Mr. Gaurav is a cybersecurity engineer, developer, researcher, and Book-Author who did his B.S.-Cybersecurity from EC-Council University & Masters from LPU. He is an India Book of Record holder, Guest speaker with 7+ years of experience in IT.
For enquiries call:
+1-469-442-0620
- Home
- Blog
- Data Science
- Role of Unstructured Data in Data Science
HomeBlogData ScienceRole of Unstructured Data in Data Science
Share
Role of Unstructured Data in Data Science
Published
05th Sep, 2023
Views
Read TimeRead it in
11 Mins
In this article
Data has become the new game changer for businesses. Typically, data scientists categorize data into three broad divisions - structured, semi-structured, and unstructured data. In this article, you will get to know about types of big data, unstructured data, sources of unstructured data, unstructured data vs. structured data, the use of structured and unstructured data in machine learning, and the difference between structured and unstructured data. Let us first understand what is unstructured data with examples.
What is unstructured data?
Unstructured data is a kind of data format where there is no organized form or type of data. Videos, texts, images, document files, audio materials, email contents and more are considered to be unstructured data. It is the most copious form of business data, and cannot be stored in a structured database or relational database. Some examples of unstructured data are the photos we post on social media platforms, the tagging we do, the multimedia files we upload, and the documents we share. Seagate predicts that the global data-sphere will expand to 163 zettabytes by 2025, where most of the data will be in the unstructured format. For more information on Data Science, check out the Data Science course best training.
In addition, you can read more about the measures of dispersion here.
Characteristics of Unstructured Data
Unstructured data cannot be organized in a predefined fashion, and is not a homogenous data model. This makes it difficult to manage. Apart from that, these are the other characteristics of unstructured data.
- You cannot store unstructured data in the form of rows and columns as we do in a database table.
- Unstructured data is heterogeneous in structure and does not have any specific data model.
- The creation of such data does not follow any semantics or habits.
- Due to the lack of any particular sequence or format, it is difficult to manage.
- Such data does not have an identifiable structure.
Sources of Unstructured Data
There are various sources of unstructured data. Some of them are:
- Content websites
- Social networking sites
- Online images
- Memos
- Reports and research papers
- Documents, spreadsheets, and presentations
- Audio mining, chatbots
- Surveys
- Feedback systems
Advantages of Unstructured Data
Unstructured data has become exceptionally easy to store because of MongoDB, Cassandra, or even using JSON. Modern NoSQL databases and software allows data engineers to collect and extract data from various sources. There are numerous benefits that enterprises and businesses can gain from unstructured data. These are:
- With the advent of unstructured data, we can store data that lacks a proper format or structure.
- There is no fixed schema or data structure for storing such data, which gives flexibility in storing data of different genres.
- Unstructured data is much more portable by nature.
- Unstructured data is scalable and flexible to store.
- Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous properties of unstructured data.
- Different applications and platforms produce unstructured data that becomes useful in business intelligence, unstructured data analytics, and various other fields.
- Unstructured data analysis allows finding comprehensive data stories from data like email contents, website information, social media posts, mobile data, cache files and more.
- Unstructured data, along with data analytics, helps companies improve customer experience.
- Detection of the taste of consumers and their choices becomes easy because of unstructured data analysis.
Disadvantages of Unstructured data
- Storing and managing unstructured data is difficult because there is no proper structure or schema.
- Data indexing is also a substantial challenge and hence becomes unclear due to its disorganized nature.
- Search results from an unstructured dataset are also not accurate because it does not have predefined attributes.
- Data security is also a challenge due to the heterogeneous form of data.
Problems faced and solutions for storing unstructured data.
Until recently, it was challenging to store, evaluate, and manage unstructured data. But with the advent of modern data analysis tools, algorithms, CAS (content addressable storage system), and big data technologies, storage and evaluation became easy. Let us first take a look at the various challenges used for storing unstructured data.
- Storing unstructured data requires a large amount of space.
- Indexing of unstructured data is a hectic task.
