difference? The answer is below.

Business Intelligence (BI) Analysis

Difference Between Big Data And Business Intelligence

You could say that a BI Analyst is a person whose job is to identify trends and gain insight by utilizing BI data, techniques and tools. Trends in the context of BI are certain patterns or tendencies that we usually find in the data.

These trend findings will later become insights that users (managers, C level, and other stakeholders) will then use to make business decisions.

The scope of BI Analyst work is:

  • • Data visualization: Provides information and displays data in infographic or visual form so that it can be easily understood by users.
  • • Experimentation: Analyzing causality and correlation (cause-effect).
  • • Analytics & modeling: Collect and analyze data related to trends, consumer behavior, product performance, and predictions.

Business Intelligence (BI) Engineer

If a BI Analyst is tasked with identifying trends, then there must be someone in charge of creating the system. This is the role of a BI Engineer, a person who develops, implements, and maintains BI systems, which include applications and dashboards.

The BI system is a structure that helps with the steps of storing and managing data with technology, which will later be used by BI Analysts to analyze data / trends to gain insight. It is important for a company to have a BI system.

The scope of work of a BI Engineer is:

  • • Data warehouse: designing and producing data models, databases, and data warehouses.
  • • Data transformation: cleaning, piping, standardizing, and summarizing data into insights so that users (stakeholders) can easily use it.
  • • Data tools: Providing and building tools to increase daily work efficiency.
  • • System & reliability maintenance: Maintaining the BI system and hardware where the BI system is located and carrying out technical problem solving when needed.
  • • Data pipeline: Extracts data from various sources into the BI system and maintains its continuity.

Difference between Business Intelligence and Data Science

Difference Between Big Data And Business Intelligence

The basic difference between Business Intelligence (BI) and Data Science is that BI focuses on analyzing historical data to monitor areas of concern and draw insights, while Data Science produces predictive insights from data patterns analyzed with Machine Learning algorithms.

In more detail, you can compare the differences between BI and Data Science below:

Business Intelligence

  • • Definition: technology-based processes that collect, store and analyze data, to improve the quality of business decision making.
  • • BI focuses on current data and historical data
  • • Primarily dealing with structured data
  • • Needed to analyze the current situation, find the root of the problem, and make decisions.

Data Science

  • • Definition: a field of study that combines statistics, applied mathematics, AI (machine learning & deep learning) to find predictions from hidden patterns in data.
  • • Data Science focuses on what will happen next from data patterns
  • • Often deal with unstructured data
  • • Needed to anticipate future scenarios, improve data accuracy, provide recommendations from data patterns.

Difference between Business Intelligence and Data Analysis

The basic difference between BI and Data Analysis is that Data Analysis is a tool or method in BI. Business Intelligence is aimed at presenting data used for decision making, while Data Analysis is aimed at changing raw data into a more useful format according to business needs.

In more detail, you can compare the differences between BI and Data Analysis below:

Business Intelligence

  • • Focuses on analyzing historical data to improve the quality of decision making
  • • BI uses the ‘big picture’ and utilizes existing insights, to explore what has happened in the past and make decisions.
  • • Produce structured data that can be digested by non-tech users (stakeholders)

Data Analysis

  • • Focuses on modifying, modeling and cleaning raw data into more useful formats according to business needs. (Difference Between Big Data And Business Intelligence)
  • • Data Analysis uses a narrower focus, using data to solve specific problems.
  • • Generally used to predict future trends, usually starting with unstructured data that requires parsing and data cleaning before analysis.

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