Data is an essential part of our daily lives. From business analytics to social media posts and back again, data is everywhere and in everything. In our advanced technological world, unstructured data is the most prevalent type of data out there.
Many businesses have unstructured documents and data from their web pages, images, servers, emails, networks, applications, and other machine data. Even something as common as text and multimedia messages are unstructured data.
According to many analytic estimates, up to 90% of all data is unstructured. Considering the world will have accumulated a total volume of 97 zettabytes of created, captured, copied, and consumed data by the end of 2022 (statista), it’s safe to say unstructured data is imminent and growing every single day. In fact, unstructured data grows at a rate of up to 65% a year.
This begs the question; what is unstructured data, exactly, and what makes it so dangerous to an organization?
What is unstructured data? Unstructured data can be anything – text, audio, or video files, images, emails – any information that doesn’t conform to conventional models. Its consistency is that of a data hodge-podge, being made up of many different types of data stored in their original formats.
Unstructured data is not yet defined by data models of schema, and it can be stored in non-relational databases like NoSQL and data lakes.
Think of unstructured data as information that isn’t being managed on a transactional platform, free from relational database management systems. Instead, they’re simply “free-spirited” records that contain bountiful information that can be useful for informed business decisions.
The original formatting of unstructured data gives businesses more information to use and collect quickly and easily, but it can be difficult to manipulate without the right tools.
There are an abundance of unstructured data examples. Unstructured data can be generated by machines or humans. As it pertains to human-generated unstructured data, it can include:
This could be any type of word-processing document, like presentations, log files, invoices, records, applications, electronic billing files, spreadsheets, and other flat files that have been enabled for the creation and storage of text.
A simpler category; made up of digital audio, video, photos, music, and other media (JPEG, MP3, MPEG-1, GIF, etc.).
The message fields in emails are unstructured, hindering traditional analytics tools from parsing them. Email can sometimes be semi-structured, as metadata makes it a little more definable.
This includes data from media-sharing sites like YouTube, Flickr, Twitter, Facebook, and other social media networks.
This includes human-generated data in the form of text messages, instant messages, iMessage, phone recordings, and other collaborative manners of communication data.
Regarding machine data, unstructured data can be in the form of:
This includes rich media in the form of weather data, geospatial data, land formation, and others.
Surveillance data and other data captured from inspection videos and photos taken from methods of digital surveillance.
This will include various types of scientific observations from space exploration, seismic energy, geophysical parameters, atmospheric chemicals, simulation & mathematical modeling results, and more.
Such as artificial intelligence, machine learning, etc.
Like weather, traffic, and oceanographic sensors
Structured data refers to information that is organized in a highly predictable and well-defined format, making it easy to process, search, and analyze using computer algorithms. This data is typically stored in a database or a spreadsheet, and its structure is defined by a fixed set of fields or columns, each with a specified data type, such as numbers, dates, or text.
Contrary to unstructured data, structured data (AKA “quantitative data”) is highly organized and can be deciphered more simply using a structured query language in a relational database. Structured data is data that comes in a standard format already follows a consistent order and conforms to a predefined data model.
Examples of structured data include financial data like sales figures, customer data like names and addresses, or product data like SKU numbers and prices. Structured data is often used to support business operations, make informed decisions, and perform data analysis, such as in business intelligence, data mining, and machine learning applications.
Data can be structured or unstructured in any form. In some cases, there are types of data that should never go unstructured. Important examples of places where data should have structure can be found in:
Booking data containing prices, destinations, flights, dates, etc. can be adapted to fit a data model precisely to fit the necessary parameters. Depending on how this data is stored, it may have predefined data models. Without this structure, it could be easy to access for data breachers.
Input in the form of structured data is used in the CRM analytics responsible for unveiling customer trends. CRM’s typically contain structured data that is highly organized.
Credit card numbers, stock information, and other financial transactions can be considered structured data if they’re categorized and organized, typically done by accounting. Financial information that isn’t organized is considered unstructured, and could be fatal to organizations.
A simpler explanation; each field in an Excel file has specified (structured) data put into defined and sortable rows and columns. Excel files, however, can contain ROT data and be stored with zero predefined data values. In this case, excel files would be considered unstructured.