- Database operations such as deleting and updating become difficult because of the disorganized nature of the data.
- Storing and managing video, audio, image file, emails, social media data is also challenging.
- Unstructured data increases the storage cost.
For solving such issues, there are some particular approaches. These are:
- CAS system helps in storing unstructured data efficiently.
- We can preserve unstructured data in XML format.
- Developers can store unstructured data in an RDBMS system supporting BLOB.
- We can convert unstructured data into flexible formats so that evaluating and storage becomes easy.
Let us now understand the differences between unstructured data vs. structured data.
Unstructured Data Vs. Structured Data
In this section, we will understand the difference between structured and unstructured data with examples.
| STRUCTURED | UNSTRUCTURED |
|---|---|
| Structured data resides in an organized format in a typical database. | Unstructured data cannot reside in an organized format, and hence we cannot store it in a typical database. |
| We can store structured data in SQL database tables having rows and columns. | Storing and managing unstructured data requires specialized databases, along with a variety of business intelligence and analytics applications. |
| It is tough to scale a database schema. | It is highly scalable. |
| Structured data gets generated in colleges, universities, banks, companies where people have to deal with names, date of birth, salary, marks and so on. | We generate or find unstructured data in social media platforms, emails, analyzed data for business intelligence, call centers, chatbots and so on. |
| Queries in structured data allow complex joining. | Unstructured data allows only textual queries. |
| The schema of a structured dataset is less flexible and dependent. | An unstructured dataset is flexible but does not have any particular schema. |
| It has various concurrency techniques. | It has no concurrency techniques. |
| We can use SQL, MySQL, SQLite, Oracle DB, Teradata to store structured data. | We can use NoSQL (Not Only SQL) to store unstructured data. |
Types of Unstructured Data
Do you have any idea just how much of unstructured data we produce and from what sources? Unstructured data includes all those forms of data that we cannot actively manage in an RDBMS system that is a transactional system. We can store structured data in the form of records. But this is not the case with unstructured data. Before the advent of object-based storage, most of the unstructured data was stored in file-based systems. Here are some of the types of unstructured data.
- Rich media content: Entertainment files, surveillance data, multimedia email attachments, geospatial data, audio files (call center and other recorded audio), weather reports (graphical), etc., comes under this genre.
- Document data: Invoices, text-file records, email contents, productivity applications, etc., are included under this genre.
- Internet of Things (IoT) data: Ticker data, sensor data, data from other IoT devices come under this genre.
Apart from all these, data from business intelligence and analysis, machine learning datasets, and artificial intelligence data training datasets are also a separate genre of unstructured data. Enroll in the KnowledgeHut Data Science course best training to kick-start your profession.
Examples of Unstructured Data
There are various sources from where we can obtain unstructured data. The prominent use of this data is in unstructured data analytics. Let us now understand what are some examples of unstructured data and their sources –
- Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data could be in the form of patient-doctor or patient-nurse conversations, which are usually recorded in audio or text formats. Unstructured data generated by machines includes emergency video camera footage, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes and more.
- Social Media is an intrinsic entity of our daily life. Billions of people come together to join channels, share different thoughts, and exchange information with their loved ones. They create and share such data over social media platforms in the form of images, video clips, audio messages, tagging people (this helps companies to map relations between two or more people), entertainment data, educational data, geolocations, texts, etc. Other spectra of data generated from social media platforms are behavior patterns, perceptions, influencers, trends, news, and events.
- Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedback and much more. These documents help to create knowledge repositories within an organization to make better implicit operations.
- Live chat, video conferencing, web meeting, chatbot-customer messages, surveillance data are other prominent examples of unstructured data that companies can cultivate to get more insights into the details of a person.
