The advent of distributed processing and the ability to process virtually any amount of data has led to the unforeseen importance of Unstructured Data in modern data architecture. Such advances have negated the need for data to be tagged as attributes or indexed in order to get meaningful insights out of it. Distributed processing when combined with the ability to process data through neural networks has made it possible to even analyze textual, image, and audio-based data.
In spite of all these advancements, it is still not easy to process Unstructured Data when compared to Structured Data. Since Unstructured Data is now a critical factor in Enterprise Data Architecture and it is imperative that architects have a clear picture of what is possible and what is not using Structured and Unstructured Data. This post compares Structured vs Unstructured Data based on various criteria relevant to the topic.
Table of Contents
What is Structured Data?
Structured Data is data that has been cataloged into attributes and indexed for easy access. The assurance that all rows will have predefined attributes and the important ones will be indexed for faster search makes it possible for complicated logic to be built using only SQL.
The problem with Structured Data is that the natural environment does not have a structure and there is a big effort in structuring the data. Such an effort often needs a lot of thought processes and manual effort. Anything that needs manual effort is not scalable.
More information regarding Structured Data can be found here.
What is Unstructured Data?
Unstructured Data is any data that may be relevant to the system and stored in the natural originating format itself. Common examples are natural language data, images, audio, etc. Analyzing them is effort-intensive and often needs large-scale computing power.
The increase in the importance of Unstructured Data has given rise to the concept of Data Lakes. Data Lakes are just places where data from different sources can be dumped without any kind of preprocessing to get it formatted or tagged. Often the only attributes related to Unstructured Data are the source and the time of capture.
A minor variation of Structured Data is the Semi-Structured Data where the data is tagged as attributes, but it is not all rows may not have all the attributes. Such data is often stored in NoSQL Databases or Graph Databases.
More information regarding Unstructured Data can be found here.
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Structured vs Unstructured Data: What’s the Difference?
The inherent differences between Structured and Unstructured Data mean they both require very different strategies for value to be extracted. You will now understand these differences on the basis of storage strategies, data manipulation strategies, retrieval strategies, and the skillset that is required to make the most of them.
|Properties||Structured Data||Unstructured Data|
|Formats||Several formats like|
CSV, XLM, and many more.
|A huge variety of formats include PDF,|
JPG, WMV, MP3, document, and many more.
|Data model||Pre-defined/ not flexible||Not pre-defined/ flexible|
|Storages||Data Warehouse ||Data Lakes|
|Databases||SQL Relational Databases||NoSQL Non-Relational Databases|
|Ease of search||Easy to search||Difficult to search|
|Tools and Technologies||RDBMS|
|NoSQL DBMSAI-driven tools|
Data Storage architectures
Data Visualization tools
|Specialists to handle data||Business Analysts, Software Engineers, Marketing Analysts||Data Scientists, Engineers, and Analysts|
The comparison has been made using the following criteria:
1) Structured vs Unstructured Data: Data Sources
Data that can be easily sorted and organized into little compartments is known as structured data. Data in Excel files, neatly split by categories and rows, is a universal example. Databases like RDBMS, MySQL, DB2, and OLTP Systems are some common data sources for structure data. Customer relationship management (CRM) platforms, association management systems (AMS), sales and finance data, and event registrations are all common structured data sources. Because structured data is standardized, analyzing metrics like customer satisfaction (CSAT) and Net Promoter Scores (NPS) is generally simple and uncomplicated.
It’s with unstructured data that things start to get interesting. It lacks standards by definition and frequently has fixed boundaries. It’s the bits and pieces acquired from documents, social media, emails, audio/visual files, open-ended response fields, notes fields, and other forms of content that are difficult to box and analyze using our conventional data analytics methods. It can be of any type, from text to numbers to graphics and sounds. Unstructured data is generated every time a customer, member, prospect, or stakeholder interacts with or discusses your company or brand.
2) Structured vs Unstructured Data: Flexibility
The biggest difference between Structured and Unstructured Data is in terms of flexibility. The strict structure means there is very little flexibility in the way data can be manipulated. This is especially true in the case of datatypes. For Structured Data, fields are tagged with datatypes in the very beginning itself. At times the same data can provide very meaningful insights if inference with different data.
Unstructured Data is not strictly tagged as attributes or datatypes and the schema is often inferred while reading according to the specific requirements during reading. This schema on write vs schema on reading philosophy gives a significant advantage to Unstructured Data in terms of flexibility.
3) Structured vs Unstructured Data: Storage
This section primarily discusses the main difference between SQL and NoSQL databases.
SQL Vs. NoSQL
Structured Data is usually stored in Relational Database Systems. There are a wide variety of Relations Databases available as free and open-source flavors as well as licensed ones. PostgreSQL, MariaDB, etc are examples of free Databases available. Oracle, SQL Server, etc. are the licensed versions available. In enterprises, if data from multiple sources in structured form has to be stored, Data Warehouses are used.
Unstructured Data is usually stored as flat files on hard disks or Cloud-based storage services like AWS S3, Azure Blob Storage, etc. Such Unstructured Data storage is termed a Data Lake. For Semi-Structured Data, NoSQL Databases like MongoDB, Cassandra, Hbase, etc are good candidates. Graph Databases like Neo4j, Titan, etc are also used in case data can be expressed as relationships.
