What is unstructured data?
Unstructured data is information that has not yet been properly cataloged by data analysis tools. This type of data is potentially very valuable, but in its current state, any actionable insights it may contain can’t be taken advantage of.
Unstructured information is most commonly found in text form. Potentially any body of text can be analyzed using unstructured data analysis tools. Relevant data sources include social media conversations, customer surveys, and customer interactions from media marketing campaigns. Besides text, some business intelligence tools can also perform analytics on images, video, and audio.
Due to the way the Internet has revolutionized human communication, the amount of unstructured data available has risen considerably in recent years. By implementing a state-of-the-art data analysis solution, organizations of all shapes and sizes can increase operational efficiency, devise new strategies, and offer a better customer experience.
Structured data vs. unstructured data
Unstructured data contains large amounts of figures, statistics, and facts. However, it is disorganized and therefore incomprehensible to anyone besides artificial intelligence. In contrast, structured data obeys pre-defined data models designed for easy access, search, comparison, and extraction.
A third data category, known as semi-structured data, is also relevant to data analysis. This type of information may exist in storage systems such as e-mail servers and is already organized to a certain degree due to its native format. The bulk of the data within these data sets is still unorganized, but it still can be superficially reviewed.
Structured and unstructured data, together with semi-structured data, are examples of big data. This term is used to refer to various forms of information capable of offering valuable insights when collected and analyzed.
A social media page, for example, contains several topics, opinions, and other types of unstructured text data. This information by itself has value, as one may be able to learn how customers feel about a product, experience, or event. The problem is that manual data processing to convert a database of that size to quantifiable information would take more than a person’s lifetime.
When managing unstructured data, the best results are achieved using unstructured data analytics tools. The latest advancements in machine learning have allowed for the development of revolutionary analysis techniques like natural language processing, giving artificial intelligence the ability to understand the nuances in communication. This faculty can be used to conduct sentiment analysis and acquire valuable data on customer preferences.
Unstructured data types & examples
A company generates a large number of documents from a wide variety of sources. The amount of business intelligence data found in documents, both physical and in PDF format, can easily get out of hand if not properly managed. In addition, data may contain spreadsheets, images, XML files, or even hand-written information, which add an extra layer of difficulty to data management.
The number of resources and man-hours required to organize this information makes the adoption of data analysis solutions a very cost-effective proposition for companies. Business intelligence tools can be used to effortlessly streamline data management on big data and conventional data models.
According to Statista, there are more than 4 billion active email users, and the number is on the rise (source). Billions of emails are sent every day, and that translates into an incredibly large mass of unstructured data. While email servers contain semi-structured data, the vast majority of information found within emails is disorganized.
Social media content obeys a semi-structured model similar to email data. Information on social media sites like Instagram and Twitter is often cataloged using hashtags and other methods of rapid interconnection.
Social media data can be very convoluted but is nonetheless an effective and constantly updating archive of ideas, opinions, and other forms of valuable user data. Performing sentiment analysis is substantially facilitated by social media, as opinion mining tools can easily gather customer data analytics related to brands and products.
Multimedia information can be any image, audio, or video source that exists as a digital file. Within data storage, multimedia media files can be easily recognized due to their format types. It is easy to know that a file is an image file if it is a JPEG, but that doesn’t offer any information about the contents of the picture.
Analyzing customer data gives an organization a fully-balanced view of customers’ opinions. General customer sentiment is constantly changing and it is prone to change radically without notice. By keeping a close eye on the development of customers’ feelings, companies can deliver more accurate and appealing products and solutions.
Unstructured data sources include:
- Phone calls
- Social media content
All sites on the Internet, from Wikipedia to a brand’s website, contain unstructured data in several forms. Text, image, audio, and video data are common features of most websites, but the HTML code they’re written on doesn’t provide any analytic potential.
Information from websites can be mined, extracted, and reorganized to discover new data about the market, the competition, and the opinions of customers. Since the content of websites changes from time to time, machine learning algorithms can be set up to constantly monitor these content variations.
Open-ended customer surveys
While a survey with multiple-choice questions is easier to analyze, open-ended questionnaires can potentially offer much more helpful data. In the past, responders answering using their own terms were seen as a hindrance, but thanks to natural language processing proper analysis of it is now a possibility. Open-ended answers allow customers to offer more nuanced answers and even give useful recommendations.
Why is unstructured data important?
If unstructured data is ignored, it becomes a liability instead of an asset. Data storage space is limited and expensive, turning the storage of unstructured data into a waste of money. In contrast, if this data is taken advantage of, it has the potential to increase the efficiency of all operations.
How to analyze unstructured data
Unstructured data analysis solutions use a combination of machine learning algorithms and natural language processing techniques to train software to work for specific industries or purposes.
The first step for unstructured data analysis is to set quantifiable goals. The data found by analysis tools can be reorganized in many ways. It is very important to know what is being achieved in order not to complicate the process with irrelevant data.
For example, one can ask themselves:
- What particular insights need to be obtained?
- What kind of positive or negative feelings can exist towards a particular topic?
2. Gathering data
Data can come from a wide range of sources. Before conducting any form of analysis, it must be devised which channels of communication are the most suited to produce the kind of data needed. For example, trying to use current data to analyze processes that happened 10 years ago is going to produce very unreliable information, as is trying to get social media site information from a customer age group that doesn’t often frequent it.
3. Optimizing data
The data analysis process can be optimized by cleaning or pre-processing the gathered data first. Cleaning unstructured data makes it easier for machines to read. To pre-process data, one must reduce noise, remove irrelevant information, and slice it into manageable units.
4. Using data analysis tools
Processing data with an unstructured data analysis solution will create quantifiable data points. These are representations of any content existing within a data set which can be used to easily understand the meaning behind the data.
Once data has reached this stage, it can benefit researchers, entrepreneurs, managers, and anyone else who can derive use from it. To make crucial information easily understandable, the best data analysis software tools come with integrated data visualization tools. The purpose of these solutions is to supply the end-user with accurate and appealing data representations.
Unstructured data analysis challenges
Working with low-quality data
Unstructured data is bound to change, and it changes often. The technology being used to analyze unstructured data must be quick and efficient enough to guarantee that hundreds of volumes of data will be monitored for updates and changes on a persistent basis.
Likewise, the veracity of data from various sources may skew results. On social media sites like Facebook, some people tend to exaggerate, distort facts, or behave dishonestly, and this data might get picked up by an algorithm as if it was relevant. Misunderstanding information can lead to the wrong decisions being made.
Keeping data secure
Data is valuable not only to the company that owns it but also to any third party that can find a use for it. This includes malicious third parties looking to profit by compromising a company’s data. The information inside storage systems may include information about people’s identities, addresses, phone numbers, bank accounts, and other sensitive material.
Unoptimized data storage
If a company is working with proprietary data sets, then it must make sure to have its information properly organized across its departments. More often than not, a lack of communication between different systems ends up creating duplicate or irrelevant data that can harm the integrity of analysis results.
What is the best tool to analyze unstructured data?
The various benefits of exploiting unstructured data make data analysis software an indispensable asset for any organization. Thanks to artificial intelligence, data can be collected, extracted, stored, analyzes, reported, and used to optimize all business processes.
Semeon offers users a revolutionary text analytics system capable of performing previously complicated and time-consuming tasks on a whim. The advanced analysis systems of Semeon can be successfully deployed on various types of data sets and can be set up to produce accurate information summaries on demand.
Built using machine learning algorithms, the artificial intelligence that powers the Semeon platform is able to adapt to the data environment it is currently analyzing, providing more accurate customer analytics and business intelligence reports.