What Is Unstructured Data

Unstructured-data-diagram

The concept of data has changed from the traditional data of yesterday. These days, 90% of data is defined as unstructured data. Unstructured data is usually text-heavy, not easily stored, or organized in a traditional database. The free-form nature of unstructured data makes it difficult for organizations to analyze and search through it. Unlike structured data, unstructured data has not been leveraged in the marketplace until the rise of data analytics tools. Sophisticated technological tools powered by Artificial Intelligence (AI) and Natural Language Processing (NLP) liberate value from unstructured data to empower businesses and people.

In today’s hyper-connected global economy, a competitive advantage could make or break a business. For a business committed to innovation, new ways to access business intelligence are a commodity. When businesses gain new insights based on data, they did not even know they had, powerful things can happen.

Unstructured Data Examples

Unstructured data is not hard to find in daily business operations. If you think of data that does not have a recognizable structure, you have identified an example of unstructured data.

Emails

While some businesses count emails as semi-structured data because they contain: date, sender, and recipient address and subject information. Other businesses consider emails as unstructured data because of the free-form nature of text in the body of the email. Considering 269 billion emails are sent and received daily that is a lot of untapped insights being trapped in email communication.

Social Media

In 2021, social media is a ubiquitous form of communication. It has become part of a lifestyle for billions of people. For most people especially the younger generations, it has become the preferred mode for creating and sharing content. Other than personal use, social media is also used by the government and businesses in industries such as:

– Retail

– Entertainment

– Education

Politics

In 2021, there are 2.80 billion active users on Facebook, 192 million active users on Twitter, and 1.074 billion active users on Instagram. Every minute: 243,000 photos are uploaded on Facebook and 350,000 tweets are generated on Twitter.

The sheer volume of data that is being generated by social media makes it a good resource for unstructured data. It is global, instantaneous, responsive, and round-the-clock data in the form of text, images, videos, and geolocations.

Social media can be broken down into structured and unstructured data. The text in social media posts is unstructured data. Conversely, data about friendships, followers, and groups is structured. Using unstructured data for text analytics to conduct sentiment analysis can yield powerful insights into social media:

– Behavior

– Trends

– Influencers

– News

Internet Data 

The Internet is a deep source of unstructured data that is continuously updated and curated by users. Using robust data analytics tools can generate powerful insights for businesses into customer behavior and collective trends that influence consumer buying behavior and loyalty. Having these insights enables businesses to shape marketing strategies and drive revenues with actionable data rather than guesswork.

Some examples of unstructured data gathered on the internet include:

· Text files

· Photos

· Video files

· Audio files

· Webpages and blog posts

· Presentations

· Survey responses

Agreements/Contracts

A lot of unstructured data can be found in agreements/contracts that are used in various industries. For example, the legal industry can parse a contract and identify key legal terms that were previously inaccessible with structured data analysis. Likewise, the real estate industry benefits from instant parsing and analysis of lengthy real estate contracts.

Medical Documents 

In today’s economy, the healthcare industry is booming and making cutting-edge advances in technology and service delivery. It is an industry that generates a tremendous volume of machine and human-generated unstructured data.

Examples of machine-generated unstructured data include:

– Data collected by imaging devices

– Wearable health monitoring devices

– Biosignal data

Human-generated data includes:

– Transcripts of patient/provider conversations

– Audio of patient/provider conversations

Unstructured data in the healthcare industry presents a tremendous untapped potential for mining and analysis. The benefits of unstructured data could revolutionize patient treatment. By using AI to improve diagnostics and patient care, public health management, and medical as well as pharmaceutical research, the healthcare industry moves forward.

Business Documents

The business world is flooded with unstructured data such as:

– Emails

– Presentations

– Text

– Images

– Videos

These formats represent sources of important information repositories within the organization. However, most businesses are not using unstructured data to drive decisions. The industries that do use insights from unstructured data are market leaders. For example, financial services in particular banking might use emails to understand a consumer’s credit rating. The legal industry can utilize insights to validate a legal contract. Human resources can apply insights from unstructured data to automate the resume process and improve the applicant selection process.

For business documents to be mined successfully, natural language processing (NLP) and machine learning (ML) techniques are used to understand large volumes of documents. When unstructured data is processed automatically, it minimizes the human error of manual processing.