Names, dates, addresses, phone numbers, should be structured. Private information should be clearly defined, organized information. Without it, your company may be at risk.
Consider semi-structured data as the connection between unstructured and structured data. It’s essentially a hybrid of both types of data. Semi-structured data is mostly unstructured, considering it doesn’t have a predefined data model or schema. Semi-structured data is only partially structured based on the metadata it contains, which sets data apart and allows it to be categorized and cataloged.
Metadata are internal tags and semantic markings that differentiate data elements. This allows data to be scaled, analyzed, searched, and placed in levels and pairings. Databases, documents, and emails can be semi-structured, but only make up a small piece of worldwide data.
This is likely the most common example of semi-structured data. The native metadata found in emails makes the classification and searching simpler to do without any extra data tools.
JSON doesn’t require a schema, so this is a format in which semi-structured data can be found. While it doesn’t need a fixed data type, it does require a structure consisting of objects, names, values, and an ordered value list in the form of an array, vector, or sequence, which gives the data structure.
HTML and other markup languages like XML can be read by both humans and machines. It doesn’t limit the amount of data you want to see in a document but does adhere to a hierarchy structure.
Unstructured data management is typically required to successfully own a structured database. Structured vs unstructured data are two types of data that differ in terms of organization and format.
Structured data is data that is organized in a highly predictable and well-defined format, such as in a database or a spreadsheet. It is typically represented by tables with rows and columns, and each column represents a specific data type, such as numbers, dates, or text. Structured data is easy to search, sort, and analyze using computer algorithms, making it valuable for data analysis and decision-making.
Unstructured data, on the other hand, is data that is not organized in a predefined manner, making it difficult to process, search, and analyze using traditional data management techniques. Examples of unstructured data include text documents, images, videos, social media posts, and emails. Unstructured data is often subjective and can contain a variety of different types of information, making it challenging to extract meaningful insights from.
Unstructured data can’t be forced into pre-defined data models. It can’t be stored in an RDBMS, and it’s incredibly difficult to sift through and analyze without the help of a team of analysts using a data tool. With the right tools, the data can be searched for content in the effort of analysis.
Contrarily, structured data can, and usually is stored in an RDMBS, and is typically called “relational” data, since it can easily be compared. Its formatting makes defining and placing data in the appropriate fields a cinch. This is what makes structured data so much easier to search, visualize, differentiate, and analyze without the need for special tools.
The main differences between structured and unstructured data are their format and organization. Structured data is highly organized and predictable, while unstructured data is more complex and varied. Additionally, structured data is typically easier to process and analyze, while unstructured data requires more advanced techniques and tools to derive insights from.
The tools necessary to handle data that is either structured or unstructured are being created to help businesses work with the wide variety of differences between the two types of data.
Overall, structured data provides enterprises with a more complete and accurate understanding of their business operations and the external environment in which they operate, enabling them to make more informed decisions and achieve their strategic objectives. This type of data helps businesses in several ways thanks to its automation, clarity, consistency, and versatility. For enterprises, structured data is required for:
Structured data came first. Tools that help analyze and test unstructured data are becoming increasingly popular, but the availability of structured data tools is more prevalent and accessible. Enterprises that require the most fine-tuned integrated applications possible will be able to find more of these using structured data.
Structured data doesn’t require an in-depth technical mind to be able to analyze information. As long as a worker has a basic understanding of the data set before them, they will be able to easily compare metrics to create informed decisions. This saves a company a ton of money on recruiting data professionals and purchasing extra tools.
Rich outcomes are possible with structured data. This means the results of a search will contain more information with interactive and visual features that help a user find more value in your business. Time-to-value is accelerated here, as people automatically find the answers they’re looking for thanks to the controlled presentation of information.
Structured data is organized enough for search engines to automatically arrange and categorize it. This feature allows data to be filtered and created to be compatible with other functions, like speech recognition, mobile applications, talk-to-text, and other accessible features. This makes your data more recognizable and available for all types of users, effectively expanding your audience, strengthening the user experience, and creating a more inclusive reach.
