Data Analytics Vs. Business Intelligence Which is better?

The great potential value of data for companies if they are properly exploited is something undeniable. For this reason, many organizations use techniques to take advantage of them one hundred percent, such as Business Intelligence vs. Data Analytics and Data Science, among many others. Each of them has a different focus and objectives. Business Intelligence focuses on providing reports and visualizations to help in decision making, Data Analytics focuses on discovering patterns and trends, and Data Science focuses on building models and algorithms to automate decision making and predict future outcomes. 

In short, these techniques are complementary and together they can help organizations get more value from their data. Although there are similarities, Data Analytics vs Business Intelligence (BI) have different goals and approaches. For example, BI focuses on using historical data to understand what has happened in the past and make informed decisions in the present.. On the other hand, Data Analytics uses data science techniques and tools to make predictions and recommendations about what might happen in the future. Although both have a similar purpose, BI focuses on retrospective analysis and data analysis focuses on prospective analysis.

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When comparing Data Analytics vs Business Intelligence, we noticed how a key point is the functionality and the needs of your company.

What is Business Intelligence vs. Data Analytics?

Let's start by defining what each tool is and what it offers in this competition between Data Analytics vs Business Intelligence. Business Intelligence (BI) refers to the use of technologies, applications and practices to collect, integrate, analyze and present business information in order to support business decisions.

For its part, Data Analytics, while also aiming to support decisions, focuses on the use of data science techniques and tools to analyze and extract valuable information from data. Although both have a similar purpose, BI focuses on the presentation of information and Data Analytics focuses on the analysis of information.

Business Intelligence

To compare Data Analytics vs Business Intelligence, you have to understand the tools and uses that each tool has. According to the renowned research, advisory and consulting firm, forrester, Business Intelligence (BI) is described as: “a set of methodologies, processes, architectures and technologies that transform raw data into meaningful information and tool that is used to enable strategic, tactical, and operational insights and decision making”.

In its broadest sense, Business Intelligence (BI) can be understood in two ways. On the one hand, it refers to the strategies, technologies and tools used by companies to collect, integrate, analyze and present business information in order to make informed decisions. On the other hand, refers to the knowledge and understanding gained through this process. It is important to keep this distinction in mind when talking about BI since it can refer to both the process and the final result. Some of the tools that we find in Business Intelligence are: 

  • Real-time monitoring
  • Dashboard development and reporting
  • benchmarking
  • BI software implementation,
  • Performance management
  • Data and text mining

In itself, Business Intelligence (BI) it is a complex process that involves several different tasks and tools. In a certain way, Data Analytics is one of the most important tools within Business Intelligence, but it is only one part of the broader process. It's important to keep in mind that BI is a complex process, and data analysis is only one piece of the larger puzzle.

data analytics vs business intelligence
When comparing Data Analytics vs Business Intelligence it is important to understand the needs of your company.

Data Analytics

As we can see when comparing Data Analytics vs. Business Intelligence, these tools complement each other to have the complete picture to make the right decisions. But, it is worth understanding what are the uses and tools of DA. Data analysis is the process of collecting, cleaning, inspecting, transforming, storing, modeling, and querying data. (along with several other related tasks). Its goal is to produce knowledge that informs decision-making, yes, in business, but also in other domains, such as science, government, or education.

Data Analytics focuses on the fundamental tasks of the analysis process, such as data collection, cleansing, inspection, transformation, storage, modeling, and query. Although it is often used in the context of business decision-making, it is not limited to this realm and is also used in other areas such as science, government, and education.

The classification of Data Analytics, according to its purpose, can be divided into four categories: descriptive, diagnostic, predictive and prescriptive:

  • descriptive analysis provides an objective description of what has happened in the past. 
  • diagnostic analysis seeks to understand the reasons behind what has happened in the past. 
  • predictive analytics uses past data to make predictions about future trends. 
  • prescriptive analysis provides actionable steps to reach a specific goal.

The Data Analytics process involves cleaning and preparing raw data for use in analysis. This includes tasks such as data collection, cleansing, categorization, conversion, aggregation, validation, and transformation. Once the data is clean, it is stored in a structure and format suitable for reporting. Often this means storing them in a data warehouse, which is a columnar storage structure that can be hosted on a scalable cloud infrastructure. The data in the data warehouse represents a single version of the truth for all organizational reports, both for Data Analytics vs Business Intelligence.

data analytics vs. business intelligence
When seeing the differences between Data Analytics vs Business Intelligence, we can notice what is in the prescriptive and descriptive analysis.

Key Differences Between Data Analytics vs Business Intelligence

Data Analytics or Business Analytics is a process that enables business users to turn unstructured data into meaningful information.. It is a tool commonly used by organizations around the world to support various business strategies and processes based on their specific needs.

Business Intelligence is a tool that is used in many organizations to improve the ability to make decisions., analyze business data, perform data mining, generate reports and improve operations. BI is primarily based on historical data stored in data warehouses or Data Marts. Some of the fundamental tasks in BI implementation include data cleansing, data modeling, data transformation, and forecasting future data trends.

Specific key points of comparison between Data Analytics vs Business Intelligence:

  • Meaning: Business Intelligence refers to the information required to improve business decision-making activities. On the other hand, Data Analytics refers to the modification of raw data in a meaningful format.
  • functionality: The main purpose of BI is to support decision making and help organizations grow their business. Meanwhile, DA's is to model, clean, predict, and transform data based on business needs.
  • Debugging: The BI mechanism can be debugged only through the provided historical data and end user requirements. The DA can be debugged through the proposed model to convert the data into a meaningful format.
In data analysis it is important to consider the differences and strengths of Data Analytics vs Business Intelligence.

In summary, Data Analytics vs Business Intelligence are related but different concepts, with different origins and goals. However, current trends in technology have led to an evolution in BI tools and data analysis., allowing business users to choose the right option for their specific needs. Both, Business Intelligence and Data Analytics have a crucial role in business growth and companies are investing in both to achieve their goals efficiently.

In Codster, we can be your ally in the development and implementation to exploit the potential of your company, creating technological solutions for identity validation tailored to your needs. If you want to know more, do not hesitate to contact us.

Eri Gutierrez

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