Business Intelligence
Basics of Business Intelligence (BI)
Business Intelligence (BI) refers to the process of collecting, analyzing and processing data to make informed 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 according to perspective and application area. 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 various 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 computer-based information systems were developed. Since then, both technologies and methods for data analysis and processing have continued to evolve to meet the growing needs of businesses and the increasing amount of data available.
The goals and benefits of business intelligence are many 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 maximize business success through data-driven decisions and continuous improvement.
Main components of Business Intelligence
An effective BI system is made up of several main components that are tightly interconnected to enable a smooth process from data collection to decision making. The main components of business intelligence include data integration, data warehousing, data analysis and processing, and reporting and visualization.
Data integration
Data integration is a key aspect of business intelligence and refers to the process of collecting, merging and preparing data from various internal and external sources. The ETL (Extract, Transform and Load) process is a commonly used data integration method:
Extraction:
This involves collecting data from various sources such as ERP systems, CRM applications, social media platforms and external databases.
Transformation:
Raw data is processed, cleaned, and put into a consistent format to eliminate inconsistencies and ensure a consistent database for analysis.
Loading:
The transformed data is transferred to the data warehouse or data marts to enable centralized storage and analysis of the data.
Data storage
Data warehousing plays a crucial role in business intelligence systems, as it forms the basis for analyzing and processing the collected information. The main data storage concepts are:
Data Warehouse:
A data warehouse is a central database that stores data from various sources in a unified format. It enables long-term storage, analysis and retrieval of large amounts of historical and current data. Data warehouses are known for their scalability and ability to support complex queries and analytics.
Data Mart:
A data mart is a specialized database focused on a specific business unit or department. Unlike a data warehouse, a data mart usually contains only a subset of the data relevant to a department’s specific needs. Data marts can exist independently or function as part of a larger data warehouse system.
Data analysis and processing
Data analytics 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 data analysis and processing:
OLAP (Online Analytical Processing):
OLAP is a technique that enables the rapid analysis of multidimensional data. 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 relationships in large data sets using statistical and machine learning algorithms. Data mining techniques can be used to build predictive models, analyze customer behavior, or detect anomalies.
Text Mining and Natural Language Processing:
Text mining is the analysis of unstructured text data to discover patterns and insights. Natural Language Processing (NLP) is a sub-discipline of Artificial Intelligence that focuses on natural language processing and analysis. Both techniques can be used in combination to perform, for example, sentiment analysis, automated text summarization, or extraction of information from documents.
Reporting and visualization
Reporting and visualization are important aspects of business intelligence because they make it easier for decision makers to access the insights gained. There are various techniques and tools for presenting 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, enabling 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 monitor performance and take targeted improvement actions.
Geospatial Analytics:
Geospatial Analytics enables the analysis and visualization of geographic data to identify spatial relationships and patterns. This technique can be used, for example, to perform 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 warehousing, data analysis and processing, and reporting and visualization, companies can optimize their business processes, identify new business opportunities, and increase their competitiveness.
Business Intelligence Tools
In recent years, a variety of business intelligence systems and tools have evolved to help companies implement their BI strategies. These systems and tools differ in terms of their functionalities, usability, scalability and cost. Choosing the right BI solution depends on a company’s specific requirements and goals.
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 analysis and generate reports without the assistance of IT experts. Mobile BI tools provide access to BI functionality via mobile devices, while embedded BI tools integrate BI functionality directly into existing business applications.
When selecting a business intelligence tool, companies should consider several criteria, such as ease of use, integration with existing applications, scalability, security and cost. In addition, companies should consider the requirements of their users, IT infrastructure and the availability of resources for implementation and maintenance.
Some examples of business intelligence software include QlikView, Microsoft Power BI, Tableau, SAP BusinessObjects and IBM Cognos Analytics. These BI solutions offer a wide range of data integration, data warehousing, data analysis and processing, and reporting and visualization capabilities. Each of these tools has its own strengths and weaknesses, so selecting the appropriate tool depends on the company’s individual requirements and preferences.
QlikView is particularly notable for its associative data analysis, which allows users to quickly and easily identify connections between different data points.
Fields of application and industries
Business intelligence (BI) has gained traction across a wide range of industries and application areas as companies across sectors realize the benefits of data-driven decision-making. The following sections provide an overview of some of the key application areas and industries where BI is used.
Financial Services
In the financial industry, banks, insurance companies, and investment firms use business intelligence systems to assess risk, optimize asset profitability, and detect fraud activity. Business intelligence helps analyze large volumes of financial data and make informed decisions based on historical and current information.
Retail
Retailers use business intelligence systems to better understand customer behavior and preferences, optimize inventory levels, develop sales strategies, and forecast sales. BI tools enable retailers to analyze data from multiple sources, such as point-of-sale 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 hospital efficiency and reduce costs. BI systems help healthcare providers identify patterns in patient data, evaluate treatment outcomes, and identify emerging health trends. In addition, BI solutions enable better resource planning, human resource management, and performance indicator analysis in the healthcare industry.
Manufacturing
Manufacturing companies use BI solutions to optimize production processes, improve supply chain efficiency, and predict machine and equipment maintenance. 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 telecom 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 are using business intelligence to better monitor energy consumption, increase asset efficiency and promote the use of renewable energy. By analyzing consumption data, weather conditions and plant data, energy companies can optimize their operations, forecast energy consumption and develop sustainable energy systems.
Logistics and transport
Logistics and transportation companies use BI software to optimize supply chains, monitor the flow of goods, and improve the efficiency of transportation routes. Business intelligence helps analyze traffic and shipping data to make informed business decisions about planning and managing supply chains, warehousing, and transportation capacity.
Trends and future of Business Intelligence
The rapid development of technologies and the increasing importance of data in the business world lead to new trends and developments in the field of Business Intelligence (BI).
Artificial intelligence and machine learning
Integrating artificial intelligence (AI) and machine learning into BI systems enables companies to perform complex data analysis, automatically identify patterns and relationships in data, and build predictive models. The use of AI and machine learning in BI tools is expected to continue to grow as companies 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 data analysis and interpretation. This enables users to perform complex analysis with little technical expertise and helps companies make faster, more accurate data-driven decisions.
Data-Driven Culture
Establishing a data-driven business culture is becoming increasingly important to get the maximum value from 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 includes training employees in data analysis and interpretation to promote the effective use of BI tools throughout the organization.
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 benefits, including cost savings, increased scalability, flexibility, and improved collaboration. This trend is expected to continue as more companies make the move to cloud-based solutions.
Mobile BI and real-time analytics
Access to BI tools via mobile devices and the ability to perform real-time analytics are increasingly important trends in the BI landscape. Mobile BI enables decision makers to access important information and data anytime, anywhere.
