Structured Versus Unstructured Data in Google Cloud

If you’re tasked with overseeing your company's cloud environment or digital transformation, you might see these buzzwords come up fairly often: structured versus unstructured data.

But these are not buzzwords by any means. Instead, they are some of your most valuable assets when it comes to business intelligence. Both types of data can be leveraged to gain valuable insights into your business operations, your client success, and your company’s potential path towards further growth.

This guide will give you a quick overview of the difference between structured versus unstructured data, and how you can analyze and make the most of it with the tools offered in the Google Cloud ecosystem.

What Is Structured Data?

Structured data is data that adheres to a strict schema with a predefined format, often residing in relational databases. It is characterized by its organization into rows and columns, where each row represents a record, and each column represents a specific attribute of that record. This is what makes it “structured,” and as a result, it fits neatly into tables. 

For instance, consider a database for an online retail store. Here, structured data would catalog product information in a systematic way. 

Each product would have its own record, with attributes like ProductID, Name, Price, and QuantityInStock held in separate columns. A setup like this makes for efficient querying and reporting. Say you wanted to see if an item was out of stock. A query could easily extract all records where the QuantityInStock is zero, indicating which products need to be reordered.

Another good example to give you an idea of structured data is airline reservation systems. Each booking is stored in a database with fields such as FlightNumber, Date, DepartureCity, ArrivalCity, SeatNumber, and PassengerName. The data is standardized so that it can be searched, sorted, and analyzed. You can look up data points by each field.

The key point to take away from this is that the point of structured data is organization and ease of access. It is simple to navigate, and not too much work to find what you need and perform complex analysis.

What Is Unstructured Data?

As the name suggests, unstructured data is the exact opposite of structured data. Unstructured data is any data that lacks a predefined format or schema. So, it is more complex to collect, process, and analyze using traditional database systems. Plus, it is often text-heavy, but can also contain data such as dates, numbers, and facts. 

Since this type of data is schema-independent, its structure is not defined until the data is read, typically requiring more specialized data processing techniques.

As a quick example, let’s consider text files — documents such as emails or messages, which contain free-form text and may also have elements such as attachments and metadata like sender, recipient, and timestamp. The body of an email, with its unstructured format, resists neat categorization into rows and columns.

Video files are another prime example. The information is unstructured and in a visual format, including movement, color, facial expressions, and more. Since this data is highly unstructured, it doesn't fit into a predefined data model, making it challenging to analyze without sophisticated image and video analysis tools.

Social media posts, too, are unstructured. Your average TikTok, Facebook, or X post can contain text, images, videos, and various metadata elements such as likes, shares, and comments. The data is highly varied and dynamic and reflects the free-flowing nature of human communication and social interaction on these platforms.

How to Use Structured Versus Unstructured Data

You can use structured data to keep tabs on the usual metrics and operations, like checking up on sales trends or managing inventory. Since it's the kind of data that fits well into spreadsheets and databases, it is very straightforward to work with.

Unstructured data — things like customer reviews, images, and social media posts — while less orderly, is just as valuable. It can give you insights into your customer opinions and help spot trends that might not show up in the numbers.

For the structured side of things, Google Cloud has plenty of tools as part of their Cloud platform to help you out. Cloud SQL and Cloud Spanner are there to manage relational databases for applications that need consistent and reliable transactions. Google has a few good tutorials on manipulating structured data with these tools.

BigQuery is also a good tool for querying large data sets quickly, which is super useful for getting insights from structured data without much hassle.

When it comes to unstructured data, that's where Google Cloud Storage comes into play. It's a place to store heaps of data, no matter what format it's in. Google's move to bring BigLake and object tables to BigQuery means you can now analyze unstructured data stored in Cloud Storage using SQL, which is a big deal because it makes working with unstructured data a lot more like dealing with structured data.

Google has an excellent tutorial covering unstructured data in BigQuery if you want to check out the technical side of things.

If you're looking to get more from your data, whether structured or unstructured, tools like Looker Studio and Looker can help. They let you put all the insights in one place, so you can see what's happening and make smarter calls for your business. 

For example, Looker can help visualize the correlation between structured sales data and unstructured customer feedback to identify drivers of purchase behavior. We have a detailed guide on blending data in Looker that can help you get moving in the right direction.

Finally, Gemini empowers users to more effectively manage their structured and unstructured data in Google Cloud. It can support: 

• Automatic Schema Migration: Automatically generate migration scripts to simplify schema evolution in Cloud SQL.
• Query Autosuggestions and Syntax Checking: Get intelligent autosuggestions and syntax checking to help you complete your queries faster and avoid mistakes.
• Data Validation Rules and Constraints: Easily set up validation rules, data constraints, and alerts to help you identify data issues early.
• Charts and Dashboards Generation: Automatically generate charts, graphs, and dashboards to visualize your database data instantly and spot trends and patterns.

Structured & Unstructured Data in Google Cloud 

In business intelligence, structured data is a valuable asset for precise analysis and forecasting due to its organized format. Unstructured data, while more complex, provides rich qualitative insights that structured data lacks. You will need to be familiar with both types if you want to gain a better understanding of your business operations and customer behavior.

Google has all of the tools you need to get the most out of your data. But it might seem daunting at first. As a Cloud expert and Google Premier Partner, Promevo can be your guide in this space and help you determine the best course of action.

Contact us today to learn more about leveraging the Google Cloud Platform to gain deeper insights into your data, no matter how organized or scattered it may be and what format it is in.