Role of Unstructured Data in Data Science

Introduction

Data has become the new oil and market for businesses. Data scientists categorize data into three broad divisions — structured, semi-structured, and unstructured data. In this article, you will get to know about 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.Let us first understand what are 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. Therefore, it requires different mechanisms to operate. We can consider the video, text, images, document files, audio material, email contents, etc., under unstructured data. It is the most copious form of business data, and we cannot store such data in a structured database or relational database.

So, you might wonder what unstructured data examples are? The photos we post on social media platforms, the tagging we do, the multimedia files we upload, the documents we share are examples of unstructured data. Seagate predicts that the global data-sphere will expand to 163 zetta bytes by 2025, where most of the data will be unstructured.

Characteristics of Unstructured Data

Unstructured data do not remain organized in a predefined fashion, as well as, they do not remain in a homogeneous data model and thus become difficult to manage. Apart from that, there are various other characteristics of unstructured data.

· You cannot store unstructured data in the form of rows and columns as we do in a table’s database.

· The data in unstructured data are heterogeneous in structure and do not have any specific data model.

· The creation of such data does not follow any semantic or habits.

· Due to the lack of any particular sequence or format, they are obstinate to manage.

· Such data do 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 allowed data engineers to collect and extract data from various sources. There are numerous benefits 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 are much more portable by nature.

· Unstructured data are scalable and flexible to store.

· Database systems like MongoDB, Cassandra, etc., can easily handle the heterogeneous property 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 content, website information, social media posts, mobile data, cache files, etc.

· Unstructured data, along with data analytics, helps companies improve customer experience.

· Detection of the taste of consumers and their choices become easy because of unstructured data analysis.

Disadvantages of Unstructured data

· Storing and managing unstructured data is difficult because they don’t have a 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, 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

· 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

· Content addressable storage (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 unstructured data vs. structured data.

Unstructured Data Vs. Structured Data

In this section, we will understand the difference between structured and unstructured with examples.

Types of Unstructured Data

Do you have any idea the quantity of unstructured data we produce and from what source?Unstructured data are 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 the case is not the same with unstructured data. Before the advent of object-based storage, most of the unstructured data gets stored in file-based systems. Here are some of the types of unstructured data.

· Rich media contents: 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., comes 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, artificial intelligence data training datasets are also a separate genre of unstructured data.

Examples of Unstructured Data

There are various sources from where we can obtain unstructured data. The prominent use of these data is in unstructured data analytics. Let us now understand what are unstructured data examples and their sources –

· Healthcare industries generate a massive volume of human as well as machine-generated unstructured data. Human-generated unstructured data are like patient-doctor or patient-nurse conversations usually recorded in audio or text formats. Unstructured data generated by machines include emergency video camera footages, surgical robots, data accumulated from medical imaging devices like endoscopes, laparoscopes, etc.

· Social Media is an intrinsic entity of our daily life. Billions of people come around, join channels, share different thoughts, and exchange information and their life with their beloved ones. Creating and sharing such data over social media platforms contains images, video clips, audio messages, tagging people (help companies 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, perceptions, influencers, trends, news, events, etc.

· Business and corporate documents generate a multitude of unstructured data such as emails, presentations, reports containing texts, images, presentation reports, video contents, feedbacks. These documents help 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 insight 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 video

· 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, etc.) also offer unstructured data analysis solutions bundled with their services.

How to analyze unstructured data?

In the past, it was unclear and trifling to store and analyze unstructured data. Enterprises used to do most of such analysis manually. But with the advent of modern tools and programming languages, most of the unstructured data analysis became highly advanced. AI-powered tools use algorithms designed precisely 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 dataset.

Before using the 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 insight 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, 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 insight 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. Visualizing the data will make them speak for themself through compelling graphs, plots, and charts.

How to Extract information from Unstructured Data?

With the growth in digitization and of the information era, repetitious transactions of 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. Though, it’d be terrifying if we can extract exact information through appropriate analysis of organized data. But it is a tough job to obtain a decent sense of these unstructured data.

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 get an upper-hand to extract 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). It can then understand what to evaluate and identify information that is essential to your business.

Conclusion

Unstructured data are heavy data found abundantly from sources like documents, records, emails, social media posts, feedback, call-records, log-in session data, video, audio, and images. Manually analyzing unstructured data is very time-consuming and full of boredom at the same time. With the growth of data science and machine learning algorithms and models, it has become easy to gather and analyze insight 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 witness the overall scenario with fine-grained analyses.