The 10 most important terms in BI

1. Database

A database is an organized system for collecting, storing, and managing data. The data is typically organized into fields, records, and tables so that it is easy to find and use.
For example, such a database can store a list of customers with their contact details so that company employees can quickly retrieve contact information when needed.
Databases play a central role in the world of business intelligence, as they form the basis for data storage and retrieval on which BI tools are built.
Various management systems are used to manage databases. These are called DBMS (Database Management Systems). They store data in a database and provide a way to interact with the data.

Examples of widely used database systems are

• Relational database management systems (RDBMS): Systems such as MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server, which are based on the relational model.
• NoSQL databases: Systems such as MongoDB, Cassandra, and Redis, which are optimized for specific use cases and are not based on the relational model.
  BI systems are typically based on relational database management systems.

2. SQL

SQL (Structured Query Language) is a specialized programming language developed for communicating with relational databases. It enables users to create and manage databases and run queries. SQL is used to retrieve, update, insert, and delete data, as well as to define and manipulate database objects such as tables, indexes, and views. SQL can also be used to formulate complex queries to select, filter, and analyze specific data records.

3. ETL / ELT

ETL is a basic process that extracts data from various sources, transforms it, and then loads it into a target system, such as a data warehouse or database.

• Extraction: Data is extracted from various sources, such as databases, files, or APIs. This can include structured data from relational databases, unstructured data from text files, or even semi-structured data from web services.
• Transformation: The extracted data is transformed to prepare it for analysis and storage. This often involves cleaning the data, removing duplicates, adjusting data formats, and adding calculations or aggregations.
• Loading: The transformed data is loaded into the target system, where it can be used for analysis and reporting. This can take place in a data warehouse, a data lake, or another database platform.

While in an ETL process the data is transformed before being loaded, in an ELT process the order is reversed, so that the raw data is first loaded into the target system and transformed there. This takes advantage of the high computing power of modern data warehouses and big data platforms.

4. Primary Key

A primary key is a column or a group of columns in a database table that uniquely identifies each data element in that table. The primary key has two important properties: uniqueness and immutability.

• Uniqueness: Each value in the primary key must be unique and may not appear more than once in the table. This ensures that each data element can be uniquely identified.
• Immutability: The primary key value of a data element should not change as long as the data element exists. This ensures the consistency of the identification.

The primary key plays an important role in database management because it ensures data integrity and serves as a unique identifier for records. In relational databases, the primary key is often indexed to make data access more efficient. This ensures that relationships between records remain correct.

5. Data Warehouse / Data Lake

A Data Warehouse is a central database specifically designed for analysis and reporting. It stores data from various operational systems and external sources in a structured and consistent format. To this end, the data warehouse regularly extracts data, which is then validated, cleaned, formatted, and reconciled with existing information. This enables complex queries and analyses, supports decision-making, and provides historical data for trend analysis. The data is stored in a structured format, such as tables, and is regularly updated through ETL processes.
In comparison, a data lake stores large amounts of raw data in its native form, including structured, semi-structured, and unstructured data.

6. OLAP

OLAP is at the heart of BI because it is a technology that enables users to analyze large volumes of multidimensional data quickly and interactively. OLAP systems support complex queries and allow data to be viewed from different perspectives by organizing it into cubes that contain various dimensions (e.g., time, geography, product). This structure makes it easier to identify trends, patterns, and anomalies and to make informed decisions. Typical OLAP operations include rolling up, drilling down, slicing, and pivoting data.

7. Data Mining

Data mining is a process that uses statistical and machine learning methods to discover patterns, trends, and insights in large amounts of data. The goal of data mining is to identify hidden connections and relationships within the data that can help make predictions or improve decision-making processes. Typical applications of data mining include customer segmentation, trend forecasting, identifying patterns in behavioral data, and detecting anomalies or outliers. Data mining is often used in combination with other analytical techniques, such as machine learning and statistical analysis, to gain valuable insights from the data.

8. Dashboard

A dashboard is a graphical user interface that displays consolidated data, key figures, and performance indicators in real time or with periodic updates. Dashboards are therefore used for data visualization and to support decision-making. For this reason, they are typically designed to give users a quick and intuitive overview of the current status and performance of a company or a specific business unit. Presenting data in graphs and charts makes it particularly easy to identify relationships, patterns, trends, and progress over time. By consolidating data from various sources, dashboards can provide users with a comprehensive overview. In doing so, the Dashboards an die Bedürfnisse der Nutzer angepasst werden.

9. KPI

A KPI is a measurable metric that quantifies and evaluates the performance of a company, a department, or a process. KPIs are used to track progress toward business goals, identify performance trends, recognize strengths and weaknesses, and evaluate the success of initiatives or strategies. KPIs can be used in various areas of a company, such as sales, marketing, finance, or production, and are typically visualized in dashboards or reports to enable decision-makers to monitor performance quickly and easily.

10. Data Analysis

Data analysis is the process of systematically examining data to derive useful information and insights from it. From descriptive analyses, which describe past events, to diagnostic analyses, which help you identify the causes of events, to predictive analyses, which forecast future events, to prescriptive analysis, which provides recommendations for action—BI systems can support a wide variety of analyses. You can find detailed information on the different types of analysis here.

Conclusion

Business Intelligence improves data-driven decision-making in companies through the use of various concepts and technologies. Familiarity with terms such as database, SQL, ETL/ELT, primary key, data warehouse/data lake, OLAP, data mining, dashboard, KPI, and data analysis is helpful for using BI software effectively. These terms form the foundation for the collection, integration, analysis, and visualization of data, which help companies gain valuable insights and make informed decisions.
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