4) Structured vs Unstructured Data: Data Manipulation
Data manipulation includes updating or deleting data in order to transform it into different forms. In the case of Structured Data, for data modification where the Destination is also a Relational Database, it is possible to have atomicity, consistency, and transaction support.
Such concepts are alien to Unstructured Data. Since Unstructured Data is mostly processed using distributed systems, transaction support and consistency are difficult to achieve. Some NoSQL Databases like MongoDB provide this to an extent in the case of smaller-scale operations.
5) Structured Vs Unstructured Data: Data Retrieval
Retrieving Structured Data is easy – both in terms of processing power needed as well as the speed of retrieval. This is because Structured Data can be easily indexed.
In the case of Unstructured Data, faster retrieval is possible if there is a Database or processing engine involved, but the processing power needed and the speed of retrieval are less.
6) Structured Vs Unstructured Data: Analysis of Performance
Analysis often involves data retrieval as well as manipulation. So this section will be a summary of the above two sections. It is pretty clear from the above sections that Unstructured Data has some disadvantages when it comes to data manipulation and retrieval.
It requires a lot more processing power and your access to hardware resources greatly affects your ability to extract value out of Unstructured Data.
An alternative is to spend considerable manual effort and create Structured Data out of Unstructured Data. But as it stands today, commodity hardware is a lot cheaper than human resources and hence hardware is the logical choice for extracting value.
7) Structured Vs Unstructured Data: Tools for Analysis
An SQL engine and a visualization tool are all that are required to make sense of Structured Data. Relational Database engines coupled with visualization tools like PowerBI, Tableau, etc get the job done in this case.
In the case of Unstructured Data, you either need to spend manual effort to convert it into Structured Data or use tools that can infer schema on reading.
Cloud-based analysis tools like AWS Athena, Google BigQuery, Azure Data Factory, etc can process your data and automatically catalog them. But almost all tools in this space are subscription-based paid services from Cloud providers like Amazon, Microsoft, etc. In case your data is textual, image or audio, you will also need deep learning frameworks to make sense of it.
8) Structured Vs Unstructured Data: Next-Gen Tools are Game Changers
Traditionally Structured Data analysis was done by business analysts and SQL was the primary skill set that was needed. Analyzing Unstructured Data needs more involved skills. Data Engineers and Data Scientists are the people who are generally employed to make sense of Unstructured Data.
Data Engineers are skilled enough to write jobs that can create Structured Data from Unstructured Data with the help of Data Scientists who use advanced machine learning techniques to extract information from Unstructured Data. Frameworks like Tensorflow, Deeplearning4j, Pytorch, Scikit Learn, etc are used.
How Semi-Structured Data Fits With Structured and Unstructured Data?
Semi-Structured Data maintains internal markings and tags that help you identify separate data elements. This enables data analysts to determine information hierarchies and grouping. Both databases and documents can be Semi-Structured.
Even though Semi-Structured data only represents a small chunk of the data pie, it is deemed valuable when used in combination with unstructured and structured data.
Emails are a good example of Semi-Structured data, but it represents a relatively large use case. Generally, Semi-Structured development focuses on simplifying data transport issues.
Examples of Semi-Structured Data
- Open Standard JSON: JSON is a Semi-Structured data interchange format. Its structure is made up of name/value pairs and an ordered value list. JSON is very good at transmitting data between servers and web applications because its structure is interchangeable among languages.
- Markup Language XML: XML is known as a Semi-Structured document language. Simply put, it is a set of document encoding rules that describe a human and machine-readable format. It is deemed valuable since its tag-driven structure is highly flexible. It can easily be adapted by coders to universalize data storage, structure, and transport on the web.
- NoSQL: Semi-Structured data forms a major chunk of various NoSQL databases. NoSQL databases are different from relational databases since they don’t separate the schema and the data. Thus, NoSQL databases are a good choice for storing information that doesn’t fit easily into the record and table format.
Tools To Use For Structured and Unstructured Data Analytics
The goal of businesses is to extract valuable insights through both unstructured and structured data sets. You can leverage a vast array of Business Intelligence tools for unstructured and structured data analytics to help you grow your data capabilities across all types of data.
Here are a few examples of Business Intelligence tools that you can use for the same:
- Oracle BI
- Microsoft Power BI
- Apache Hadoop
- Zoho Analytics
- TextMiner and SAS Viya
- Cogito Semantic Technology
Since most of the natural environment is unstructured, almost all data is unstructured when originated. It is after a preprocessing that Structured Data is created. The data revolution created by distributed processing and deep learning techniques provided us with some great tools in aiding this Unstructured Data to Structured Data conversion.
Converting Unstructured Data to Structured Data is not only about using creating clusters and applying machine learning techniques. It involves connecting various data sources and implementing jobs that execute the conversion process.
A good ETL tool can create a big impact in generating this value. If you are looking for such a tool, that can significantly reduce your developer effort, Hevo is a good alternative.
Integrating and analyzing data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo Data, a No-code Data Pipeline helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. Hevo with its strong integration with 100+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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Feel free to share your experience with Structured vs Unstructured Data with us in the comments section below!