October 22, 2021

What is structured vs. unstructured data?

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As self-service BI and analytics tools become more popular, the average business user needs to be more technologically-savvy than ever before. 

Increasingly, employees in every department are using data to understand trends, jump on opportunities, and streamline their work. When business intelligence and embedded analytics are becoming more integrated into daily workflows, employees need basic data literacy skills to maintain governance standards and act on valuable insights.

Enterprises have a mounting volume of data they can leverage to better understand their customers and make more informed business decisions. But, these huge amounts of data—what we often refer to as big data—come in a wide variety of formats and types, making it difficult for businesses to manage and use it effectively.

When most of us think of data, we think of spreadsheets of names and numbers sitting in a database, otherwise known as structured data. But, only 20% of data is considered structured, and that number is continuing to drop; now, more often than not, companies are trying to glean valuable insights from unstructured data, which can be much more difficult to normalize, store, and analyze. 

Understanding the difference between structured and unstructured data is an important starting point for data literacy. Here’s what you should know about structured and unstructured data.

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What is structured data?

When quantitative data is in a format a database can readily read and understand, it’s considered structured data. In fact, traditional structured data is often machine generated to fit into a format like a table, so analyzing that data is easy since it fits into a predefined data model. 

Common examples of structured data are Excel CSV files or SQL databases filled with financial transactions, phone numbers, or inventory information. Since rows and columns are labelled, you can quickly and easily aggregate data from multiple sources into relational databases to garner insights through analysis.

Since financial and transactional data is often normalized and structured, many businesses are familiar with using structured data to make business decisions already. Once data has been scrubbed and standardized, it’s ready to model and evaluate. However, only using structured data poses huge limitations for evolving businesses, especially as business intelligence tools incorporate artificial intelligence (AI) and machine learning (ML) capabilities. By only examining structured data, business leaders may only see part of the story their data could reveal. 

Also read: What Is AI and How Can Businesses Use It?

What is unstructured data?

A growing percentage of business data is considered unstructured. Unstructured data is any data a company uses that’s stored in a native format other than a normalized spreadsheet or predefined data model. 

Commonly, unstructured data contains more text than numbers or figures, making it more difficult to standardize. This means content from emails, web content, social media posts, chat records, and more are considered unstructured. However, unstructured data includes data in formats beyond text too, like audio and video recordings, photos, scientific data, and more. Generally speaking, any qualitative data that isn’t easy to normalize into a spreadsheet and search through falls under the unstructured umbrella. 

A huge percentage of a company’s growing data gravity is unstructured and needs to be normalized before it can be analyzed. Unstructured data can be exceptionally useful to help enterprises make better business decisions, but only if it’s properly leveraged. Otherwise, storing this data in data lakes and noSQL databases can quickly pose an ever-growing expense and potential risk for organizations.

What is semi-structured data?

Semi-structured data combines both structured and unstructured elements, but it’s often recognized as part of unstructured data. Often, while parts of semi-structured data can be read and analyzed within existing and standardized data models, the structured elements alone don’t provide the full value of the data. 

A good example of semi-structured data is HTML code. While HTML code is organized in a structured way and contains defined tags and elements, it’s still challenging for a database to make sense of it. Often, there’s still a large degree of restructuring necessary to analyze it and garner insights, which is where BI tools are helpful. 

How are unstructured and structured data important for businesses

Ultimately, enterprises leverage data analysis to make more informed decisions in every business unit. Adopting a data-driven culture offers companies a substantial competitive advantage by empowering business leaders to quickly recognize and jump on business opportunities, provide better customer service, design stickier sales and marketing campaigns, and create more compelling products and services for their clientele. 

Also read: Considering Your Technical Needs: Choosing Between Traditional vs. Self-Service BI Tools

While many companies have traditionally leveraged structured data through Excel modeling, structured data only offers a small sliver of information on a company’s customers and their habits. Now, businesses can use BI tools like Qlik, Microsoft Power BI, and Tableau to generate even more meaningful insights in record time by analyzing their structured and unstructured data together. 

While analysts may still need to normalize some unstructured data, capabilities like natural language processing (NLP) are increasingly transitioning unstructured data into a recognizable format that BI tools can read and analyze. From there, BI tools use AI and ML to recognize patterns and clear out noisy, or irrelevant, data. Both analysts and business leaders can use BI tools and embedded analytics to query this newly-expanded database and visualize the data in a new way.

Using AI and ML, businesses can ask new questions and find new actionable insights from a wider, more inclusive data set. Rather than looking solely at retroactive data to try to predict the future, BI tools use AI to offer businesses predictive analytics, which can help them make more successful business decisions and present new revenue-generating opportunities. 

Before, many companies could only leverage the data clearly accessible in a spreadsheet, which might take days or weeks for an analyst to present in a report. That meant business leaders could spend multiple days poring over disparate reports to plan for the next quarter or make an important decision. But, now that structured and unstructured data can be readily analyzed and accessed through self-service BI tools, decision makers across the business can use analytics to spend less time deliberating and more time taking value-driven action. 

Using structured and unstructured data to make better decisions

Business leaders are inundated with daily choices. In the past, many leaders relied heavily on their expertise to choose a direction. However, without tracking their performance with data, they may not clearly know whether they made a smart business decision until the revenue figures dipped or soared.

By using structured and unstructured data, leaders no longer have to leave business performance up to chance. By taking advantage of the latest business intelligence technology, professionals across the enterprise can gain deeper insight into what’s working and what isn’t in their business. Plus, with AI and ML, these leaders can even receive specific recommendations for how to move forward and generate the best results.

Data offers leaders an extra vote of confidence to support their business acumen and expertise. By using data to better understand their customers, businesses more effectively connect with their buyers and create products or services that better meet their needs. By combining structured data with unstructured data, workers can access a new perspective on their business practices, embrace new opportunities, and pivot more quickly when a decision goes south. 

Utilizing your company’s data starts with the right tools. Business intelligence tools make it easier than ever to normalize, analyze, and visualize your company’s valuable data. Interpreting structured, unstructured, and semi-structured data is substantially easier with the help of AI, ML, NLP, and other capabilities common to BI tools. 

Start exploring what robust data analysis can do for your business. Contact us today to find a BI tool that’s right for your needs.