For business decisions, data is key. The capacity of an organization to collect the correct information, analyze it and take action is what defines its performance level. But the volume of data accessible to enterprises, as well as the multiple forms of data available, is growing. Business info, from purely developed relational databases to your last tweet, comes in a wide range of formats. In all its various formats, all this data can be grouped into two major categories: structured data and unstructured data. Start your 30-day free trial today.
What Is Structured Data?
In relational databases (RDBMS), structured data normally resides. Fields store Social Security numbers, phone numbers or ZIP codes with length-delineated data. Also, variable-length text strings such as names are included in records, making them possible to search. As long as the data is produced within an RDBMS system, data may be machine or human-generated. Using data type and field names, such as numerical or alphabetical, date or currency, this format is eminently searchable both through human-generated queries and by algorithms.
Airline reservation systems, sales transactions, inventory management and ATM operation are typical relational database applications of structured data. Inside relational databases, Structured Query Language (SQL) makes queries on this kind of structured data. Some relational databases point to unstructured or stored information, such as applications for customer relationship management (CRM). At best, the incorporation can be uncomfortable, as memo fields do not loan themselves to conventional queries from the database. Also, some of the data from the CRM is structured.
What Is Unstructured Data?
Unstructured data is everything else in reality. The internal structure of unstructured data is not organized through pre-defined data types or schema. It may be generated by humans or machines or maybe textual or non-textual. It can also be stored like NoSQL inside a non-relational database. Typical unstructured data generated by humans involves:
- Social Media: Data from Twitter, Facebook, LinkedIn.
- Mobile Data: Locations, text messages.
- Website: Instagram, YouTube, photo sharing sites.
- Text files: Word processing, email, presentations, spreadsheets, logs.
- Business Applications: Productivity applications, MS Office documents.
- Communications: phone recordings, Chat, IM, collaboration software.
- Media: MP3, video and audio files, digital photos.
- Email: Thanks to the metadata, email has some inner structure, and we often refer to it as semi-structured. Its message area is unstructured. However, it cannot be parsed by traditional analytics tools.
Typical unstructured data generated by machine involves:
- Digital Surveillance: Photos and video of Surveillance.
- Satellite Imagery: Landforms, weather data, military movements.
- Sensor Data: Traffic, oceanographic sensors, weather.
- Scientific Data: Space exploration, oil and gas exploration, atmospheric data, seismic imagery.
Differences Between Structured vs Unstructured Data
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Qualitative vs Quantitative Data
Structured data is also quantitative data, which means that hard numbers or items that can be counted typically consist of it. Regression (to analyze relations between variables), data clustering (based on various attributes) and classification (to evaluate probability) are the techniques for analysis.
On the other hand, unstructured data is also classified as qualitative data, and traditional techniques and approaches cannot be used to interpret and process it. Qualitative data can come from consumer reviews, interviews and social media posts in a business context. Advanced analytics strategies such as data mining and data stacking are needed to derive information from qualitative data.
Defined vs Undefined Data
Clearly defined forms of data in a structure are structured data, while unstructured data is typically processed in its native format. In rows and columns, structured data lives and can be mapped into predefined fields. Unstructured data does not have a predefined data type, unlike structured data, which is standardized and convenient to access in relational databases.
Ease of Analysis
How well it lends itself to understanding is one of the most fundamental distinctions between structured and unstructured data. Structured data, both for humans and for algorithms, is simple to search for. On the other hand, unstructured data is generally harder to search for and requires processing to make it comprehensible. Deconstruction is difficult when it lacks a predefined data model and thus does not fit into relational databases.
Although there is a wide variety of advanced structured data analytics tools, most of the analytics tools for processing and mining unstructured data are still in the development stage. The absence of a predefined framework makes data mining challenging, and it is a challenge to build best practices for how to manage data sources such as blogs, rich media, consumer communication and social media.
Storage in Data Houses vs Data Lakes
In data warehouses, structured data is mostly stored. In data lakes, unstructured data is stored. The endpoint for the data's journey through an ETL pipeline is a data warehouse. On the other hand, a data lake is a nearly limitless archive where data is stored in its native form or after a specific "cleaning" process is performed.
Both have cloud-use potential. Structured data takes less space for storage, whereas more is needed for unstructured data. Such a tiny image, for example, takes up more space than several pages of text. For the databases, structured data is commonly stored in a relational database (RDBMS), while so-called non-relational or NoSQL databases are the better fit for unstructured data.
Variety of Formats vs Predefined Format
Texts and numbers are the most common method for structured data. In a data model, structured data has been described beforehand. On the other hand, unstructured data comes in a number of sizes and shapes. From video, audio and imagery to email and info of the sensor, it may consist of anything. The unstructured data has no data model; it is stored in a data lake or natively that does not need any transformation.
To analyze unstructured data, new tools are available, especially provided specific use case parameters. These methods are mostly based on machine learning. Machine learning can also be used for structured data analytics, but it requires a large volume of several different types of unstructured data. No matter what the company specifications are, whether the data is structured or unstructured, today's aim is to tap business value. The needed knowledge is provided by good certifications like the Hadoop certification.