Are you grappling with the unstructured vs structured data question?

To become a truly insights-driven organization, companies must be able to make use of all enterprise data available to them. One of the biggest issues? Unstructured data, which Forbes reported in 2019 affects 95% of businesses.

And it’s not surprising, when most analysts estimate that 80% of all data generated is unstructured. Think of emails, social media posts, instant message chats, call center transcriptions, lovingly crafted PowerPoint presentations, and so much more.

First, let’s back up and discuss what the differences are between the two, plus a third gray-area term called ‘semi-structured data’ that exists between the two.

What Is Structured Data?

Structured data is what is commonly thought of as quantitative data, and describes the data contained in fields. It is called structured because its nature and function are identified by metadata tags.

This type of data is usually in a structured format, like that of facts and figures, and is the most traditional form of data (since the earliest database management systems were able to store and process it!).

This type of data is highly organized and fits neatly into columns and rows, which makes it very easy to search and analyze using traditional information retrieval systems.

Structured data is predefined and is often referred to as ‘schema-on-write.’ The most popular example of this is in a relational database. Because data has been formatted into precisely defined fields, like addresses and phone numbers, it’s easy to query with a structured query language like SQL.

Examples and Uses of Structured Data

In enterprises, structured data is commonly found in systems that handle customer orders, inventory, and accounting. Examples of data found in these are names, addresses, emails, credit card numbers, bar codes, and order dates.

Common use cases of structured data come in the form of relational databases in processes such as online reservations (e.g., booking flights or dinner at your favorite restaurant), inventory control (e.g., quantities of items in a flash sale), and customer relationship management (e.g., customer’s last date of purchase).

Structured Data Benefits

  • Machine learning algorithms can easily use it. Due to its fundamental nature, machines can easily manipulate and query data that’s been well organized. (At least, well-organized in a way that machines like it!)
  • Business users can easily use it. You don’t need a deep understanding of a concept to run a query in a database or spreadsheet and bring up relevant information when that information has already been predefined.
  • Traditional BI tools have greater access. Historically, structured data has been the only option. Thus, products designed to use and analyze structured data are abundant, giving data managers a lot of choice. 

Structured Data Drawbacks

  • Its use is limited. Since structured data is in a predefined format, it can only be used for its original purpose. 
  • Storage is inflexible. Structured data is stored in data warehouses and relational databases, both of which are rigid in their ability to store only certain file formats. Any changes to records will require an update of all the structured data.
  • It’s difficult to unify. In order to join data from different schemas you have to normalize it. This can be highly intensive.

What Is Unstructured Data?

It’s usually not a good idea to define something as a negative, but it’s easy to remember that while structured data has a predefined format, unstructured data does not. This type of data is text-based or non-text based and oftentimes is made by humans, although more and more unstructured data is now machine-generated.

Companies create a vast amount of unstructured data and will use a variety of techniques and technologies to mine valuable nuggets of information from unstructured data. Full-text search using a unified index is a must – but machine learning, deep learning, and cloud-based technologies are becoming staples as well.

Because it is unstructured, this type of data cannot be searched with a structured query language and typical business intelligence tools. But that doesn’t mean you can’t do analyze this type of data.

Examples and Uses of Unstructured Data

Unstructured data includes content like text, audio, image, and video files such as social media posts, emails, chat messages, presentations, transcripts and open-ended survey responses. Many business documents are also unstructured, such as legal contracts and reports. Increasingly, more unstructured data is coming from advances in IoTs and real-time streaming data, such as data from sensors and satellite imagery.

By using an intelligent search engine that is powered by machine learning, companies can allow data scientists to gain valuable and actionable insights. One popular use case is mining social media posts and product reviews to better understand customers’ preferences.

Another use case, predictive analytics, takes data and makes predictions about future events and trends. This can be for aligning your business with customers’ future buying habits or to ascertain medical risks for patients based on images such as MRIs.

Unstructured Data Benefits

  • It comes in flexible formats. The variety of formats means a greater number of use cases and business applications are possible.
  • It leads to easy storage. Since it doesn’t have to be predefined, it can be stored quickly and easily.
  • Companies can use it for competitive advantage. There’s a lot of unstructured data and it’s only growing. Due to its volume and ability to provide more substantial insights into customers, unstructured data can help companies become more competitive if used effectively.

Unstructured Data Drawbacks

  • It’s difficult to analyze. The variety and number of different formats makes unstructured data challenging to analyze and use. Data scientists can’t use regular BI tools.
  • Lexicons can be varied. People have a myriad of ways of saying – just about everything. You will need to have machine learning in order to semantically associate terms.
  • It requires a specialized tool and expertise. Most people won’t be able to get value from this raw form of data without help. A team of data scientists using specific data management tools are necessary to handle the analysis of unstructured data – unless you have a platform designed to help.
  • Data management can be challenging. Because unstructured data is often difficult to index and organize, it may be left ungoverned and forgotten, which can lead to legal and compliance risks for a business and expensive storage costs.

Structured Data vs. Unstructured Data

Although most of the explosion of incoming data today is in the form of unstructured or semi-structured data, companies today still rely on structured data in relational databases for deriving insights. Both types of data provide important information to businesses today, depending on the business needs.

Structured vs. Unstructured Data: Key Differences

Structured DataUnstructured Data
AnalysisQuantitativeQualitative
SchemaSchema-on-writeSchema-on-read
SearchEasy with a SQL-based toolDifficult, needs robust full-text search enhanced with machine learning for natural language querying
FormatPredefinedUsually text onlyNot predefined text, audio, video, image
StorageRelational database management systems, data warehousesApplications, NoSQL databases, data lakes
Source: Adapted from TechTarget

What Is Semi-structured Data?

third category of data, semi-structured data, sits between structured and unstructured data. It does not have a predefined data model, so is not possible to arrange into tables, but it also possesses structural elements that make data storage easier.

Many times, semi-structured data comes from taking unstructured data and adding structure to it to make it more searchable and accessible. The structure in this kind of data usually comes from metadata (e.g., heading, date, and location of a photograph), which allows for some level of organization, search, and analysis.

Examples of Semistructured Data

Semi-structured data includes the data model JSON and XML markup language, commonly found in web pages and applications. Email is a common form of semi-structured data as its metadata, such as date sent or recipient email addresses, can be searched and organized, but it contains unstructured data in its contents.

Making the Most of Your Enterprise Data

Most companies, 64% of respondents in a Deloitte survey, still rely only on their structured data for business analytics and actionable insights. This means most businesses are missing out on the benefits of unstructured data: The same survey found that executives that took unstructured data into account were 24% more likely to surpass their business goals.

Because of the volume and vastness of unstructured data, it is often referred to. as Big Data Analytics. Working with unstructured content is admittedly difficult, yet it’s a major reason AI-powered enterprise search exists.

Working with unstructured content is admittedly difficult, yet it’s a major reason enterprise search exists. Since unstructured content resides in containers, it’s hard to find and identify unless you have good metadata in place.

A unified search system designed for enterprises can give users a common interface to allow natural language processing and querying of this data. This allows data scientists and business professionals to retrieve the information they need to make the best decisions for their businesses.

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About Rebecca U. Shin

Rebecca U. Shin is a freelance writer based in Southern California. She has more than two decades of journalism and content marketing experience with a focus in the B2B software space. She is a graduate of Pomona College and Northwestern University's Medill School of Journalism.

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