The 10 most important terms in BI

1. Database

A database is an organized system for collecting, storing, and managing data. The data is usually organized in fields, records, and tables in such a way 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 administer databases. These are called DBMS (Database Management System). They store the data in a database and offer a way of interacting with the data.

Examples of widely used database systems are

• Relational database 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 generally based on relational database systems.

2. SQL

SQL (Structured Query Language) is a special programming language that was developed for communication with relational databases. It enables users to create and manage databases and perform 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 sets.

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 cleansing data, removing duplicates, customizing 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 the data is transformed before loading in an ETL process, the order is reversed in an ELT process so that the raw data is first loaded into the target system and only transformed there. This utilizes the high computing power of modern data warehouses and big data platforms.

4. Primary Key

A primary key is a column or 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 must not occur more than once in the table. This means 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 as it ensures the integrity of the data and acts as a unique reference to data records. In relational databases, the primary key is often provided with an index to make access to the data more efficient. This ensures that relationships between data records remain correct.

5. Data Warehouse / Data Lake

A data warehouse is a central database designed specifically for analysis and reporting. It stores data from various operational systems and external sources in a structured and consistent form. The data warehouse regularly extracts data for this purpose, which is then validated, cleansed, formatted, and compared with existing information. This enables complex queries and analyses, supports decision-making, and provides historical data for trend analyses. The data is stored in a structured format such as tables and is regularly updated using 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 the core of BI because it is a technology that enables users to analyze large amounts of multidimensional data quickly and interactively. OLAP systems support complex queries and make it possible to view data from different perspectives by organizing data into cubes that contain different dimensions (e.g. time, geography, product). This structure makes it easier to recognize trends, patterns, and anomalies and to make informed decisions. Typical OLAP operations include summarizing (roll-up), detailing (drill-down), filtering (slicing), and rotating (pivoting) data.

7. Data Mining

Data mining is a process by which patterns, trends, and findings in large amounts of data can be discovered using statistical and machine learning methods. Data mining aims to identify hidden connections and relationships between data that can help to make predictions or improve decision-making processes. Typical applications of data mining include segmenting customers, predicting trends, finding patterns in behavioral data, or detecting anomalies or outliers. Data mining is often used in combination with other analysis 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 is updated periodically. Dashboards are therefore used to visualize data and support decision-making. They are therefore usually designed to provide users with a quick and intuitive overview of the current status and performance of a company or a specific business unit. The display in graphs and charts makes it particularly easy to recognize relationships, patterns, trends, and progress over time. By bringing together data from different sources, dashboards can provide users with a comprehensive overview. The dashboards can be adapted to the needs of the user.

9. KPI

A KPI is a measurable indicator that quantifies and evaluates the performance of a company, department, or 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 different areas of a business, such as sales, marketing, finance, or production, and are typically visualized in dashboards or reports to allow decision-makers to quickly and easily monitor performance.

10. Data analyses

Data analysis is the process of systematically examining data to gain useful information and insights. From descriptive analyses, which describe past events, to diagnostic analyses, which help you to identify the causes of events, to predictive analyses, which predict future events, to prescriptive analyses, which provide recommendations for action, BI systems can enable a variety of analyses. Detailed information on the different types of analysis can be found here.

Conclusion

Business intelligence improves data-driven decision-making in companies by using various concepts and technologies. Knowledge of terms such as database, SQL, ETL/ELT, primary key, data warehouse/data lake, OLAP, data mining, dashboard, KPI and data analytics helps use BI software effectively. These terms form the basis for the collection, integration, analysis, and visualization of data that helps companies gain valuable insights and make informed decisions.
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