Definition of unstructured data
Every click, search, swipe, share, or media stream becomes data. This data may be structured, semi-structured, or unstructured.
Structured data neatly fits into tables, like a relational database model, and includes clear-cut information such as dates, credit card numbers, or geolocation coordinates.
Semi-structured data, while not residing in strict relational tables, has special labels or identifiers that make it organized enough to be searchable. This kind of data is common in certain computer file formats used by many websites, like JSON or XML.
Unstructured data is everything else — from emails and text to images and videos. Without a predefined structure or format, this type of data presents challenges for traditional database tools, making it tougher to unlock and utilize its full benefits.
Examples of unstructured data
-
Social Media: Posts, tweets, comments
-
Sensor Outputs: IoT device readings, environmental data
-
Logs: Server, website traffic, application usage
-
Communications: Emails, chats, meeting transcripts
-
Documents: Text files, business reports, PDFs
-
Multimedia: Audio (customer calls), video (surveillance, streams), images (photos, scans)
-
Customer Feedback: Reviews, survey responses, feedback forms
-
Collaborative Content: Shared whiteboards, project management tool data
-
Research Data: R&D project files, scientific datasets, simulations
-
Geospatial Data: Maps, location tracking data
-
Miscellaneous Data: 3D models, artwork, design files
How unstructured data is related to big data
Unstructured data is part of what we call big data, a term that encompasses the challenges and techniques related to the volume, velocity, and variety of data.
It is the unstructured variety of big data that poses the greatest challenge in terms of analysis and utilization. To harness the benefits of unstructured data, its processing and analysis require innovative approaches and technologies, such as artificial intelligence (AI).
Benefits of unstructured data
Leaders who know how to put AI to work on their unstructured data can unlock unprecedented opportunities. Those include:
-
Strategic Decision-Making:
-
Evaluating customers’ emotions expressed in social media and communication channels to refine business strategies
-
Predicting market trends by analyzing social media posts, commentary on forums, or customer support tickets to predict market demand
-
-
Customer-Centric Approaches:
-
Analyzing customer service transcripts and online behavior to reduce churn and offer tailored experiences and services
-
Utilizing sentiment analysis on user-generated content to directly inform customer relationship management
-
Understanding preferences expressed in customer surveys to increase upsell opportunities
-
-
Innovation & Development:
-
Directing R&D investment by leveraging insights from academic publications and patents
-
Speeding up product iteration cycles through real-time social media monitoring for immediate user feedback
-
Gleaning actionable feedback from online reviews and forums to inform product upgrades and innovation
-
-
Competitive Intelligence:
-
Dissecting industry reports and white papers to sharpen market positioning
-
Monitoring competitor news, updates, and customer reactions to maintain a competitive edge
-
Tracking startup activity and venture capital flows in industry-specific news feeds to detect emerging competitors
-
-
Risk & Compliance Assurance:
-
Automating the detection of non-compliance in unstructured financial narratives to pre-empt regulatory penalties
-
Analyzing legal documents and case law repositories for mitigating litigation risks
-
-
Operational Excellence:
-
Identifying inefficiencies through analysis of logs and unstructured workflow data to streamline internal operations
-
Examining unstructured financial data and expenditure reports to recognize cost-saving opportunities
-
Analyzing sensor data from logistics operations to improve supply chain management
-
Extracting insights from employee-generated content in corporate wikis and forums to better allocate resources
-
Harnessing natural language processing for interpreting error logs and maintenance reports to reduce machinery downtime
-
-
Marketing & Outreach:
-
Understanding language patterns in customer queries and online discussions to refine SEO strategies
-
Perform demographic and psychographic analysis of social media profiles and engagement data to optimize ad targeting
-
Mapping the customer journey through analysis of web navigation patterns and conversion funnels to amplify customer acquisition
-
Creating high-impact, data-driven campaigns by evaluating the success metrics from previous marketing content across various channels
-
-
Scientific & Medical Advancement:
-
Extracting data from unstructured clinical trial reports and patient records to enable AI-powered drug discovery
-
Aggregating and analyzing symptom-related discussions on health forums and social platforms to detect emerging public health trends
-
Extracting patterns and correlations from scientific papers, patient records, and forums to accelerate medical research
-
-
Cultural Understanding:
-
Exploring narrative changes in online media and blogosphere to gauge public opinion and detect societal and cultural shifts
-
Examining sentiment in community feedback and public forums to uncover public policy impacts
-
Using linguistic analysis on digital archives to track the evolution of language and cultural narratives
-
Data is getting bigger and demanding more investments
-
By 2025, the world is projected to create more than 180 zettabytes of data, up from nine zettabytes in 2013 (Statista)
-
Now, more than half of big companies deal with at least 5 petabytes of data (Komprise)
-
Out of all this data, about 80% is unstructured (Komprise)
-
Approximately 70% of businesses allocate over 30% of their IT budget to data storage and plan to increase this spending year over year (Komprise)
-
In 2022, 87.8% of companies stepped up investments in data, including data modernization, and 93.9% said they would spend even more in 2023
-
91.9% of businesses have extracted measurable value from their money put into data and analysis in 2023 (NewVantage Partners)
Outplaying the Matrix with AI — ITRex success stories
Remember the scene from The Matrix when Morpheus beat Neo? “Do you think that’s the air you’re breathing?” he asked his student.
That wasn’t.
What they were immersed in was a sea of formless data, and Morpheus knew how to navigate it to be “faster and stronger.”
Outside of the Matrix, we, too, wade through chaotic streams of data, often without direction.
Yet, a key exists that can decipher this chaos and reveal the hidden patterns and insights. This key is Artificial Intelligence (AI).
AI starts by collecting unstructured data from as many sources as needed. It prepares this data for analysis, sorting and cleaning it to remove irrelevant details.
With the stage set, AI embarks on the crucial task of pattern recognition. Here, ML algorithms come into play, identifying patterns and anomalies within this data without needing it to be labeled first.
Yet, AI’s capabilities are not limited to ML alone.
Natural language processing (NLP) allows AI to understand human language by picking out key themes, opinions, and emotions from text. Computer vision empowers AI to interpret images and videos, identifying objects and faces within visual data. For audio, AI uses speech recognition technologies to convert spoken words into text and mine them for valuable insights.
And then comes the new frontier: generative AI.
Distinct from its analytical counterparts, generative AIis not content with merely understanding data. It creates. This form of AI uses raw digital content to forge new data, learning from the vast complexity of unstructured information to produce novel outputs.
Stepping into this evolving landscape, ITRex has been pioneering the development of advanced AI tools for over a decade, helping clients to not just cope with but also capitalize on their data deluges. Among our innovative solutions are:
-
A comprehensive data analytics platform for the world’s top retailer, enabling a 360-degree view of all data sources and data across the organization for better decision-making (case study)
-
A strategic decision support system for a logistics operator, optimizing global shipment management for enhanced cost efficiency (case study)
-
A real-time surveillance anomaly detection system for amusement arcade chains, designed to pinpoint and address irregular gamer behavior (case study)
-
ML-driven PDF editing tool for a top SaaS provider, streamlining document management with intelligent automation (case study)
-
A decision-support platform for cancer treatment, leveraging a decade of patient-reported outcomes to guide therapeutic choices (case study)
-
A predictive analytics app for anticipating football game strategies using data from sensors inserted into players’ shoulder pads (case study)
-
A content decoding app for a media startup to detect character traits in content and audiences for reaching out with emotionally intelligent (case study)
Wrapping up
With technologies advancing at an unprecedented pace in a highly competitive environment, data will be for many the key to success in the digital era.
Are you disrupting the competition or among those disrupted?
