Basics of Business Intelligence (BI)
Business Intelligence (BI) refers to the process of collecting, analyzing and processing data in order to make well-founded business decisions. The focus is on transforming raw data into valuable information and insights to help companies achieve their business goals and increase their competitiveness. This section explains the basic concepts of business intelligence and their relevance to business success.
The definition of Business Intelligence: Business Intelligence varies depending on the perspective and area of application. In general, however, BI refers to methods, technologies and practices used to analyze business data to enable better strategic and operational decisions. An important aspect of BI is to collect large amounts of structured and unstructured data from different sources, organize it and present it in a user-friendly format.
The historical development of business intelligence dates back to the 1960s, when the first computerized information systems were developed. Since then, both the technologies and the methods for data analysis and processing have continued to develop in order to meet the growing requirements of companies and the increasing amount of available data.
The goals and benefits of business intelligence are manifold and include identifying business opportunities, optimizing business processes, improving customer satisfaction and increasing efficiency. BI also enables companies to identify risks and opportunities at an early stage and take preventive measures to better manage future challenges. In summary, business intelligence helps to maximize business success through data-driven decisions and continuous improvement.
Main components of Business Intelligence
An effective BI system is made up of various main components that are closely interlinked to enable a smooth process from data collection to decision-making. The main components of Business Intelligence include data integration, data storage, data analysis and processing as well as reporting and visualization.
Integration of data
Data integration is a central aspect of business intelligence and refers to the process of collecting, merging and processing data from various internal and external sources. The ETL process (extraction, transformation and loading) is a frequently used method for data integration:
Extraction:
Data is collected from various sources such as ERP systems, CRM applications, social media platforms and external databases.
Transformation:
The raw data is processed, cleansed and converted into a standardized format in order to eliminate inconsistencies and ensure a consistent database for the analysis.
Loading:
The transformed data is transferred to the data warehouse or data marts to enable the data to be stored and analyzed centrally.
Data storage
Data storage plays a crucial role in business intelligence systems, as it forms the basis for analyzing and processing the information collected. The most important data storage concepts are
Data Warehouse:
A data warehouse is a central database that stores data from various sources in a standardized format. It enables the long-term storage, analysis and retrieval of large volumes of historical and current data. Data warehouses are known for their scalability and ability to support complex queries and analyses.
Data Mart:
A data mart is a specialized database that is geared towards a specific business area or department. In contrast to a data warehouse, a data mart usually only contains a subset of the data that is relevant to the specific requirements of a department. Data marts can exist independently or function as part of a larger data warehouse system.
Data analysis and processing
Data analysis and processing are at the core of business intelligence and refer to the transformation of raw data into valuable insights that can be used for decision making. There are various methods and techniques for analyzing and processing data:
OLAP (Online Analytical Processing):
OLAP is a technique that enables multidimensional data to be analyzed quickly. It supports complex queries and allows users to analyze data from different perspectives and at different levels of aggregation.
Data Mining:
Data mining refers to the process of identifying patterns, trends and correlations in large data sets using statistical and machine learning algorithms. Data mining techniques can be used to create predictive models, analyze customer behavior or detect anomalies.
Text Mining and Natural Language Processing:
Text mining is the analysis of unstructured text data in order to gain patterns and insights. Natural Language Processing (NLP) is a sub-discipline of artificial intelligence that focuses on the processing and analysis of natural language. Both techniques can be used in combination to carry out sentiment analysis, automated text summaries or the extraction of information from documents, for example.
Reporting and visualization
Reporting and visualization are important aspects of business intelligence, as they make it easier for decision-makers to access the insights gained. There are various techniques and tools for displaying information:
Dashboards:
Dashboards are interactive user interfaces that display key performance indicators (KPIs), charts and tables to convey the current status of business processes and goals at a glance. Dashboards can be updated in real time and enable users to make decisions quickly and efficiently.
Key Performance Indicators (KPIs):
KPIs are quantifiable metrics that measure the success of a company, department or project in relation to defined goals. KPIs help decision-makers to monitor performance and take targeted improvement measures.
Geospatial Analytics:
Geospatial analytics enables the analysis and visualization of geographic data in order to identify spatial relationships and patterns. This technology can be used, for example, to carry out market analyses, optimize sales territories or make location decisions.
Overall, the main components of Business Intelligence form a complex and integrated system that aims to help companies analyze their data and make informed, data-based decisions. By combining data integration, data storage, data analysis and processing as well as reporting and visualization, companies can optimize their business processes, identify new business opportunities and increase their competitiveness.
Business intelligence tools
In recent years, a large number of business intelligence systems and tools have been developed to support companies in implementing their BI strategies. These systems and tools differ in terms of their functionalities, user-friendliness, scalability and costs. Choosing the right BI solution depends on the specific requirements and objectives of a company.
