Structured data are datasets with strong and consistent organization. Structured data is managed with structured query language (SQL), where users can easily search and edit data.
Structured data is organized into rows and columns with known and predictable content. Each column contains a specific type of data, such as date, text, money, or percentage. Data that does not match the data type of this column is rejected as an error.
Relational database tables and spreadsheets typically contain structured data. The high level of semantic structure combines master data and historical data into a data model. Data model subject areas include topics such as customers, inventory, sales transactions, prices, and suppliers. Structured data is easy to use, and data integrity can be strengthened. When large amounts of past phenomena are captured, structured data becomes big data.
Because structured data makes the editing process simple and fast, it can be easily understood by machine learning algorithms. Queries are also made easier by users who can access, understand and interpret the data.
Structured data handles highly organized quantitative data managed with SQL databases, while unstructured data handles qualitative data that does not use predefined databases that are managed in the best NoSQL databases (such as MongoDB). All important business processes and decisions are based on structured data. Data warehouses are the basis of data lakes and applications. When integrated into a data model, structured data provides fast and great business value.
Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records.
Data Privacy refers to the secure and confidential protection of personal or sensitive data of individuals or organizations during the collection, storage, sharing and processing of personal or sensitive data.
The concept of digital transformation has been supported by many industry experts since 2012, allowing companies to update their business models. Technologies such as data analytics tools, artificial intelligence and cloud computing services are contributing to the development of digital transformation in companies.
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