The global data sphere is expanding at a crazy rate. Tons and tons of data types are being generated every second. However, not all data is created equal. We have both types: one is organized and structured neatly into tables and databases, while on the other side, we have this rebel, unstructured data. Estimations say that around 80% of the data out there is unstructured.

Let’s find out how to deal with unstructured data using Artificial Intelligence. But first, have a look at the unstructured data rundown.

What is Unstructured Data?

Unstructured data is wild — it doesn’t fit into traditional rows and columns. Things like text documents, images, videos, social media posts, and even GIFs are unstructured data. All this data is messy and untamed. For this reason, it’s harder to analyze and make the most out of it.

Despite being messy, Unstructured data holds a treasure trove of insights. It could help businesses understand customer sentiments, detect patterns, make predictions, and more if structured properly.

With the right tools and technologies, unstructured data can make sense. For instance, Natural language processing, machine learning, and AI algorithms can sift through it and extract meaning from unstructured data to help you make informed decisions.

Structured Data vs Unstructured Data: What’s the Difference?

Data exists in many forms and sizes but mainly into two types: Structured and unstructured.

Structuring the Data: Why Does it Matter?

Businesses, daily, deal with huge amounts of unstructured data from various sources like customer feedback, reviews, social media comments, in-app reviews, and so on. Gartner estimates that up to 90% of enterprise data is unstructured or semi-structured, and most of it isn’t put to good use.

Moreover, Unstructured data is wider than these feedbacks, numerical values, and facts. In fact, your emotions, opinions, and even thoughts are considered unstructured data.

This data is a goldmine of insights that can impact a business strategy and decision-making. Also, it can make processes better and cut costs. But managing unstructured data can be challenging. It’s time-consuming, expensive, and prone to human bias as well. Plus, it doesn’t offer scalability.

Turning unstructured data into a quantifiable form is quite challenging. It needs more advanced tools, complex rules, and techniques whatsoever.

What Are The Challenges of Managing Unstructured Data?

Managing unstructured data comes with its fair share of challenges. Here are some major challenges you might face.

• Overwhelming Volume of Data

Unstructured data is exploding at a remarkable rate. According to estimates, the world has created a staggering 175 zettabytes of data up till 2023. That equals 175 trillion gigabytes or 175 million terabytes.

Social media platforms, multimedia content forms, and IoT devices have massively created a sheer volume of unstructured data. Storing, processing, and reviewing such massive amounts of data needs a robust infrastructure and scalable solutions.

• Tangled Web

Since unstructured data lacks a predefined structure, it’s hard to organize and categorize it effectively. Traditional data processing will not be enough. Also, finding out specific data points within such a massive, tangled web is like searching for a needle in a haystack.

• Integration Issues

Unstructured data often exist side by side with structured data. Integrating and aligning that unstructured data with existing systems and processes is an uphill task. Careful planning and efficient data mapping must be done to ensure seamless integration and a unified data view.

• Data Cleansing

One of the major challenges of managing unstructured data is to cleanse and validate it. Almost 60% of a Data Scientist’s effort goes into cleansing the data.

The amount of data you create and deal with daily contains inconsistencies, errors, and noise. It makes the data cleansing process a daunting task. Also, extracting meaningful insights within such data becomes more challenging. So maintaining data quality and overall accuracy takes a lot of work.

• Requires Computational Resources

Unstructured data like images and text require deep analysis and interpretation to yield insights. Image recognition, natural language processing, and machine learning algorithms play a key role in processing and interpreting data. You need high expertise and computational resources to implement such techniques and algorithms to make the most out of unstructured data.

• Data Governance and Compliance

Since unstructured data is diverse and falls under the data governance and compliance regulation category, ensuring its adherence to data protection laws, industry standards, and internal policies needs a lot of time.

Unstructured Data Made Easy With Artificial Intelligence 

Unstructured data management is the process of collecting, storing, organizing, and analyzing data with no predefined structure. However, leveraging Artificial Intelligence is the only way to manage such messy, untamed data. The key is to get started.

AI technologies can unlock the potential of unstructured data — Using AI-powered tools, businesses can process, mine, integrate, store, track, index, and report business insights from raw and unstructured data.

