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There are three forms of data in business analytics: structured, unstructured, and semi-structured. Of these, structured data has the history of being the most popular. There is an explosion of analytical tools that help process large volumes of structured data with a view to improving business processes. We need to increase the use of the other two forms of data in analytics.
Let us understand what the three forms of data are, and why it's been hard to leverage unstructured and semi-structured data in businesses to date.
Structured data is data we can store information in a pre-defined format.
Data like names, addresses, transactions and orders are all examples of structured data points. A blockchain can also create structured data in the form of records of transactions. New information becomes a part of a new block and then a chain.
Machines and humans both generate structured data.
Structured data is often stored in a SQL database, which organizes the data into a set of tables, that follow a set schema or data model. A schema or data model defines the format of data and which columns exist in which tables, and how these are connected. SQL databases are often referred to as a relational database, as the data is interdependent and often stored across multiple tables which have relationships between one another. For example, you could have an order table and a user table, with the connection being based on an order id that a user has placed the in the past.
Structured data often lives in a Data Warehouse, or Spreadsheets like Excel or Google Docs.
Unstructured data is raw and exists in free form. By default, this data does not follow a set schema or data model, and thus cannot be easily organized into a relational database.
Unstructured data is more qualitative than quantitative. Some examples of unstructured data include text message content, images and customer support tickets. We can sort it into relational databases, but there is an additional layer of processing required to add the structure necessary to sort the data into a database
A data lake is a unique storage system, also called an unstructured data warehouse. Most organizations use business analytics software to analyze unstructured data.
Unlike structured data, semi-structured data has hierarchies of nested information and does not neatly fit in o a defined schema. An example of semi-structured data might be an email, which has some structured data points associated with it like sender, time of day and folder. However, the content of the message itself is unstructured text. CSV, XML and JSON are other examples on semi-structured data.
In recent years there has been an explosion of tools that help you analyze structured data. However, the study of unstructured data is also important as it gives indicators that can help improve many aspects of your product and customer experience, including:
Other examples of how structured data is typically leveraged include:
Blaze helps with analyzing both structured and unstructured data, to help you grow your user base efficiently. Visit our website to understand more, or schedule a demo with us today.