What Is Structured Data?
What Is Unstructured Data?
|Structured data is typically stored in tabular form and managed in a relational database (RDBMS). Fields contain data of a predefined format. Some fields might have a strict format, such as phone numbers or addresses, while other fields can have variable-length text strings, such as names or descriptions.
Structured data might be generated by either humans or machines. It is easy to manage and highly searchable, both via human-generated queries and automated analysis by traditional statistical methods and machine learning (ML) algorithms.
Structured data is used in almost every industry. Common examples of applications that rely on structured data include customer relationship management (CRM), invoicing systems, product databases, and contact lists.
|Unstructured data includes various content such as documents, videos, audio files, posts on social media, and emails. These data types can be difficult to standardize and categorize.
Unstructured data often consists of data collections rather than a clear data element—for example, a document with thousands of words addressing multiple topics. In this case, the document’s contents cannot easily be defined as one entity. Generally, tools that handle structured data cannot parse unstructured documents to help categorize their data.
Unstructured data is manageable, but data items are typically stored as objects in their original format. Users and tools can manipulate the data when needed; otherwise, it remains in its raw form—a process known as schema-on-read.
Structured Data Pros and Cons
Pros of structured data:
- Easy to use for business users—structured data can be used by business users who understand the subject matter related to the data. It is useful for entry level users with access to basic tools like Excel, and can be even more useful for power users familiar with SQL or business intelligence (BI) tools.
- Extensive tools support—structured data is several decades old and most data management and analytics tools support it. There is a huge variety of RDBMS, data analytics, and big data management tools for structured datasets.
- Instantly usable—structured data can be used, with no further processing, by a variety of business processes. For example, customer data in structured form can be visualized and manipulated by a CRM system.
Cons of structured data:
- Data preparation—data often needs to undergo complex transformations before it can enter a flexible data store.
- Not flexible—structured data requires users to create schema data definitions in advance. It is difficult to change the structure over time, and because there is a fixed, predefined structure, data can only be used for its intended purpose. This limits the use cases that can be served by structured data.
- High overhead—structured data is often stored in data warehouses, which can store structured data at large scale and enable fast access for user queries. A data warehouse is a complex system requiring significant resources to run, operate and maintain.
- Complex data structures—as organizations grow, the number of databases, tables, and fields grows exponentially. It becomes difficult to manage structured data, and it is common to have overlaps between datasets, redundant data, and stale or low quality data.
Unstructured Data Pros and Cons
Pros of unstructured data:
- Native format—unstructured data can be stored in its native format until needed, with no pre-processing.
- Flexible—unstructured data can be used for many different purposes and can contain a much wider variety of data, including textual data, images, videos, and source code.
- Low overhead—unstructured data can be stored and processed at much lower cost using elastically scalable data lakes.
Cons of unstructured data:
- Lack of visibility—it is difficult to tell what is stored in a data lake and whether the data is useful. Data lakes can turn into “data swamps” with large amounts of data, which is not useful for the organization, yet incurs costs to store and manage it.
- Requires advanced analytics—there is typically a need for data science skills and advanced algorithms to analyze and extract insights from unstructured data. This also means it is not useful for most business users, who do not have the skills to perform advanced analytics.
- Requires dedicated tools—retrieving and processing unstructured data requires specialized tooling and expertise.
Structured Data vs. Unstructured Data: Key Differences
The following elements differentiate structured and unstructured data.
Usually, structured data is in the form of numbers and text, presented in standardized, readable formats. XML and CSV are the most popular formats. In structured data models, the data format is predetermined. On the other hand, unstructured data often comes in various shapes and sizes. It does not conform to a predefined data model and stays in the native (original) formats. Examples include video (i.e., WMV, MPW) and audio files (i.e., MP3, WAV)
Structured data follows a predefined relational data model describing the relationship of data elements. Unstructured data does not have a set data model but can have a hidden structure.
Organizations store structured data in relational databases. Data warehouses help centralize large volumes of stored structured data from different databases. Organizations store unstructured data in raw formats, not in databases. Data lakes can store large amounts of unstructured data.
Structured data typically resides in a relational database, arranged in tables with rows and columns. Labels specify the data types. A table’s schema consists of the data column and type configuration. Relational databases process data using SQL, an easy syntax for users to read.
Unstructured data often resides in a non-relational (NoSQL) database. This database type stores multiple data models without tables—this is usually a document, wide-column, graph, and key-volume database. It can process large data volumes and handle high loads. A NoSQL database contains collections of documents that resemble rows but don’t use a tabular schema, so there can be different data types in the same collection. The non-relational model enables faster queries.
Searchability and Ease of Use
Structured data is usually easier to search and use, while unstructured data involves more complex search and analysis. Unstructured data requires processing to understand it, such as stacking before placing it in a relational database. Structured data is older, so there are more analytics tools available. Standard data mining solutions cannot handle unstructured data.
Quantitative vs. Qualitative
Structured data is quantitative, meaning that it has countable elements. It is easier to analyze by classifying items based on common characteristics, investigating the relationships between variables, or clustering the data into attribute-based groups.
Unstructured data is qualitative, meaning the information it contains is subjective, and traditional analytics tools and methods can’t handle it. For example, customer feedback on social media can generate data in text form, requiring advanced analytics to process it. Techniques include splitting and stacking data volumes into logical groupings, data mining, and pattern detection.
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