Unstructured data typically contains loads of compliance and security red flags - specifically when it comes to unstructured data and GDPR compliance. Despite the massive potential unstructured data has for businesses, it doesn’t come without some drawbacks. Anything that could cause enough challenges to hinder a business from profiting and succeeding is worth a little apprehension. The areas of distress concerning traditional approaches to unstructured data analysis include:
Unstructured data grows at an exponential rate every day. A single item or file could contain a few bytes to a few dozen terabytes each, and when they come in droves at a time, this can be difficult to manage. The larger the dataset, the harder it is to efficiently analyze and store data. This is especially the case as unstructured data comes in various formats. More intricate systems and tools are needed to effectively balance and maintain all objects and files.
Unstructured data can be seriously detrimental to an organization's financial success. The quality of each piece of data can vary regarding the quality because it can be quite difficult to verify for accuracy. This creates an issue for businesses to determine which data is reliable and which is irrelevant. For example, if a company gets data from social media posts that declare a part of their product didn’t work properly, that’d be a potential way to fix the issue and make things right for their customers. However, if that person was inaccurate in their post, either recalling a product from another company by accident or simply exaggerating the truth, then the business will be left with diluted data that will damage their reputation and product in the long run.
Most analytics tools and databases were built to manage structured data. This leaves data analysis professionals finding new ways to extract, clean, organize, and store incredible volumes of disorganized data. This also becomes a problem for collaborative efforts across the board. Conventional methods of sharing data (to or from hospitals, corporations, educational facilities, etc.) can be hindered as data replication and more sophisticated tools are needed to share an enormous quantity of chaotic data.
Since unstructured data is stored in its original formatting, it’s only defined on a needed basis. This opens the door for more data use cases because the data can be acclimated to fit a certain agenda.
One of the most important use cases for unstructured data comes in the form of customer relations and experience. Unstructured data can be thoroughly picked apart to find ways to enhance customer and user experience through a few different methods.
Text entered for a chatbot is usually done in a conversational manner, creating little bits of unstructured data. It’s then the chatbot’s job to interpret unstructured data and route the customer to the department or personnel with the answers they’re looking for. This is so a customer doesn’t have to call a representative or waste time doing their own research.
After the chatbot has analyzed the unstructured data being fed to it, it can inform sentiment analysis to tell a business how they are doing and what needs to be improved. This can also be done by analyzing social media posts, online discussions, company reviews, support tickets, and more. This can provide retailers, manufacturers, marketers, and other businesses with the insight they need to improve their sales experience.
When customer experience is considered for companies, the rest of the business plans and goals will naturally follow. Aside from making the customer happy, other business requirements can be optimized using unstructured data.
Predictive data is used to notify businesses when data suggests critical findings. This allows businesses to act and address the issue before it becomes detrimental to their company. For instance, if sensor data picks up changes in a given field, workers can be prompted to check and maintain their equipment to brace for the impact of a change in circumstances.
Once a company becomes aware of its product value through the unstructured data provided by its customers, it can begin to build on that. Knowing exactly from the source what is wrong with a product and how it can be improved paves the way for companies to act and meet customer expectations. Furthermore, gaps in the market are easily identified and can be covered more effectively. This can increase time-to-value and ultimately make a business more profitable.
Data mining enables businesses to sift through unstructured data to find the most insightful information possible to perfect their marketing strategies. With unstructured data, companies can see the “why”, “when”, and “how” regarding purchasing patterns and the personal preferences of their target audience. This allows them to hone their marketing to appeal to the specific wants and needs of their clients and customers. Unstructured data can also be used in this way to catch up with what competitors are doing, and how to cover areas they may be missing.
Considering how important unstructured data is to enterprises, it’s common for them to require the conversion of unstructured data to structured data without compromising its value. To do this, methods of collecting, cleaning, structuring, and storing or moving the data should be performed.
With an end goal and relevant data sources in mind, technology is used to gather unstructured data in real time. This data is typically stored in a data lake that can hold the data with its raw formatting.
As mentioned before, one of the biggest issues with unstructured data is the amount of redundant or sensitive content it could contain. By cleaning the data, useless information can be skimmed from the surface of the important stuff, leaving you with only the most critical data possible.
Unstructured data programs use algorithms to dissect unstructured data so it can put everything in categories and classifications. If identified through text, data structuring techniques are used here to extract certain entities that are important, like “name”, “location”, “company”, “time” and other data patterns that are easily comprehended by humans and machines.
