Big Data vs Business Intelligence: What are their differences?
In an increasingly data-driven environment, disciplines such as Big Data and Business Intelligence (BI) are being used more frequently in business and corporate decision-making. While both technologies share the common goal of optimising the understanding and exploitation of data, their approaches, methods and tools differ significantly. Throughout this article, we will analyse the singularities of each discipline and their particular uses and applications. Let's get started!
Big Data and Business Intelligence: Different approaches and operating methods
Big Data and Business Intelligence both focus on data analysis at a conceptual level and from a more theoretical perspective, although they do so in very different ways and using other types of technologies.
Big Data
The term Big Data refers to extremely large and complex data sets, the monitoring and analysis of which requires the use of advanced technologies, as well as a robust technological infrastructure to support such a large volume of data. Furthermore, Big Data can be used for the real-time processing of unstructured data from various sources such as social media, sensors, IoT (Internet of Things) devices, transaction logs, databases, and websites, etc.
The approach of Big Data is based on the use of tools and technologies such as Hadoop, Apache Spark, AirFlow and distributed data analysis platforms, with the purpose of obtaining insights and knowledge that are not immediately obvious. To do so, this discipline is also supported by others, such as Data Science, Machine Learning and AI.
Business Intelligence (BI)
Business Intelligence, on the other hand, refers to a set of tools, technologies and practices used to analyse a company's structured data, including the analysis of data in tabular formats (rows and columns). Business Intelligence focuses on making strategic decisions based on historical data and generating detailed reports on past performance. BI tools enable the creation of the famous dashboards, graphs, reports and displays that help executives and managers make more informed decisions.
The most common BI application comprises platforms such as Power BI, Tableau, QlikView and Looker Studio, IBM Cognos, and AWS QuickSight, etc.
Differences in data management and storage between Business Intelligence and Big Data
The differences in data management and storage between Big Data and Business Intelligence are notorious and clearly defined, as both areas handle very different volumes and types of data.
Big Data
In the case of Big Data, data is massive, usually unstructured or semi-structured, and comes from multiple sources, i.e. from diverse origins, requiring distributed systems and specialised technologies to manage it properly. With no pre-determined structure, data can include text, images, audio and video, which means that their storage and processing require highly scalable and flexible platforms. In this sense, within the context of Big Data, it is very common to use NoSQL databases, such as MongoDB or Cassandra, and cloud storage systems, such as Amazon S3 Cloud Storage from GCP or Azure services.
Moreover, Big Data involves not only storing large volumes of data but also its efficient, real-time processing or batch processing.
Business Intelligence
In comparison, data management in BI is much simpler and more structured. BI uses relational databases (RDBMS - Relational Database Management System) such as SQL Server, Mysql or Oracle, where data is organised into tables and has well-defined relationships (relational data model). This data is usually from internal company sources, such as enterprise resource planning (ERP) systems, CRMs or transactional databases.
Because BI development focuses on historical data, it does not require the same real-time processing ability that is needed to process Big Data. Furthermore, the storage of this data focuses on simplifying agile queries and reporting through data that has been previously processed and cleaned, applying ETL process techniques.
Data analysis: Key contrasts between Big Data and Business Intelligence
Big Data
The use and application of Big Data is based on the application of advanced machine learning techniques, predictive analytics and artificial intelligence to detect patterns and correlations hidden in this massive amount of data. This approach provides the possibility of making predictions, complex segmentations or establishing relationships between variables that could not have been identified without the power of Big Data tools.
Business Intelligence
As mentioned above, BI analysis focuses primarily on describing what has happened in the past through detailed reports and key metrics. BI tools provide a clear and simple understanding of historical data that enables strategic decisions to be made in the short, medium and long term.
Jobs and skills: Big Data vs Business Intelligence
The jobs and skills for working in Big Data and Business Intelligence are also very different, as they require very different technical and business abilities.
Let us briefly comment on some relevant aspects to be highlighted:
Big Data
Professionals in the field of data analytics, such as scientists, programming technologists (Python, Java, R, Scala), advanced statistics and Big Data technologies such as Hadoop and Spark must be able to collaborate with distributed architectures, NoSQL databases, and possess knowledge in the application of cloud computing platforms such as AWS or Google Cloud.
Business Intelligence
On the other hand, jobs in Business Intelligence, such as BI analysts, BI consultants or BI developers, need skills in specific BI tools (e.g., Power BI, Tableau, Qlik, among others). A Data Analyst must have a deep understanding of business processes and be able to generate strategic insights based on historical data, which are correctly aligned with the company's strategic objectives.
Comparison of other key differences between Business Intelligence and Big Data
Here is a brief summary of the key differences between Big Data and Business Intelligence:
Aspect | Big Data | Business Intelligence (BI) |
|---|---|---|
Data type | Unstructured, semi-structured and structured data. | Mainly structured data. |
Volume of data | Massive, usually in petabytes or more. | Generally smaller and more manageable. |
Source of data | External sources, social media, IoT, sensors, etc. | Internal sources, transactional databases. |
Approach to analysis | Predictive, exploratory, pattern discovery. | Descriptive, retrospective, reporting. |
Tools and technologies | Hadoop, Spark, NoSQL, machine learning, artificial intelligence. | Power BI, Tableau, QlikView, SQL, OLAP. |
Processing time | Real or near-real time. | Based on historical data, not real time. |
Conclusions
While both Big Data and Business Intelligence play a key role in the digital transformation of organisations, it is essential to understand their fundamental differences in order to apply the appropriate technologies according to the strategic objectives of each organisation.
If you would like to obtain the knowledge and skills necessary to excel in these fields, OBS Business School, from Planeta Formación y Universidades, has educational programmes that will give you the opportunity to understand both Big Data technologies and Business Intelligence tools.
If you are ready to take the next step in your career, OBS Business School can be your gateway to becoming a leader in the field of data.