There are several categories of BI applications, such as self-service BI, mobile BI or embedded BI. Self-service BI tools enable end users to perform data analyses and create reports without the support of IT experts. Mobile BI tools provide access to BI functions via mobile devices, while embedded BI tools integrate BI functionalities directly into existing business applications.
When selecting a business intelligence tool, companies should consider various criteria, such as user-friendliness, integration with existing applications, scalability, security and costs. Companies should also consider the requirements of their users, the IT infrastructure and the availability of resources for implementation and maintenance.
Some examples of business intelligence software are QlikView, Microsoft Power BI, Tableau, SAP BusinessObjects and IBM Cognos Analytics. These BI solutions offer a wide range of functions for data integration, data storage, data analysis and processing as well as reporting and visualization. Each of these tools has its own strengths and weaknesses, so the selection of the right tool depends on the individual requirements and preferences of the company.
QlikView is particularly characterized by its associative data analysis, which enables users to quickly and easily identify connections between different data points.
Fields of application and industries
Business Intelligence (BI) has gained importance in numerous industries and fields of application as companies from various sectors recognize the benefits of data-driven decision-making. The following sections provide an overview of some of the most important fields of application and sectors in which BI is used.
Financial services
In the financial sector, banks, insurance companies and investment companies use business intelligence systems to assess risks, optimize the profitability of investments and detect fraudulent activities. Business intelligence helps to analyze large volumes of financial data and make well-founded decisions based on historical and current information.
Retail trade
Retailers use business intelligence systems to better understand customer behavior and preferences, optimize stock levels, develop sales strategies and create sales forecasts. BI tools allow retailers to analyze data from various sources such as POS systems, e-commerce platforms and loyalty programs to create targeted marketing campaigns and increase customer satisfaction.
Healthcare
In healthcare, BI is used to analyze patient data, improve the quality of medical care, increase the efficiency of hospitals and reduce costs. BI systems help healthcare providers to recognize patterns in patient data, evaluate treatment outcomes and identify emerging health trends. BI solutions also enable better resource planning, personnel management and the analysis of performance indicators in the healthcare industry.
Manufacturing
Manufacturing companies use BI solutions to optimize production processes, improve the efficiency of supply chains and predict the maintenance of machinery and equipment. By analyzing production data, inventory levels, quality control and supplier information, manufacturing companies can make informed decisions to reduce costs, improve product quality and optimize customer service.
Telecommunications
In the telecommunications industry, BI is used to analyze network performance and utilization, increase customer satisfaction and loyalty, and identify new business opportunities. BI tools enable telecommunications providers to analyze data from various sources such as network traffic data, billing systems and customer service requests. This leads to improved network planning, more efficient use of resources and the development of targeted marketing strategies.
Energy and supply
Energy and utility companies use business intelligence to better monitor energy consumption, increase plant efficiency and promote the use of renewable energies. By analyzing consumption data, weather conditions and system data, energy companies can optimize their operations, forecast energy consumption and develop sustainable energy systems.
Logistics and transportation
Logistics and transport companies use BI software to optimize supply chains, monitor the flow of goods and improve the efficiency of transport routes. Business intelligence helps analyze traffic and shipping data to make informed business decisions about planning and managing supply chains, warehousing and transport capacity.
Trends and the future of business intelligence
The rapid development of technologies and the increasing importance of data in the business world are leading to new trends and developments in the field of Business Intelligence (BI).
Artificial intelligence and machine learning
The integration of artificial intelligence (AI) and machine learning into BI systems enables companies to perform complex data analyses, automatically recognize patterns and correlations in data and create predictive models. The use of AI and machine learning in BI tools is expected to continue to grow as organizations look for ways to improve their data analytics capabilities and become more competitive.
Augmented analytics
Augmented analytics is an emerging trend in BI that uses AI and machine learning to automate the process of analyzing and interpreting data. This enables users to carry out complex analyses with little technical expertise and helps companies to make faster and more precise data-based decisions.
Data-Driven Culture
Establishing a data-driven corporate culture is becoming increasingly important in order to maximize the benefits of business intelligence. Companies that recognize the value of data and invest in developing BI strategies are better positioned to make informed business decisions and increase their competitiveness. This also includes training employees in data analysis and interpretation in order to promote the effective use of BI tools throughout the company.
Cloud-based BI solutions
With the increasing spread of cloud computing technologies, the implementation of cloud-based BI solutions is also gaining in importance. Cloud-based BI systems offer numerous advantages, such as cost savings, increased scalability, flexibility and improved collaboration. This trend is expected to continue as more and more companies make the switch to cloud-based solutions.
Mobile BI and real-time analyses
Access to BI tools via mobile devices and the ability to perform real-time analysis are increasingly important trends in the BI landscape. Mobile BI enables decision-makers to access important information and data anytime and anywhere.
