Data is an integral part of business decisions.
The Big Data Analytics market could hit a value of $103 billion by 2023, creating an astounding 2.72 million jobs in data science over a few coming years.
A company’s vision improves following its ability to gather the correct data, interpret it and use the lessons derived to influence its operational success.
However, the amount of data that companies access today is rising and comes in different kinds and formats. The data in use are grouped into two main divides: structured and unstructured data.
So what is structured and unstructured data?
Structured data consists of clearly outlined data types that come with searchable patterns. In contrast, unstructured data isn't easily searchable and includes commonly used formats such as video, audio, and social media post content.
For companies in the life science industry, both types of data are essential. Their work requires analysis and visualization to make meaningful discoveries.
Structured data definition
Structured data defines resident data in the form of a fixed field within a record or file. The field stores length-specific data.
Examples of structured data include ZIP codes, phone numbers, and email addresses. Records can be of string and variable length or generated by humans or machines.
Structured data is searchable by humans using generated queries and algorithms using data types and field names such as numeric, alphabetic, date and currency. Structured Query Language (SQL) is used for querying within relational databases.
This type of data is typically stored in a relational database management system (RDBMS) and usually consists of text and numbers, which can be sourced manually or automatically within the RDBMS-defined structure.
- ATM activity
- Inventory control
- Student fee payment databases
- Airline reservation and ticketing
Structured data: Pros and cons
The table below outlines the pros and cons of structured data:
| Pros | Cons |
| Easier to manage and also requires less processing for retrieval | Structured data is stored in data warehouses which, while built to minimize space, is difficult to change |
| The querying process is simple since algorithms can easily crawl structured data | It comes in a predefined format, therefore has a limited scope of application |
| There are a variety of tools that simplify the access, management, and interpretation of structured data |
What is unstructured data?
Unstructured data, also known as qualitative data, is the data type that is stored in its original format and isn’t processed until the need arises. Sometimes, this type of data has a specific structure, though this isn’t predefined.
Unstructured data exists in greater variety and abundance than structured data. Essentially, unstructured data is responsible for at least 80% of all enterprise data, and the stats are going up each day.
Consequently, companies that don’t consider unstructured data are missing out on a crucial angle of business intelligence.
Typical unstructured data that is human-generated includes the following:- Email, which is semi-structured via its metadata
- Websites like Instagram, YouTube, and similar photo-sharing platforms
- Social media channels like Twitter, Facebook, and LinkedIn
- Mobile data through text messages
- Business application data from MS Office and other data processing packages
- Media files, including audio and video file formats
- Digital surveillance videos and photos
- Satellite images from weather and landforms
- Sensor data from oceanography and vehicle traffic
Unstructured Data: Pros and Cons
The table below outlines the pros and cons of unstructured data:
| Pros | Cons |
| It comes in a wide variety, enabling a large number of applications and use cases | The wide variety of formats makes unstructured data hard to interpret and leverage |
| Quickly collected and stored since it doesn’t have a predefined format of storage | |
| Stored in local or cloud data lakes, making it highly scalable | |
| It comes in greater volumes than its structured counterparts, thereby providing greater opportunities to use data competitively |
The middle ground: Semi-structured data
Semi-structured data is also nicknamed “data that is self-describing.” This data format has a nature that falls between its unstructured vs. structured counterparts.
Technically, it uses semantic markers that store the data as a dataset that consists of records and fields.
Examples Of Semi-Structured Data
A familiar example of semi-structured data is found in photos stored in smartphones. Each photo has an element of location, time, and other structure information that easily distinguishes the photo from others.
Common semi-structured data formats include:
- JSON (JavaScript Object Notation), which is structured in name/value pairs, as well as an ordered value list. Its interchangeable nature can be easily transmitted between servers and web applications.
- XML is a semi-structured document language. It has a tag-driven structure that can be flexibly used for web transportation, making data structure and storage universal.
- NoSQL (“Not Only SQL”) is a database type that varies from relational databases in that it doesn’t separate data from its schema. This makes NoSQL a hot favorite for storing text that varies in length. NoSQL examples include CouchDB and MongoDB.
Unstructured vs. structured data: 5 notable differences
What is the difference between structured data and unstructured data?
Structured data vs. unstructured data can be appropriately understood by considering:- Who’ll be using the data?
- What data types will they be collecting?
- When should the data be prepared before being stored or during usage
- Where will the data be kept?
- How will the data be kept?
The five questions above emphasize the fundamentals and help users understand the difference between structured and unstructured data.
