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This is just some announcement which people will want to pay attention to! Learn More

Data structuring: a key first step

Now that most organizations have been relying heavily on computer-based storage for several decades, and on cloud-based storage for at least several years, huge backlogs of data are common. It’s also common for this data to be unstructured, which means that it is in a raw, disorganized state: PDFs, text files, images, videos, and audio files are stored in such a way that their contents cannot be very easily accessed, analyzed, or leveraged for the good of the business. Backlogs of unstructured data within businesses are growing by 55-65% every single year—it’s not an issue that will go away any time soon!

So why not just leave enterprise data in a big digital pile? Because data structuring is the key first step towards the implementation of AI solutions that save businesses increasing amounts of time and money. 

One common example of how data structuring can be beneficial is in the improvement of retail catalogs and retail user experience. Retailers now receive most of their crucial data, such as product attributes and purchase orders, in formats ranging from faxed sheets of paper to PDFs to digital images. The business may be managing their inventory adequately with this unstructured data, but if it were digitized, categorized, and tagged for the business’ individual needs, the data could be used to produce more thorough and up-to-date product catalogs, to establish better online search capabilities for customers and employees, to achieve more accurate inventory management, and to move towards more accurate and therefore quicker order fulfillment. 

Even better, once an enterprise structures their data, it is ready for the application of more advanced AI solutions. In addition to the aforementioned improvements to business processes, structured data can be used in a broad range of machine learning applications that cut costs, improve efficiency and accuracy, and reduce reliance on human labor. 

By structuring their data, businesses can make their previously inert data backlogs work for them. It’s the first step towards making AI work for the business, too. 

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