After data has been structured and organized, it can be properly analyzed using traditional data analysis tools.
Unstructured data analytics are the key to the future of business. Since it’s rebellious in nature and new data is constantly being generated, data-wrangling tools have created new opportunities to use unstructured data. To help businesses harness the massive volume of unstructured data information, data tools have led the way in data intelligence and automation.
Big data tools like Hadoop can process and store unstructured data. As previously mentioned, data lakes are another useful tool for storing unstructured data integrated into its original formatting. There are other popular platforms created to work with unstructured data, including:
Contains analyzing and reporting features that help business decisions (an analytics SaaS dashboard, such as the Aparavi dashboard)
Combines unstructured data with other data sources so businesses can analyze them on a business-use basis
Widely accessible, and designed to deal with quantitative data (an all-in-one software like Aparavi)
When humans and technology collaborate to provide businesses with relevant data, DataOps platforms come through to help companies take control of their data lifecycle. One particular platform, Aparavi, can help companies in this way.
The Aparavi Platform uses data intelligence and automation to simplify unstructured data management across an enterprise. Wherever your data is stored, Aparavi uses a single cloud-based user portal to search through all the unstructured data. This is done with an advanced metadata search and indexing content, which allows the content to be found accurately and efficiently.
Aparavi can find defined content in unstructured data using advanced custom classifications and speedy analytics. It can quickly identify irrelevant data and provide continued data cleanliness using automated classification actions based on policies and compliance checks that you administer. From there, data is easily leveraged for intelligent business decisions from the most accurate and insightful information possible.
With Aparavi, you can locate unstructured data regardless of where it is. From social networks, geo-location data, documents and emails, weblogs, clickstreams, and more, unstructured data can be wrangled and fed to BI and ML applications.
Aparavi’s classification, indexing, and tagging features can proficiently organize your data and place them all in the necessary categories so they can always be located, secured, and used in the best ways possible. This type of data control takes structuring unstructured data to a new level, as data is segmented by level of sensitivity and other metrics that are important and unique to any business.
Structuring unstructured data to streamline your data-driven decisions and marketing efforts has never been this easy. Schedule a demo today and see how Aparavi can transform chaotic data into intelligent information to enhance your data management.
Storing unstructured data in data lakes and warehouses are a popular choice that can be effective depending on how much data you have. Similarly, cloud databases can appease all the storage requirements necessary for data, including essential data management and scalable archiving with the help of a SaaS platform like Aparavi.
Certain data tools can identify unstructured data simply by applying custom classifications and analytics strategies that can identify, clean, and categorize irrelevant, unorganized data.
The right platform can help you index any kind of file with selectable text by searching through them and classifying each one automatically. Manual work can potentially take years.
Unstructured data does not have any particular formatting or sequencing structure, making it difficult to identify. Unstructured data also cannot be stored in a row/column structure and does not follow any rules or schema.
Unstructured data can come in the form of text, image, log, or application data files, PDFs, emails, media sharing data, and more! Everyone has unstructured data, and it can be dangerous to the health of an organization.
Begin with your end goal in mind; what knowledge do you want to gain from this analysis? Then you can begin collecting, cleaning, and structuring unstructured data.
Image files and other media are classified as unstructured data since they don’t have a defined structure - with defined classifications, tags, and policies they can become structured.
Unstructured data can be stored in clouds, data warehouses, data lakes, NoSQL databases and more. Just about any location can contain unstructured data.
First, the unstructured data needs to be accessible, searchable, and organized. This is done by using data tools and platforms such as Aparavi that can properly leverage the value of data for proper data hygiene and processing.
Yes, email is considered unstructured data because the message fields cannot be parsed with normal data tools. It can also be considered semi-structured data due to metadata. Aparavi can help structure any kind of existing data.
At least 80% of current living enterprise data is unstructured, and the rate of unstructured data is constantly growing.
Without structured data, search visibility, indexing, and click-through rates for companies can suffer greatly in the realm of compliance, data breaching, overpaying for storage, and fines. This can diminish time-to-value, and companies can even go bankrupt if left unhandled.
Aparavi is a tool that can help you perform all of these pressing data tasks and then some!