Another crucial difference, apart from storage, is the nature of the analysis. Structured data has attracted mature analytical tools, while those used for mining and processing unstructured data are still in development.
Traditional data mining tools make little value from valuable data sources such as weblogs, rich media, social media, and customer interaction history.
Additionally, unstructured data commands greater than 80% of all enterprise data, with a 55% to 65% matching growth rate per annum.
Organizations that don’t match up their tools to analyze this massive data category leave valuable business analysis on the table.
The table below overviews structured vs. unstructured data concerning differences.
| Structured data | Unstructured data | |
| Data definition |
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| Data analysis |
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| Data nature |
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| Data storage |
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| Data format |
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Metadata: The master data
Metadata is “data about data.” It’s the master dataset that defines other data types in a given domain.
Metadata contains precious details that help a user better analyze a data item to aid in decision-making. Additionally, there are preset fields with additional information concerning a given dataset.
For instance, a web article contains metadata such as a featured image, headline, alt-text, snippet, and slug. This information differentiates pieces of web content on the website. This also applies to tags applied to a video.
Application of unstructured data to life science focused firms
The life science industry has recently undergone a digital data disruption, from IoT wearables to high-resolution imaging, not forgetting on-demand patient information that can now be digitally obtained.
Health organizations process a lot of data daily through normal business operations. Collaboration is key to healthcare data processing and interpretation, as observed by Ketan:
“It is critical to collaborate with researchers and the technology ecosystem to develop innovative solutions to seemingly intractable problems emerging in healthcare and life sciences today.”
- Ketan Paranjape, Director of Life Sciences and Healthcare-Intel
Existing and emergent analytical techniques can be used to process “dark data” to understand the treatment and corresponding outcomes better. The insights obtained can further develop more accurate treatment plans for individuals and populations.
Why life science firms should harness their unstructured data
Based on the challenges and opportunities provided by dark data, life science organizations can leverage their unstructured data for the following main reasons:
- Deciphering institutional knowledge: Papers and research conducted and written by professionals and scientists, videos that highlight a safety procedure, and presentations based on corporate research are all examples of corporate, unstructured data.
To effectively operate as a company, there should be a way to harness, search and make this data discoverable. Otherwise, staff won’t utilize this asset effectively, as seen when these key contributors exit an organization.- Adoption of better data and meta-data management techniques. Unstructured data pose a new challenge to life science organizations: they have ontologies and lexicons that don’t harmonize very well with broader search technology.
When organizations master how data is used, combined, and reused, they’ll achieve better reconciliation and analysis for better accuracy.
This is also the case when a firm purchases an asset from another organization: it usually takes a long time to realize the knowledge acquired fully. The speed of revelation is affected by how well the company responds to and manages unstructured data.
For example, progress on COVID-19 research has been made possible by repurposing research on existing drugs. A large portion of this research was done on data in its unstructured form e.g. experiment memos or data from an excel sheet.- Advancement of personalized medicine by processing the treatment routines’ dispensement and outcomes. Patient statistics should be better processed to care for preferences, genomes, and body characteristics for users with the same conditions.
ResoluteAI Can Help With This Process
To fully answer the question “what is the difference between structured and unstructured data,” we’ll need to understand that the process of analyzing structured data presents a formidable challenge considering that over 80% of enterprise data falls into this group. Through artificial intelligence and machine learning techniques, enterprise search software can effectively convert your unstructured data into structured data.
A working enterprise search product can help organizational staff search through a company repository.These records could contain relevant videos, PDF files, audio files from a meeting, or plots within Excel spreadsheets.
Harnessing these types of unstructured data can help staff get the information they want in a few clicks.
ResoluteAI is an intelligence search startup aimed at empowering organizations dealing with life sciences to make their next big discovery using data. We have search and analysis tools that are specially fit for the life science sector.
Our tools allow clients to handle searches at the corporate level and across publications, patents, and clinical trials. ResoluteAI’s search tools incorporate the following:
- Search functionality that rides on artificial intelligence and machine learning technology, fine-tuned for the sciences
- Search support for all relevant media, including video files, PDFs, and spreadsheets.
We have a base of clients who fall in the top 12 in big pharma by market cap. We are also proud to work with a couple of top 5 international consumer products companies that incorporate our tools into procedures used by their scientists for experiments and easy reference. Biotechnology firm Aditxt’s case study also highlights how biotech companies can use their unstructured data to gain critical insights for future research.
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