AI and Unstructured Data: Transform Chaos into Clarity

Making sense of unstructured data can prove to be valuable, especially because every business thrives on internal information in one way or another. However, considering 80-90% of all new enterprise data is unstructured [1], this sheer volume can often feel overwhelming for a data scientist using a traditional data management/analysis tool. It’s pretty obvious that digging for it can be labor-intensive. To address these challenges, integrating AI into business practices is becoming increasingly important.

To overcome this problem, AI is making major strides, turning unstructured data and molding it into useful information. Let’s learn more about it.

What Is Unstructured Data?

To put it simply, unstructured data is not organized in any particular order or manner, lacks a predefined data model or schema, and comes in a wide variety of formats. Considering these factors, analyzing and extracting information from unstructured data is particularly difficult. Real-world examples of unstructured data include emails, photos, videos, audio, customer reviews, and social media posts.

To understand it better, think of a random, big box under your bed that’s been collecting dust all year round, which contains photographs, diaries, letters, and CDs. It may be a mess, and it’s going to take a lot of time and effort and potentially additional equipment (like gloves and a face mask) to sort it out. But inside of it, there might be information that you would dearly want to retain. That’s essentially what unstructured data is.

Since it’s important to be informed about both sides of a coin, here’s the difference between structured and unstructured data:

With that said, there potentially lies a treasure past the stormy seas of unstructured data. When analyzed currently, it’s capable of providing valuable insights into customer behavior, sentiment, and market trends.

The Role of AI in Managing Unstructured Data

Since unstructured data comes in no specific format nor can it be effectively analyzed using traditional data analysis tools, it’s a wise decision to use artificial intelligence to get clarity on it. AI can transform unstructured data into a structured, organized, and valuable resource.

It’s a broad concept that has quite a few technologies to process data, including:

• Natural Language Processing (NLP): A subset of AI that grants it the ability to understand, interpret, and generate human language, the way we speak it. This is inclusive of tone, formal and casual. NLP’s algorithm looks for key information, relationships, patterns, trends, and sentiment within an unstructured database.
• Machine Learning (ML): This process is what allows the AI to automatically categorize, tag, and classify content from large volumes of unstructured data and find trends and patterns within it. ML also improves over time, increasing its accuracy and efficiency in handling data.
• Deep Learning: A subset of ML that, by utilizing artificial neural networks, mimics the human brain's ability to learn and recognize patterns in complex data, such as images and videos. This allows AI to understand various forms of unstructured data.
• Data repository: The core component of an AI knowledge base is the fact that it becomes your organization’s repository for data. It contains all the information about your company that you ingest it with, be it data regarding finances, human resources, product details, customer support logs, project details, and marketing strategy reports.

Our AI tool, Atlas UP, is a great example of this. Once configured, it integrates your internal systems across HR, Finance, Sales, and Project platforms to answer any company-related question within seconds. It sorts through the unstructured data and gives you valuable insights, showing how enterprise information management solutions can be optimized with AI.

Benefits of Using AI for Unstructured Data Management

AI has the capability to process massive volumes of data, ranging from gigabytes to petabytes, extremely quickly, and this range is only increasing as the technology advances. Here are some more benefits:

• Search and Discovery: AI tools like Atlas UP are trained in NLP and ML - they allow queries in natural language and locate/retrieve the information in no time. It eliminates the need for manual searching and sifting through countless documents.
• Content Enrichment: Since there’s no manual sorting and tagging, the data is better organized, significantly reducing time and effort. From there, it analyzes the data further to add context and understand the meaning, relationships between them, and sentiments behind all the information.
• Insights: It uncovers patterns, trends, and correlations that may not be obvious to the human eye in the mess of unstructured data. This insight can potentially provide valuable insights into customer behavior, market trends, and operational performance, reinforcing the value of AI's role in enhancing business intelligence.

Use Cases of AI in Unstructured Data

Here are some practical use cases of AI in unstructured data:

1. Customer Support and Self-Service

Scenario: A company wants to improve its customer support services by utilizing unstructured data from customer interactions.

The AI can be tasked to analyze the sentiments behind emails, social media posts, online reviews as well as customer interaction transcripts. This helps identify common issues and areas for improvement, enhancing the overall customer experience​. For example, if a customer sends an email asking about a specific product feature, the AI can quickly retrieve relevant information from past interactions and provide an accurate answer.

