Why Data Structuring Matters

Katharine Stevenson
4 min readOct 23, 2020


It’s the first step towards robust AI solutions

Digital transformations begin with structured data. Yet most businesses are still dealing with heavily siloed, unstructured data that holds them back from implementing technologies that could help them gain efficiencies. Even worse, the backlogs of unstructured data within businesses are growing by 55–65% every single year. More data is piled on during every second of an enterprise’s continued existence, and even more is added when the business is in a period of growth: emails, PDFs, images, video files, and audio content proliferate.

Photo by Alain Pham on Unsplash

This data plays a major role in digital transformation. As long as it remains unstructured, it is difficult to apply business solutions that can decrease costs, increase efficiencies, and help enterprises make progress on the application of digital tools. As data becomes more structured, performance is enhanced and the transformation evolution can speed up significantly. For example, a major retailer may want to improve the quality of their online searches, or an apparel retailer may want to more accurately forecast the demand for dresses of a certain size, color, pattern, and length. But until their data is structured, this task is not possible: automation tools can’t work accurately with data that hasn’t been annotated, tagged, and structured.

Regardless of the industry in question, the process of automating data structuring is generally the same. It starts with Defining the taxonomy and definitions of the tags that need to be extracted, and ends with Deployment of customized APIs that can be accessed to process raw unstructured data into customized structured datasets. In between, there must be a process for Developing customized AI models, and Tuning of the process and tools used to ensure accuracy. Once data structuring is begun, the business can begin the process of digital transformation in earnest.

What are some of the ways data structuring helps businesses?

From useless images to critical data

Many businesses receive crucial data, such as product attributes or purchase orders, in formats that do not lend themselves to easy structuring. These can be faxed sheets, PDFs in various degrees of readability, and even multi-product images that are difficult to parse. Once these items are turned into structured data that can be interpreted not just by humans but also by computers and algorithms, it becomes usable across the business in countless ways: more thorough product catalogs, better search capabilities for customers and employees, more accurate inventory management, and more accurate and therefore quicker order fulfillment.

Accurate image classification

Businesses do not only need to parse information from their files, they also need to classify these images to achieve accurate data structuring. Backlogs of unclassified images, audio files, and videos take up massive amounts of space on servers and on clouds, and do little good to businesses when they are not accurately grouped in ways that make sense to the industry and individual enterprise. Useful methods of classification might be grouping similar types of vehicles, identifying and grouping products featuring the same licensed image, or extracting and grouping images of icons and other symbols.

Insights and interpretations

One of the many useful results of data structuring is the possibility of interpreting freshly structured data in new ways to gain critical business insights. Some of the many ways businesses use their structured data are to make crop yield predictions based on field images, to extract nutritional information and other product specifications from PDFs, and to gain accurate structure measurements from raw architectural blueprints.

What is the best way to achieve data structuring?

Every business wants to gain the benefits of data structuring, but most have already experienced the pain of attempting to structure their data backlogs and their constant stream of new incoming data manually. In many cases, such as for large-scale retailers or major distributors, the amount of incoming data on a daily basis is so great that manually classifying and tagging it is actually impossible, even with outsourcing.

This is where automation comes in. Although data structuring is the first step towards an enterprise powered by robust AI, an automated tagging and classification system is the first AI solution that most businesses are able to implement. Data automation solutions train AI to handle the process of tagging, classifying, and annotating data, at scale and speed, so that businesses can manage their unstructured data backlogs and move forward with constantly structured incoming data. Because there are AI solutions, they allow businesses to implement auto-tagging without overspending and while gaining efficiency.

Once an initial AI auto-tagging solution is implemented, businesses have the opportunity to add more AI power. Next steps might include custom platforms for building, managing, and deploying suites of custom AI models tailored to the individual businesses and their unique challenges. The best solutions handle everything an enterprise needs to maintain relevant, swift, profit-boosting AI models that can be easily scaled as the business grows. Continuous monitoring and ongoing optimization should be available long-term.

This piece originally appeared on CrowdANALYTIX.



Katharine Stevenson

Writer and Content Manager for CrowdANALYTIX. PhD from UT Austin.