Data plays a vital role in business operations and comes in many forms. Everything from a well-organized Enterprise Content Management System (ECM) to a collection of documents within your organization both exemplify valuable data. However, they differ significantly in their ability to provide value to your organization.
What is Structured Data?
- Quantitative
- Easy to search
- Predefined, standardized format
- Stored in data warehouses
- Ready for analysis
- Only select data types (generally text and numbers)
- Typically XML or CSV
The difference between the ECM and the documents in the example above is that the data in the ECM is consistent in its format, clearly defined in its properties, and easy to process with basic tools — it’s structured. The data in the ECM is collected with a specific purpose and is structured in a consistent way with set characteristics that make it easily filtered, searched, or categorized.
What is Unstructured Data?
- Qualitative
- Requires complex processes to search
- Undefined, native format
- Stored in data lakes
- Needs processing to analyze
- Variety of types, shapes, and sizes conglomerated together
- Might be DOC, JPG, PNG, MPG, MP3, WAV, and others
The documents, on the other hand, could include anything from paper, emails, outdated media, or text in any syntax with any sort of information the user chose to type — it’s unstructured. This makes the data in the documents more difficult to organize, store, and interpret without specialized machine learning algorithms or some sort of processing by an expert in data science.
Structured vs Unstructured Data Examples
| Structured Data Examples |
Unstructured Data Examples |
- Databases
- ZIP codes
- Phone numbers
- Dates and times
- Barcodes and SKU numbers
- Point of sale data
- Spreadsheets
- Credit card numbers
- Email addresses
- Inventory control
- Student fee payment databases
- Airline reservations and passenger data
- Medical information and patient data
|
- Text documents
- Emails
- Social media posts
- Web pages
- Text messages
- Conversations on chat apps
- Digital photos
- Surveillance recordings
- Audio and video files
- Podcasts
- Business applications
- Satellite imagery
- Weather data
- Geospatial data
- Scientific data
- Traffic, weather, or other sensor data
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The vast majority of data that is accessible today is unstructured; it simply exists. Texts, emails, websites, photos, videos, and other unstructured data examples are all over the internet and business computing systems. Any effort to draw insights from this data would require a way to filter and identify patterns with no consistent traits, formats, or patterns.
A much smaller portion of the world’s data is intentionally structured and defined as it is collected, for deliberate use and analysis with a predefined purpose. Any sort of numbers, codes, spreadsheet entries, or field-based personal information are examples of structured data.
Pros and Cons of Structured and Unstructured Data
Both types of data can be useful to a business, although each has benefits and drawbacks. Structured data has been in use for longer and has more obvious use cases — after all, it was created specifically to fulfill them. However, technology that has emerged in the world’s digital transformation may offer even more valuable opportunities for businesses if they’re willing to invest in the resources to unlock the much broader wellspring of unstructured “Big Data.”
| |
Structured Data |
Unstructured Data |
| Pros |
- Easy to use and interpret; no expertise needed
- Immediately usable
- Data is consistent and of higher quality
- Convenient and organized for storage
- Can be fed to machine learning models without trimming
- More tools are able to measure and analyze it
- Structured data is easily searchable due to its organized format
|
- Plentiful sources of unstructured data are out available (most data)
- Easily scalable; stored in data lakes with minimal need for database management
- Pay-as-you-use pricing of data lakes can reduce costs and resource consumption
- Rapid accumulation of data
- All data can remain in its native format
- More use cases, since data is not defined until it is needed
|
| Cons |
- Schema dependent, which limits storage options.
- Copes poorly with changing business scenarios; changes in data requirements require updates to all structured data
- Limited usage; predefined structure for an intended purpose
- Often manually entered into the database (unless you have a service provider who handles this for you)
|
- Data science expertise is needed to prepare/analyze
- Must be manipulated with specialized tools
- Can take more time to interpret
- Stored on less authentic/encrypted shared servers; more prone to ransomware and cyber attacks
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Use Cases for Unstructured vs Structured Data
Structured Data:
- Operational Efficiency and Process Optimization: Structured data from operational systems, such as production data, supply chain data, or employee performance metrics, can be leveraged to optimize processes, identify bottlenecks, and improve overall operational efficiency. By analyzing structured data, businesses can streamline workflows, reduce costs, and enhance productivity.
- Business Intelligence and Reporting: Structured data forms the foundation for business intelligence and reporting systems. By aggregating and analyzing structured data, businesses can generate meaningful reports, visualizations, and dashboards that provide insights into performance, trends, and opportunities.
- Compliance and Regulatory Reporting: Structured data plays a crucial role in compliance and regulatory reporting requirements. By organizing and analyzing structured data, businesses can ensure accurate and timely reporting, adhere to regulatory guidelines, and mitigate compliance risks.
Unstructured Data:
- Customer Data Mining: While traditional CRM data can be useful, broader insights on customer behaviors and sentiments can be mined from reviews, social media posts, and other unstructured data formats. Processing this data can produce valuable takeaways for your business.
- Smarter Chatbots: Text analysis and machine learning algorithms can empower intelligent chatbots to more effectively route questions from customers to the ideal resources or answers.
- Predictive Data Analytics: Major shifts in the market are sometimes visible from larger trends that are not explicitly defined in more structured data. With the right expertise, predictive analytics can make use of this data to help you plan ahead and alert your business before it’s too late to shift course.
An experienced partner makes it easier to achieve intelligent data management, regardless of whether you’re working with structured data, unstructured data, or both. Document conversion services can transform unstructured data into structured data, converting it into a consistent and more usable format.
Scan-Optics is an expert partner with a comprehensive suite of services and technologies for scanning, digitizing, validating, converting, organizing, and processing your data in a secure and accurate fashion, even when using disparate systems. The Scan-Optics team is ready to optimize your existing files and incoming data and help to streamline your processes and resources for a more efficient and productive business.
Unleash the power of your data and talk to an expert today.