There are massive amounts of data that can be analysed to uncover helpful insights across all businesses and fields. Understanding the different types of data your organisation is storing is essential to developing an effective data management strategy.
Many may not be aware of the difference between these data types and why they require different approaches for data governance. In this post, we’re diving into a comparison of unstructured data versus structured data.
Some data is structured, fitting neatly into spreadsheets and databases. Most data however, is unstructured, often in the form of text, chats, audio or images.
According to IDC, 80% of data will be unstructured in the next five years. Managing this mix and generating insight will be a crucial operating discipline.
Structured Data vs Unstructured Data
Both types of data are collected, processed, and analysed in different ways, yet with the same goal of extracting information to make data-driven decisions. Let’s take a closer look at the differences.
Can be displayed in rows, columns and relational databases
Numbers, dates and strings
Estimated 20% of enterprise data (Gartner)
Requires less storage
Easier to manage and protect with legacy solutions
Cannot be displayed in rows, columns or relational databases
Images, audio, video, word processing files, emails, spreadsheets
Estimated 80% of enterprise data (Gartner)
Requires more storage
More difficult to manage and protect with legacy solutions
What is Structured Data?
Structured data is stored in rows and columns of databases, spreadsheets, CRMs and ERP systems, and other systems of record. Structured data is quantitative, highly organised, and easy to analyse using data analytics software. It has been organised into a formatted repository that is typically a database; stored in a tabular structure with rows and columns. They have relational keys and can easily be mapped into pre-designed fields. Applications such as Microsoft Excel are used to store and organise structured data and can easily be connected to other analytical tools for further analysis.
Structured data is great for basic organisational and quantitative calculations, but must fit into rigid, pre-set parameters. The data points are easily searchable within their set structure and can be cross-referenced with other databases. You could search by customer address to discover which products are most popular in a certain location or find out which products are ordered multiple times by multiple customers.
Unfortunately, structured data isn’t great for uncovering the “How” and “Why”.
What is Unstructured Data?
Unstructured data is information that has no set organisation and doesn’t fit into a defined framework. It has an “open format”, like audio, video, images, and all manner of text like reports, emails, and social media posts.
Finding insights within unstructured data isn’t easy, but when properly analysed, this data can be extremely valuable to extract qualitative results, like customer opinions, or organise business data like customer service tickets, into individual categories to be routed to the proper employee.
Advanced data analysis software use machine learning algorithms to “read” unstructured text, then categorise and analyse it as a human would, but in a fraction of the time and with very high accuracy.
Let’s look at customer feedback as an example. It can come from anywhere: emails, phone calls, surveys and social media posts to name a few. Text analysis programs can perform a variety of automatic tasks to sort this data such as, keyword extraction (finding relevant words and expressions), sentiment analysis (classifying positive, negative and neutral emotions) and more.
Unstructured data can present several problems when attempting to analyse it because the formats and locations can vary widely. However, with the help of text analysis software, unstructured data can be automatically formatted and properly analysed with machine learning.
Whether structured or unstructured, data should be at the heart of every business decision. Structured data provides a view into individual customer habits or quantitative trends, but when you learn to properly organise and analyse unstructured data, the insights increase exponentially. You’ll see how qualitative data results can provide much more useful information.
Go beyond mere numbers and statistics to actual keywords, accurate classifications, and full-blown opinions. Monitor your brand regularly and in real-time to learn what’s working and what’s not – and quickly rectify.