As a business professional, you're no stranger to the overwhelming amount of unstructured data that floods your inbox, fills your documents and populates your databases on a daily basis. DOMO’s 11th edition of Data Never Sleeps found that the average person produces 102MB of data every minute, with people sending 241 million emails and LinkedIn users submitting 6,060 resumes. So, what happens with this data and how is it utilised? Is it even utilised at all? While we may recognise that there is plenty of value in unstructured data, using it effectively can be challenging due to its disorganised, improper format.
In this article, we dive into the differences between structured vs unstructured data, provide examples of both and discuss how easy it is to convert unstructured data into structured data, so you can create actionable insights using Affinda’s artificial intelligence (AI) technology, without having to convert this data manually, a time-consuming process.
What is unstructured data?
You encounter unstructured data every day - it's in the emails you read, the reports you write, the social media posts you publish and the images you process. In simple terms, unstructured data refers to any data or information that isn’t organised in a predefined schema or structured way, making it more complex to analyse using traditional methods. Unlike structured data, which fits neatly into rows and columns in databases (think an Excel spreadsheet or a Customer Relationship Management (CRM) database), unstructured data is more freeform. Unstructured data comes from various sources, such as customer service emails, product reviews, social media interactions and audio or video recordings.
These diverse data sources can provide valuable insights across different industries. For example, in marketing, unstructured data can offer deep understanding of customer behavior and habits. In the legal sector, it can serve as a crucial record of communications for compliance purposes and in the finance industry, it can help with audits, financial statements and more. However, the raw form of unstructured data presents a challenge. It cannot be directly utilised without proper processing. To extract meaningful information from unstructured data, specialised tools and technologies are necessary. These technologies transform the unorganised data into an easily accessible data structure you can use, allowing businesses to leverage the wealth of information hidden within it.
Unstructured data examples
While the list of unstructured data examples is extensive, here are several common examples you might run into in a professional space:
- Social media posts from any platform like Twitter, Facebook, and Instagram
- Any type of email from any email client, as emails contain free-form text, attachments, and metadata such as timestamps and sender information
- All digital photos and images
- Video content, including recorded meetings, training sessions, demo videos, webinars and more
- PDFs and Word documents such as business reports, contracts and more
- All audio files including voice recordings, customer service calls and podcasts
- All content on websites and website pages including text, images and multimedia
Are images unstructured data?
Yes, images are considered unstructured data. Images are a great example of how unstructured data does not follow a specific scheme or structure because they consist of raw pixel data and metadata that cannot be easily categorised or analysed using traditional data processing methods.
Is email unstructured data?
Yes, emails are considered unstructured data. Emails typically contain free-form text and can include embedded elements such as attachments, hyperlinks, and metadata (like timestamps, sender and recipient information). This makes an email a great example of unstructured data as analysing the contents of emails requires more sophisticated tools.
What is structured data?
Structured data, as defined by Amazon Web Services (AWS), is a type of data structure that has a standardised format for efficient access by software and humans alike. It is organised in a highly defined manner, typically in rows and columns, making it easy to store, search and analyse. Structured data allows for seamless integration into relational databases and spreadsheets where each data point is identifiable and accessible through predefined categories.
Structured data examples
In the debate of structured vs. unstructured data, structured data stands out due to its well-defined schema and ease of organisation. Structured data is highly organised and easily searchable using basic algorithms, making it important to various business processes like accounting, financial reporting, marketing and business development. Here are some common examples of structured data that you may already be working with on a daily basis.
Spreadsheets
Spreadsheets are one of the most popular forms of structured data used by professionals around the world. Spreadsheets like Microsoft Excel and Google Sheets organise data in rows and columns. Each cell can hold specific data such as text, numbers, dates and more, making it easy to perform calculations, sort data and generate reports.
Relational databases
A relational database is a type of database that organises data into rows and columns, which is a great example of structured data. Common SQL database examples include MySQL, PostgreSQL and Microsoft SQL Server.
Customer Relationship Management (CRM) systems
A CRM system is a tool that businesses use to manage their interactions with customers. It uses structured data to organise customer information into a searchable database. Popular CRMs include Salesforce and HubSpot and are commonly used by millions of businesses around the world. CRMs store information such as customer details, names, purchase orders, interaction history and more.
Inventory management systems
These help businesses from varying industries, including retail, manufacturing, healthcare, logistics and automotive, keep track of inventory levels, orders, sales, deliveries and more. Inventory management systems maintain structured data about product quantities, locations, supplier information and more, making it easy to manage stock levels, predict inventory need, and streamline supply chain operations.
What is structured data used for?
Structured data plays a crucial role in various industries, use cases, business operations and analytical processes. Its organised format makes it ideal for a wide range of applications that require precise, easily accessible information. Here are some key areas where structured data is commonly used in business.
Customer information
Businesses use structured data to keep track of customer details, generally through a Customer Relationship Management (CRM) system. This includes details such as name, address, phone number and purchase history. By utilising this information, you can send out personalised marketing efforts, create targeted sales strategies, improve customer service and ultimately increase customer satisfaction.
Financial records
Every company has a financial team, whether it’s Accounts Payable/Accounts Receivable, Payroll, or even just Finance. Businesses use structured data for financial tracking and reporting to record income, expenses and transactions. With the right tools, this data can then generate insightful reports and dashboards to better understand business performance.
Business data
Structured data is necessary when used in data analytics. Data scientists and engineers, as well as marketing professionals can leverage business data to help uncover patterns, correlations and insights to drive strategic business decisions.
Machine learning
In the realm of AI, businesses use structured data to train and use machine learning models (MLMs). This allows businesses to use advanced technology to better understand their operations and make data-driven decisions to improve their processes.
How to convert unstructured data to structured data
If you’re looking for a way to convert your unstructured data into a format that you can use, Affinda can help. We offer a suite of powerful products designed specifically for document analysis and data extraction, enabling you to extract data from any unstructured data type into structured, meaningful data that you can use for whatever you need. Here’s how:
Step 1: Upload your document/s
Start by registering for a free trial account. Once registered, you can easily upload your unstructured documents to our platform. Whether you’re dealing with resumes, invoices, purchase orders or any of the 100+ different document types we support, Affinda has you covered. We support various file types and can process documents in multiple languages.
Step 2: Affinda’s technology will automatically extract the data
Affinda uses advanced Optical Character Recognition (OCR) and Artificial Intelligence (AI) technologies to extract text and data from the uploaded documents. This includes parsing detailed information from complex documents, ensuring high accuracy and efficiency.
Step 3: Your data will then be structured and classified
The extracted data is then organised into a structured format. For example, resume data is parsed into fields like name, contact information, skills and experience, while invoice data is categorised into fields such as invoice number, date, amount and vendor details, just to name a few. This process is completed automatically through Affinda’s AI technology, with the ability to parse thousands of documents in a matter of seconds.
Step 4: Integrate with existing software and automate your business processes
You can integrate the structured data into your existing business workflows and systems using Affinda’s powerful API. This allows for the seamless automation of tasks such as candidate screening in the recruitment industry, financial data aggregation in the banking and finance industry and much more. Tools like Pipedream can be used to connect Affinda’s capabilities with hundreds of other applications, further streamlining your processes.
Take advantage of your business’ unstructured data
Are you ready to drive innovation and efficiency in your daily operations and turn your unstructured data into a usable data structure? Then it’s time to start using AI-powered tools like Affinda. Easily convert your unstructured data into structured formats so you can gain deeper insights into your customers and streamline business processes. Get in touch with our team today to find out how we can help you convert your unstructured data into a usable data structure.