Some prominent examples of unstructured data used in enterprises and organizations are:
- Reports and documents, like Word files or PDF files
- Multimedia files, such as audio, images, designed texts, themes, and videos
- System logs
- Medical images
- Flat files
- Scanned documents (which are images that hold numbers and text – for example, OCR)
- Biometric data
Unstructured Data Analytics Tools
You might be wondering what tools can come into use to gather and analyze information that does not have a predefined structure or model. Various tools and programming languages use structured and unstructured data for machine learning and data analysis. These are:
- Tableau
- MonkeyLearn
- Apache Spark
- SAS
- Python
- MS. Excel
- RapidMiner
- KNIME
- QlikView
- Python programming
- R programming
- Many cloud services (like Amazon AWS, Microsoft Azure, IBM Cloud, Google Cloud) also offer unstructured data analysis solutions bundled with their services.
How to analyze unstructured data?
In the past, the process of storage and analysis of unstructured data was not well defined. Enterprises used to carry out this kind of analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis methods became highly advanced. AI-powered tools use algorithms designed precisely to help to break down unstructured data for analysis. Unstructured data analytics tools, along with Natural language processing (NLP) and machine learning algorithms, help advanced software tools analyze and extract analytical data from the unstructured datasets.
Before using these tools for analyzing unstructured data, you must properly go through a few steps and keep these points in mind.
- Set a clear goal for analyzing the data: It is essential to clear your intention about what insights you want to extract from your unstructured data. Knowing this will help you distinguish what type of data you are planning to accumulate.
- Collect relevant data: Unstructured data is available everywhere, whether it's a social media platform, online feedback or reviews, or a survey form. Depending on the previous point, that is your goal - you have to be precise about what data you want to collect in real-time. Also, keep in mind whether your collected details are relevant or not.
- Clean your data: Data cleaning or data cleansing is a significant process to detect corrupt or irrelevant data from the dataset, followed by modifying or deleting the coarse and sloppy data. This phase is also known as the data-preprocessing phase, where you have to reduce the noise, carry out data slicing for meaningful representation, and remove unnecessary data.
- Use Technology and tools: Once you perform the data cleaning, it is time to utilize unstructured data analysis tools to prepare and cultivate the insights from your data. Technologies used for unstructured data storage (NoSQL) can help in managing your flow of data. Other tools and programming libraries like Tableau, Matplotlib, Pandas, and Google Data Studio allows us to extract and visualize unstructured data. Data can be visualized and presented in the form of compelling graphs, plots, and charts.
How to Extract information from Unstructured Data?
With the growth in digitization during the information era, repetitious transactions in data cause data flooding. The exponential accretion in the speed of digital data creation has brought a whole new domain of understanding user interaction with the online world. According to Gartner, 80% of the data created by an organization or its application is unstructured. While extracting exact information through appropriate analysis of organized data is not yet possible, even obtaining a decent sense of this unstructured data is quite tough.
Until now, there are no perfect tools to analyze unstructured data. But algorithms and tools designed using machine learning, Natural language processing, Deep learning, and Graph Analysis (a mathematical method for estimating graph structures) help us to get the upper hand in extracting information from unstructured data. Other neural network models like modern linguistic models follow unsupervised learning techniques to gain a good 'knowledge' about the unstructured dataset before going into a specific supervised learning step. AI-based algorithms and technologies are capable enough to extract keywords, locations, phone numbers, analyze image meaning (through digital image processing). We can then understand what to evaluate and identify information that is essential to your business.
Conclusion
Unstructured data is found abundantly from sources like documents, records, emails, social media posts, feedbacks, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and can be very boring at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insights from unstructured information.
According to some research, data analytics tools like MonkeyLearn Studio, Tableau, RapidMiner help analyze unstructured data 1200x faster than the manual approach. Analyzing such data will help you learn more about your customers as well as competitors. Text analysis software, along with machine learning models, will help you dig deep into such datasets and make you gain an in-depth understanding of the overall scenario with fine-grained analyses.
Upcoming Data Science Batches & Dates
| Name | Date | Fee | Know more |
|---|
Course Advisor
Hello! Ready to elevate your career? Explore 500+ future-ready courses with KnowledgeHut upGrad 🚀x
1
Fast forward your career with 500+ future-ready courses ⏩💻👩🎓📚x
1
