Structured vs. Unstructured Data: What You Need To Know
They say data is king. That’s true only if you’re able to accurately use it. Companies are amassing vast amounts of information from their customers, employees and other stakeholders with the goal of using the data collected to understand consumer behavior, market trends, develop products and drive business decisions. The information collected falls into one of two categories: structured or unstructured data.
Let’s take a closer look to help you get the most out of your data.
What is Structured Data?
Structured data is data that has a standardized format, complies with a data model, is highly organized and is easily accessed by humans. It is perfect for seeing patterns, finding exactly what you’re looking for, and performing deep analysis. Structured data is typically quantitative in nature and stored in databases or data warehouses. Examples include names, dates, and addresses stored in a database that can be queried.
Let’s assume you export a spreadsheet containing customer information from a database. You save it as Customer Information October 2022.xls. Each row and column contains details like first and last name, address, telephone number, email address, etc. By naming the file, including details about its content, and organizing the information in structured rows and columns, you are creating structured data.
Attributes of Structured Data
Regardless of how it’s stored, good structured data should have the following characteristics:
- A clearly defined structure that conforms to a data model
- Is well organized, so the definition, format and meaning of the data is easily understood.
- Data that lives in fixed fields (usually in rows and columns) within a file or record
- Entities in the same group have the same attributes
- Similar entities are clustered together to create classes
- Is easily queried and accessed by humans and other programs
- Data elements can be addressed enabling efficient processing and analysis
Sources of Structured Data
Structured data can be computer or human-generated from a variety of sources, including
- Databases, such as SQL, MySQL, SQLite, OLPT, etc
- Sensors, such as GPS and RFID tags
- Web server and network logs
- Medical and smart devices
- Online forms and surveys
- Clickstream data — information collected about a user while they browse through a website.
What is Unstructured Data?
Where structured data is quantitative, unstructured data is qualitative in nature and does not have a readily identifiable structure. As a result, it cannot be processed through a conventional, pre-defined data model. Unstructured data is difficult to search, comprehend and derive value. Examples of unstructured data include media files, documents such as emails and presentations, social media, survey responses, etc.
Imagine you have a folder full of thousands of PDF documents with names like asdfkljhq234.pdf. You need to sort the files by what type of document they are, what they are about, and who the big players are in each. Doing this would require you to open each document and perform your own analysis, which would be painstaking and slow.
Now, imagine that same problem but using AI tools to quickly extract and organize the data. For instance, you can use Kensho Extract, an advanced machine learning software that intelligently analyzes the layout of documents, to extract the tables and text in the PDFs. To understand the big themes and document types, you could use Kensho Classify. If you want to see who the key players are, you would run Kensho NERD, an AI-powered solution that identifies entities in text. Now you can easily and quickly sort your documents and begin to understand them at a high level, all without needing to manually read a single one.
Attributes of Unstructured Data
Unstructured data has the following attributes:
- It has no easily identifiable structure and does not conform to a data model
- Data is stored in ad-hoc ways, such as in data lakes, S3 buckets, folders, etc, making it hard to search and understand.
- It does not follow any standardized rules around naming and metadata
- Files tend to be larger, which can be challenging to store
- Requires specialized computer programs to mine data
Sources of Unstructured Data
Similar to structured data, unstructured data can also be machine or human-generated from sources like
- Rich media such as images and videos, geo-spatial data, etc.
- Web pages
- Social media
- Internet of Things, sensor data, ticker data.
- Document collections such as emails, invoices, productivity applications, etc.
Why Should Companies Care About Unstructured Data?
Companies spend a lot of money and resources generating data. Yet, recent reports indicate that up to 90% of the world’s data is unstructured. That is a staggering amount of data potentially left untapped and unused. Solutions in the Kensho AI Toolkit are well suited for handling messy, unstructured data by helping to add structure and enabling valuable data extraction.
For instance, Kensho Scribe is an AI-powered, speech-to-text solution that transcribes audio and visual files. Kensho Extract, an advanced machine learning software, intelligently analyzes the layout of documents, to extract unstructured data and convert it to machine-readable paragraphs for further use and analysis. Adding natural language processing tools such as Kensho NERD help you enrich your data. NERD easily identifies key elements and links them to a connected data source, enabling data enrichment and extraction of valuable information.
While unstructured data may be messy, it can be a treasure trove of insight useful for business intelligence and analytics. Some implications for business use cases include data mining to identify patterns in consumer behavior and product sentiment as well as predictive analysis to understand and forecast market trends.
Ready to maximize the value in your data? Talk to our AI team today to learn more about Kensho NERD or sign up for a free trial today!