Here’s how AI can help in structuring unstructured Data:

• Automates Data Extraction with NLP 

Whether you want to extract key details from text documents, transcribe audio recordings, or recognize objects in images, AI algorithms can process unstructured data at scale, saving time and effort.

AI-powered techniques such as NLP make computers understand, analyze, and extract meaning from human language. NER (Named Entity Recognition) helps you identify and classify unstructured data such as names, locations, dates, etc. This classification further helps you in analysis and processing.

What’s more, NLP algorithms can automatically generate concise summaries of lengthy text documents. Instead of going through a full-fledged document, you can have a quick overview of the content.

You can also translate text data into multiple languages with NLP. Machine translation models powered by NLP help organizations accurately translate the data enabling them to handle multilingual unstructured data effectively.

• Text and Sentiment Analysis

What AI algorithms do best is analyze and understand the textual data. Sentiment Analysis, topic modeling, and NER help you derive meaning and context from the unstructured data format. Businesses can get valuable insights into customer sentiments, find out what trends, and make data-driven decisions with unstructured textual data.

More than this, analyzing unstructured data lets companies monitor their brand reputation in real time. By tracking sentiment trends, and finding out negative or positive brand mentions, you can manage the potential issues, make informed decisions and optimize customer satisfaction while staying ahead of their competitors.

• Analyzes Images and Videos with Computer Vision 

Computer vision enables computers to see and understand the world just like us.

This branch of AI can automatically analyze and annotate images and videos. It helps you organize, categorize, and retrieve unstructured visual data smoothly.

In addition to this, computer vision allows for visual search. By comparing visual content and identifying similarities, you can navigate through huge amounts of unstructured data while searching for similar or related images with it.

Take Your Visual Data to the Next Level and Capture valuable insights from it with RevolveAI’s Computer Vision solutions. 

Also, the exponential growth of user-generated content lets computer vision algorithms assist in content moderation tasks and can identify anomalies within visual data as well.

• Optical Character Recognition (OCR) 

OCR manages the unstructured text data in the best way. It converts printed or handwritten text into machine-readable and searchable text format.

OCR digitizes unstructured documents, e.g., invoices, receipts, forms, contracts, and handwritten notes, making processing, analyzing, and storing the extracted information easier. It makes information retrieval so convenient and saves time and effort that would otherwise be spent manually scanning through documents to find relevant data.

• Detects Anomalies 

Unstructured data contains anomalies and potential fraudulent instances which aren’t easy to detect. AI-powered tools can identify anomalies by establishing baseline patterns and detecting deviations. First, AI analyzes historical data and training models to identify normal behavior, then flag unusual patterns that may indicate fraud, network intrusions, or other abnormal patterns.

• Content Classification and Tagging 

Classifying the unstructured content helps you put the data into different, predefined categories based on features, topics, or themes.

AI Algorithms can automate the content classification process and create custom tags. These algorithms learn patterns, relationships, and characteristics of different content types, which enables them to classify content accurately while putting manual tagging to an end.

Unstructured data create chaos that’s impossible to manage without Artificial Intelligence. AI’s ability to process, analyze and extract insights from huge amounts of unstructured data is beyond amazing. It tames the chaos, extracts valuable information, and empowers businesses to make data-driven decisions.

Top 10 Unstructured Data Management Tools For Businesses

Many structured and unstructured data is being generated continuously by businesses, consumers, and mobile apps. Every day, people create about 2.5 quintillion bytes of data, according to a study by IBM.

In the right hands, data can be an invaluable resource. Businesses and individuals require the ability or skills to make full use of their data, however. This is why data management tools are created.

From providing efficient storage mechanisms to offering real-time monitoring and advanced analytics for the growing unstructured data, these tools lend you a helping hand to tackle all the challenges associated with both types of data.

With so many tools in the market, below is a list of the top 10 Data Management Tools that help you get value from your data.

Winding up

Unstructured data plays a crucial role in Big Data Analytics, representing a rich untapped vein of insights that can drive strategic decision-making and business success. Therefore, how to deal with unstructured data effectively will be a crucial factor for businesses in this madly data-driven world.