Business Intelligence – BI

Business Intelligence (BI) refers to the strategies, technologies, and practices used to collect, integrate, analyze, and present business information. The primary purpose of Business Intelligence is to assist in interpreting large volumes of data and provide support to make informed business decisions. 

The Business Intelligence process includes four key steps to convert the raw data into meaningful insights: data collection, data integration, data analysis, and data visualization and reporting.

By analyzing historical and current data, BI helps businesses recognize trends, identify business opportunities, and improve operations.  

Origins of BI

Business Intelligence has its roots in the Decision Support Systems (DSS) that were popular in the 1960s and through to the 1980s. These were computer-aided information systems that helped businesses make decisions based on data.  

The modern understanding of Business Intelligence with computerized data analysis emerged from the data warehousing trend of the late 1980s and early 1990s. Howard Dresner, a research analyst at Gartner, popularized “business intelligence” as an umbrella term to describe concepts and methods to improve business decision-making using fact-based support systems. Dresner’s definition is still widely accepted.  

Growing in Scale and Application

With the rise of computerized business systems in the late 20th century, large amounts of data became available for analysis. As a result, companies started to build data warehouses to store this information and leverage it strategically to gain a competitive edge.  

The advent of the internet and the digitalization of business functions in the late 20th and early 21st centuries increased the amount of data businesses had to deal with. This led to the development of more sophisticated tools and practices for managing and interpreting this data, creating the field of BI as we know it today.  

Going Social and Getting to Big Data

With the growth of big data, cloud computing, artificial intelligence, and machine learning, the BI landscape has evolved even more in recent years. Now, not only does BI involve the analysis of internal data, but it also incorporates external data sources such as social media, IoT devices, and more.  

Moreover, BI has also democratized over time. Earlier, BI systems were primarily the domain of data analysts and IT departments.

But modern BI tools like Tableau, Power BI, and Looker have made data analysis accessible to non-technical users, empowering them to make data-driven decisions without needing to rely on IT. This trend, known as self-service BI, is one of the significant developments in the BI field.  

Key Steps in the BI Process

To provide actionable insights, BI specialists generally use these four major steps in their work:

1. Data Collection: BI relies heavily on data from different business operations, such as sales, production, internal operations, market analytics, and customer behavior. This data can come from internal systems (like CRM and ERP) and external sources (like social media and market research).
2. Data Integration: BI uses tools and software to integrate this diverse data into a standard, manageable format. It often involves data warehousing or data lakes for storing and managing vast volumes of data.
3. Data Analysis: This is the heart of BI. It involves converting the raw data into meaningful insights. This can include statistical analysis, data mining, predictive modeling, machine learning, and other advanced analytics techniques.
4. Data Visualization and Reporting: This involves presenting the data and insights in an easily digestible format. Dashboards, reports, graphs, and charts are commonly used for this purpose.  

Key BI Technical Tools

BI specialists use numerous tools to get their work done. Major categories and leading products in each include:

1. Data Warehousing Tools: Tools like Apache Hadoop, Amazon Redshift, and Google BigQuery store and manage large data sets.
2. Data Integration Tools: Tools like Informatica PowerCenter, Microsoft SQL Server Integration Services (SSIS), and Talend are used to integrate data from different sources and convert them into a standard format.
3. BI Platforms: Platforms like Microsoft Power BI, Tableau, and QlikView provide end-to-end BI solutions for everything from data integration to visualization.
4. Advanced Analytics Tools: Tools like R, Python, SAS, and SPSS are used for complex data analysis, including predictive modeling, machine learning, and statistical analysis.  

Conclusion

The ultimate goal of BI is to provide actionable insights that guide decision-making and strategy formation in an organization. Advancements in AI, machine learning, predictive analytics, and real-time analytics are driving new BI capabilities.  

As a result, businesses of all sizes and industries leverage BI tools to understand their operations and market conditions and make informed strategic decisions.

What is Business Intelligence? – 5 mins