Data itself is a collection of facts, figures, or other information that can be used to gain insights, make decisions, or inform analysis. Data can come in many different forms, including structured data, unstructured data, and semi-structured data. Structured data refers to data that is organized into a specific format, such as a database table with defined columns and rows. Unstructured data refers to data that does not have a specific structure, such as text or images. Semi-structured data refers to data that has some structure, but not as much as structured data, such as a document with defined sections or tags.

Examples of structured data include:

• Sales data: A table of sales data that includes information such as the date of the sale, the product sold, and the price of the product.

• Financial data: A spreadsheet of financial data that includes information such as revenue, expenses, and profits.

• Customer data: A database of customer data that includes information such as name, address, and purchase history.

Examples of unstructured data include:

• Text data: Customer reviews or social media posts.

• Image data: Photographs or satellite imagery.

• Audio data: Recorded interviews or phone calls.

Examples of semi-structured data include:

• Web data: HTML documents that include defined tags and sections.

• Email data: Emails that include defined fields such as sender, recipient, and subject.

Data by itself is not useful – it needs to be analyzed and interpreted in order to be valuable. As a data analytics company, we analyze data, and to use that analysis to provide insights and recommendations to stakeholders. This involves a range of techniques and tools, including statistical analysis, machine learning, and data visualization.