2. Healthcare and Medical Research

Scenario: A healthcare provider aims to improve patient care and medical research by analyzing unstructured clinical data.

Electronic Health Records include doctor’s notes and patient history. AI can be used to extract valuable insights from it to improve diagnosis accuracy and treatment plans​. It can also be used to extract vast amounts of unstructured medical literature to keep healthcare professionals up-to-date with the latest research.

Traditional data management software struggles to handle visual data. AI, however, excels here. It’s capable of identifying and categorizing objects within images or videos. For example, it can be used in medical imaging diagnostics to detect anomalies in medical images such as MRIs, CT scans, and X-rays.

3. Human Resources and Talent Management

Scenario: A corporation is looking to improve its hiring process and talent management.

With its ability to go through data, AI can be used to read unstructured resumes, organize them based on defined criteria like skills, experiences, and qualifications, show only the relevant results, and keep track of applications and their status. Feedback forms, emails, and internal social media can also be looked into for sentimental analysis of your overall employee satisfaction.

Atlas UP works wonders in this area. Being a central knowledge base of your company, it will gather all the relevant information about your employees, for example, the number of employees currently on payroll.

Some other use cases of AI in unstructured data include:

• Summarization: It’s capable of condensing large documents, meeting transcripts, or reports into a consumable summary.
• Customer Support Automation: It can analyze texts like customer chat logs, emails, FAQs, and reviews to create answers to common queries, reducing the overall workload and improving response times - Creating a win-win situation.
• Sentiment Analysis: Using social media posts, customer emails, forum discussions, product reviews, and survey responses, AI algorithms identify emotions (positive, negative, or neutral) within the text. This gives you a better understanding of public perception, customer satisfaction, and market trends.

How Atlas UP Can Help Make Sense of Unstructured Data

Atlas UP is our AI-powered knowledge base software that can essentially become the personal respondent of your company for every employee. Given how the majority of a company's data is unstructured, the ability to extract valuable insights from it can be incredibly useful, so let Atlas UP play a part in it.

Once you configure it, it’ll become the brain of all the company information you provide, as well as those that are publicly available. The same information is then used to answer your queries within seconds.

This instant access to any question across departments will help you build not only a culture of inquiry and curiosity but also increase the ability to make careful decisions without dependency. And let’s not forget about the total amount of time your company will save (+ the impact it’ll have on your overall workforce.)

Moreover, when you use Atlas UP, all of your data is tightly protected with cutting-edge encryption. We follow strict SOC 2 Type II and GDPR compliance, which means, your data stays yours. We never use it for model training or marketing without your permission.

Book a demo with us today to infuse AI into your unstructured data for clarity, convenience, and great outcomes.

Closing Thoughts

The ability of AI to process, analyze, and make sense of unstructured data to extract valuable insights is what makes it a must-use, especially when traditional methods and manual analysis turn out to be inefficient.

AI only seems to be getting better, and considering the fact that it’s being used in most companies, it’s almost necessary to have it in your arsenal to stay competitive. To narrow down your search, we recommend Atlas UP for better analysis, management, and extraction of your unstructured data.

Frequently Asked Questions

Can AI work on unstructured data?

Absolutely! Traditional methods of data analysis might not be very effective at the task, but AI, by leveraging natural language processing and machine learning, takes the cake. NLP and ML are technologies designed to process and analyze unstructured data types, classify information, and convert it into structured formats for further analysis.

What is an example of unstructured data in AI?

Unstructured data examples include emails, product reviews, chats, and social media posts. They come in various formats, like texts, images, audio, and video - all of which can be handled by AI.

What is structured vs unstructured data in AI?

Information that is organized in a predefined manner, typically in rows and columns within databases, is easily searchable and analyzable due to its consistent format is called structured data. And for what is unstructured data in AI, it’s the complete opposite of it - it lacks a specific format or organization.

How can AI unlock the power of unstructured data?

Since unstructured data is not in a consistent format, it cannot be analyzed efficiently by traditional data management software. And while your employees can do it manually too, it would take a lot of time. AI can effectively perform this task by transforming it into useful and actionable insights, all within seconds.