Structured Vs. Unstructured Data

For businesses to effectively leverage unstructured data, there needs to be a distinction made between unstructured data and structured data.

Differences

Organizational Structure

Structured data is easy to search and organize. It is stored in a defined formats while unstructured data is usually stored in an unorganized format. Structured data is organized in rows and columns so it can be mapped into predefined fields. Conversely, unstructured data does not have predefined fields and can be presented in a variety of formats.

Insights

Structured data yield quantitative insights using numbers and statistical analysis. Unstructured data yields qualitative insights from sources such as social media, customer surveys, and interviews. These sources are not in a numerical format and require more advanced analytics techniques like data mining to extract insights.

Storage

Data warehouses are used to store structured data. Data lakes store unstructured data. The difference is that a data warehouse is an endpoint for data whereas a data lake is an almost endless repository for data. Data lakes store unstructured data in its original format or after the initial cleaning process.

Ease Of Use

Structured data is easy to understand. Unstructured data requires more effort to analyze and understand. This is one of the most significant differences between structured and unstructured data. For humans and algorithms alike, structured data is easy to understand. It lends itself to analysis. On the other hand, unstructured data lacks a predefined data model, and it is challenging to deconstruct. The best practices for unstructured data are still in infancy making it challenging to analyze customer reviews, social media data, and customer communication.

Format

Structured data comes in predefined formats. Unstructured data comes in a variety of formats. Because unstructured data is housed in a data lake that does not require transformation, it comes in a variety of shapes and sizes. Unstructured data can be in any medium such as video, audio, images, and text.

Industries That Have Unstructured Data

The nascent stages of unstructured data techniques have opened opportunities for businesses across industries. The more customer touch-points an industry has the greater the opportunity to capitalize on unstructured data insights. Businesses have leveraged Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) practices with unstructured data techniques to transform and innovate business practices.

Types Of Industries

Some of the industries that have benefitted from unstructured data include retail and financial services. The retail industry was at the forefront of using customer data to guide operations. It used customer emails, voice, images, and store records to segment marketing and influence consumer behavior. Other industries followed the example of retail to transform operations. This included real estate, legal, healthcare, marketing, and hospitality.

Financial Services

For financial services, investing in unstructured data has paid off in time savings. In finance, time is money, and the ability to make decisions faster than competitors is a competitive advantage. For example, NLP can be used to parse unstructured data in financial documents to pull out key numbers for earnings reports. Leveraging unstructured data can also help increase compliance in finance.

How Can I Find Value In Unstructured Data

As unstructured data is gaining momentum in industries and advancing in technology, many users pose the question: how can I find value in unstructured data?

To maximize the benefits of unstructured data, the user needs to choose the right data analytics tool and techniques. For example, the Content Analytics Platform (CAP) from Scion Analytics empowers users and businesses to liberate value from unstructured data. If you think of unstructured data as chaotic with free form structure, CAP takes it and transforms it into structured data so you can use the data and convert it into usable knowledge.

Data Discovery Process

In the data discovery process, CAP uses “indexing”, a process of indexing words within the tool. Another data discovery process is common concepts for one or more documents. It is when the platform analyzes a collection of documents to see what they have in common. After the data discovery process, data technologies are used to process the data.

Data Technologies

When it comes to unstructured data technologies, CAP does a parsing (breaking it apart) of unstructured data documents, normalizes the data, and then identifies key concepts, keyword phrases, and dictionary terms to find the elements that help you identify the area of the data that is important to you. Once the data is transformed into digestible content, it allows you to make decisions and perform actions with the content.

The CAP uses AI to help organize and transform the data and NLP technologies to understand textual content. NLP technology helps with the readability capability to understand the purpose of the content.

Insights

A user can customize the capabilities of the CAP to maximize the insights gleaned from unstructured data for a particular business. Previously untapped data now becomes available, previous questions now have answers and the user is empowered to make data-driven decisions. For businesses, the transformation from guesswork to actionable insights demonstrates the power of unstructured data.

Conclusion

Unstructured data is just beginning to dictate the future of businesses across industries as it carries more inherent value and opportunity than structured data. It is through the analysis of social media, emails, and legal contracts that businesses learn how to predict the behavior and influence buying patterns of customers. From retail to financial, businesses are adapting to change and embracing unstructured data with the right tools like CAP. This is just the beginning of what unstructured data can do.

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