Data is essential for any use case in information technology. Almost every possible service on the internet uses data to achieve an objective and\or collects data. There are dedicated companies with expertise in collecting, processing and performing data analytics.
Data fuels social media such as Facebook, Twitter and Instagram which use it for targeted advertisements, content recommendation systems on OTT (over-the-top) streaming platforms such as Netflix also use users’ data to understand preferences and disinclinations.
Information Technology (IT) industries have experienced exponential growth in the volume of incoming data. This can be attributed to high-speed internet network access including Wifi, 4G, 5G and a simultaneous increase in the number of users. The worldwide digital population as of is forecasted to climb to 5.63 billion in 2025.
Moreover, the industry has also witnessed a boom in IoT (Internet of Things) devices which are devices other than computers and mobile phones connected to the internet such as security cameras, systems, household items such as temperature regulators, refrigerators and automated machines in factories, self-driving cars e.t.c. These devices are constantly collecting and sending data to the servers for further data management.
Difference between Structured vs Unstructured Data
A gigantic amount of data can exist in either structured or unstructured form. Let’s take a look into how both types of data differ from each other and their respective benefits.
What is Structured Data?
Data in structured form is usually stored in the form of text, is well organized in nature with data sequentially arranged in the form of rows and columns. This type of data is well suited for data analysis and data mining tools and compatible with RDBMS (Relational Database Management System).
Advantages of Structured Data
It is convenient to work with structured data since it can be stored in the form of traditional RDBMS which consists of tables. It can have multiple tables with well-defined and unchangeable relationships based on primary and foreign keys.
Structured data is stored in a way which makes it easier to perform CRUD (Create Read Update and Delete) since the data conforms to a conventional and rigid relational schema-based structure.
Disadvantages of Structured Data:
While the data is finely structured and has undergone fine-grained data processing, structured data has limited utility because of this very reason as it crops valuable information.
How is structured data used?
Following are some examples of how structured data can be used:
- Software Engineering: Structured data in an RDMS is used for many web applications and software as a data storage backend, a schema is designed before the application is even built. Afterwards, any data necessary for the application to function is stored and operated in the relational database.
- Spreadsheets: Spreadsheets are also a form of structured data which is used for keeping track and performing operations on data.
What is Unstructured Data?
This type of data is not usually restricted to text, it can belong to, but is not restricted to any of the following data sources:
- Word documents such as docx files from Google docs or Microsoft word, or any other text, pdf file.
- HTML Web pages
- Business documents
- Digital Spreadsheets
- Digital Images
- Social Media Posts chats and messages (unless they are encrypted)
- Radio data from satellites, sensors e.t.c
This type of data is unfinished and unorganized in nature, for this reason, it is not easily compatible with RDBMS or other data mining tools.
Unstructured data can be stored in the form of NoSQL databases which, as the name suggests do not rely on traditional SQL query-based relational databases, rather the information is stored in the form of JSON file consisting of key/value pairs, some notable NoSQL databases include MongoDB, AmazonDB and Google Firebase.
There are two approaches to analyzing this form of data - to either transform it into structured data or to use exploratory data analysis and other techniques to extract information while the data remains in an unstructured form.
Transforming to a structured dataset results in convenience but causes loss of potentially valuable information as discussed before. In contrast, tools and techniques can be used which extract information regardless of the underlying structure of the data.
Advantages of Unstructured Data:
It has been found that 80% of “business-relevant” information is generated and initially collected in unstructured form, hence it is available in a significant volume. Such a significant volume of data warrants exploratory analysis to extract actionable business intelligence.
This type of data can be used to improve customer service by helping understand customer needs, shortcomings and any communication gaps between customers and a service. Knowing more about what customers have to say or act in similar situations gives room to business innovations. Gaps filled with the help of a more comprehensive unstructured data help not only overcome shortcomings but also introduce new features/services which could appeal to a targeted customer base.
Disadvantages of Unstructured Data:
Transforming and processing unstructured data brings a different set of costs and challenges in terms of data analytics and data security. It is also difficult to perform essential operations such as CRUD (Create Read Update and Delete) on this type of data due to a lack of schema and structure.
How is unstructured data used?
Following are some examples of how unstructured data is used:
- Self-driving cars: Datasets consisting of labelled images and videos are used to train predictive Artificial Intelligence models which are able to learn patterns and images using the labelled dataset before they eventually make decisions on the road.
- Sentiment analysis: It is defined as the “contextual mining of text which identifies and extracts subjective information in the source material and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations” (source).
For instance, a sentiment analysis could be performed on Apple’s customer base by extracting all tweets which have a hashtag (e.g. #Apple, #apple) or tagged the official account (@Apple). A classifier model can then be trained to predict the sentiment of any new tweet in the future.
- Recommendation systems: Unstructured data in the form of data points can be used to track the user’s behaviour and suggest content on OTT services or advertisements on social media based on their previous preferences and spending history. For instance, the “you might like” section on Netflix or the advertisements appearing on your Facebook feed is done with the help of recommendation systems trained on unstructured data.
Data is essential as it provides valuable business insights. however, there are different kinds of data generated by a range of data sources-structured and unstructured data. Structured data can be stored in an RDBMS while unstructured data follows no particular conventions.
Performing operations and analysis on structured data is much more convenient however since most of the data is generated in unstructured form, utilizing it provides a more comprehensive story.
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