In today’s digital world, businesses have access to more data than ever before. The approach to utilizing these data sets effectively can play a crucial role in driving business success. Therefore, businesses must enhance data collection and analysis to ensure they make the most of the data available to them. But before you jump onto the data management bandwagon, it’s crucial to understand different data structures and their role in unlocking true business value. Once you gain a basic understanding of structured vs unstructured data, you will be better positioned to enhance data decisioning.
From a top-level view, structured vs unstructured data might seem less complicated, but the actual challenge lies in analyzing these data sets and structuring them to drive maximum value. Request a Free proposal to learn how Quantzig can help you improve decision-making by unlocking your business data’s true potential.
What is Unstructured Data?
Unstructured data is the raw information that is readily available to businesses from various sources. These data sets do not follow a standard data model or a pre-defined approach to organizing and managing data. Though unstructured data is generally text-heavy, it can also be composed of dates, numbers, facts, and BLOBs.
Other examples of unstructured data include-
- Audio files, weather data
- Invoices, records, emails, productivity applications
- Sensor data, satellite imagery, surveillance imagery
If you’re looking for more information on structured vs unstructured data, request more info now.
What is Structured Data?
In today’s digital world, unstructured data is abundantly available. Unlike unstructured datasets, structure data sets are more organized and easy to interpret. However, unstructured data sets can be structured by organizing data in a row and column format where the relationship between different rows and columns remains constant throughout. Being highly organized, structured data sets are easier to process, access, and work with and are compatible with most BI tools, allowing users to optimize resources from a technical perspective.
Other examples of structured data include-
- Names, dates, addresses
- Account numbers, credit or debit card numbers
- Stock information
Read on to learn more about structured vs unstructured data and discover why the difference between structured and unstructured data matters from a business perspective.
Structured vs. Unstructured Data: Points to Remember
- More than 80% of data that is readily available to businesses is unstructured data
- Unstructured data sets can be difficult to process
- Structured data sets are easier to process and require less storage space
While analyzing the differences between structured vs unstructured data, data management might seem easy, but in reality, handling a mixture of these data sets is challenging. Especially if you’re looking to decode and interpret insights from the huge volumes of semi-structured data sets available to you.
Want to learn more about the difference between structured and unstructured data? Speak to an analytics expert right away!
Quantzig has developed advanced data management solutions to address this issue by leveraging its data analytics and BI capabilities. Quantzig’s advanced analytics solutions leverage Naural Language Processing, Machine Learning (ML), and other innovative technologies to help key decision-makers make data-driven decisions. “From a business perspective, both data types hold great value, and advanced BI tools that are available today can accelerate data aggregation, querying, and analysis to create business value from them,” said an analytics expert a Quantzig.