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Structured vs. Unstructured Data: What’s the Difference?

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Businesses run on data. Data is so integrated into our daily lives that, most of the time, we don’t even realize just how often we use it to perform basic tasks and make decisions. Some of the ways we use data on a regular basis include:

  • Interacting with a spreadsheet
  • Reading a profit and loss statement
  • Checking how many views your website got yesterday 
  • Evaluating dozens of resumes during the hiring process
  • Sorting and labeling products in categories
  • Analyzing responses to an email offer

All of these activities require data, but the ways the data is structured (or not)—and the ways it can therefore be manipulated—can be quite different. Let’s take a look at structured versus unstructured data and how they are different. 

Structured vs. Unstructured Data

Structured data is fairly easy to recognize, and it’s typically the first thing that comes to mind when people think of data. It is organized, perhaps in a spreadsheet or relational database, and formatted in a way that’s easy to understand, often in columns and rows. Structured data is also typically quantitative data, such as names, numbers, addresses, product numbers, user IDs, and so on. You might have used structured data in an Excel spreadsheet, a CRM system, or inventory management software.

Unstructured data, as the name suggests, doesn’t have a predefined structure and can be much more difficult to organize and analyze.

The information is typically qualitative in nature and not as easy to categorize. Some examples of unstructured data include images, videos, audio, social media activity, X-rays, and so on. Although humans can often intuitively review and understand unstructured data—you can look at an X-ray to detect pneumonia, for example—teaching machines how to do this is not always so simple. However, developments in deep learning and artificial intelligence make it possible for computers to handle large quantities of unstructured data, providing many benefits to the people and organizations that tap into it.

How Skyl.ai Handles Unstructured Data

Can you perform all of these example tasks manually? Of course, but it can be time-consuming and prone to human error, which is why we use software for so many of these activities. People have been able to use computers to manipulate and analyze structured data for a long time, but automating processes around unstructured data is relatively new. You might know how to turn a spreadsheet into a visual chart or graph with a few clicks, but you probably don’t have a tool on hand for rapidly sorting images into categories or looking at thousands of X-rays quickly.

Machine learning can be used for these unstructured data applications and many more. Skyl.ai is a unified platform that allows you to build a machine learning workflow, gather and label data, train the model, analyze results in real time, and deploy the model. The most difficult data to handle is unstructured. This is Skyl.ai’s area of expertise. Skyl.ai gives structure to unstructured data, empowering organizations to reach their goals in improving business outcomes with benefits such as increased revenue, optimized cost, efficiency, and customer satisfaction.

Unique Ways Skyl.ai Leverages Unstructured Data in Various Industries

Perhaps one of the best ways to understand the many benefits of maximizing your data through artificial intelligence is to examine a few real-world examples. Take a look at some of the ways Skyl.ai has helped businesses in various industries achieve their goals. 

Improving Quality in the Automotive Industry

Computer vision is being used by automotive manufacturers at various stages in the production process to inspect parts and processes. Manual inspection is a cumbersome and slow process, but an essential one because even the smallest defects can have an impact on the assembly process. By using multiple scanners and cameras in combination with computer vision, these tasks can be performed more efficiently and more accurately. By detecting defects earlier in the manufacturing process, automotive manufacturers save money on waste, unnecessary repairs, and costly rework. The end result is better quality through automated inspection processes. 

The automotive repair industry also takes advantage of natural language processing (NLP) technology to search the lengthy product manuals created by original equipment manufacturers (OEMs). With NLP, the manuals can be automatically digitized, tagged, categorized, and stored in a searchable database. Rather than sifting through thousands of pages, repair technicians can perform digital searches to operate more efficiently. 

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

Saving Man-Hours and Costs in the Pharmaceutical Industry

The pharmaceutical industry also uses NLP to organize unstructured data and enhance the quality of research. Rather than spending thousands of hours reading clinical reports and lab notes and searching scientific literature for relevant information, researchers can quickly find the data that is most relevant to their work. NLP applications can be trained to understand the scientific terms and jargon specific to the industry so they can search for particular topics, words, and phrases. In addition to saving hours of manual searching, this helps eliminate the costs of unnecessary experiments, accelerates trials, and brings solutions to market more quickly. 

Computer vision is also used in the pharmaceutical industry for quality control, both during manufacturing and after packaging and labeling. This helps manufacturers adhere to the strict guidelines for labeling and reduces the risk of human error during the inspection process. Once the standards have been defined, computer vision can scan hours of video footage to identify packaging that doesn’t meet the specifications, including inaccurate barcodes. The same technology can be used during production to identify pharmaceuticals that don’t meet the requirements, catching errors early and ultimately saving money by reducing production of drugs that don’t meet quality standards. 

Assessing Properties in the Real Estate Industry

AI is being readily adopted in the real estate industry for a number of applications. For real estate platforms, Computer Vision can be used to analyze large groups of listings to identify and tag details such as materials, architectural style, types of rooms, and more. This saves listers time and helps ensure that site users get a better experience. The information generated by computer vision can also be cross-referenced with other data, such as location and comparable properties, to help buyers more quickly and accurately assess the value of the listed property.   

Another way listing sites are using AI to improve the user experience is through image curation. Removing poor-quality images makes the searching process more enjoyable for buyers and improves the quality of each listing on the site. AI can also detect and remove fraudulent listings by looking for specific features of photos, identifying duplicates, and using other techniques. 

Reducing Accidents in the Manufacturing Industry

Human safety inspectors can only be in one place at one time, making it virtually impossible to enforce compliance with safety standards in every aspect of manufacturing. However, with artificial intelligence, machines can be taught to automatically identify activities that impact quality or safety and notify the appropriate people at the time of the incident. Examples include improper use of personal protective equipment, standard operating procedure steps being skipped, and unauthorized personnel entering prohibited areas. 

With video cameras and AI, every area in a facility can be monitored all the time, reducing the burden on safety personnel and allowing them to focus their attention on incidents as they arise. This type of monitoring also helps improve compliance because employees know their actions are being recorded. Fewer accidents means better protection of workers, reduced liability, and better efficiency. 

Predicting Trends in the Retail Sector

The retail industry is always striving to stay on top of trends, and AI can be used to identify demand for certain products by analyzing what consumers search and discuss on social media. Identifying trends early allows retailers to stock up on the products that will be most popular and stay ahead of the demand curve. AI can also be used in retail stores to help maintain inventory levels, keep shelves stocked, identify shrinkage, and flag expired products for removal.  

Leverage AI in Your Organization

Understanding the difference between structured and unstructured data is important because it helps you know the limitations of your current systems and prompts exploration of new solutions. Although it’s certainly possible to manually sift through spreadsheets or text documents with thousands of customer reviews to try to glean insights, AI can do it faster and more accurately.

You have a world of data at your fingertips, but if you’re not using it to improve your business, you’re not getting the most value from this priceless resource. Tapping into machine learning can help you take your business to the next level. To learn more about how to incorporate AI into your organization, check out our machine learning checklist.

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