The world generates an astounding 2.5 quintillion bytes of data a day, up to 90% of which is unstructured, meaning you can’t easily store it in a traditional database or spreadsheet.
This creates a significant challenge for any organisation that relies on data (which is most): how to keep up with the ever-increasing amount of it without further overloading already-overloaded systems. AI—and being able to support AI with unstructured data management—plays a very important role in all of this.
Read on for the importance, impact, challenges, and trends of unstructured data.
What Is Unstructured Data?
Unstructured data lacks a predefined format or model, such as texts, images, and videos, making it complex to organize and analyse.
Unlike structured data, which fits neatly into databases and spreadsheets with clearly defined fields and categories, unstructured data lacks a specific organizational structure. It can include text documents, emails, social media posts, images, videos, audio recordings, and more.
Unstructured data is what we would call modern data, meaning it’s always flowing, geo-distributed, multimodal, and unpredictable.
Unstructured data is typically stored in its raw form as an object, as opposed to a file.
Benefits of Unstructured Data Analysis
Apart from now being the most common data type by far, as previously mentioned, being able to rapidly process and analyse unstructured data is important for several reasons:
Insights
Unstructured data contains valuable insights and hidden patterns that can drive business decisions, inform strategies, and improve operational efficiency. Extracting meaningful information from unstructured data helps uncover trends, identify customer preferences, and pinpoint emerging market opportunities.
Competitive Advantage
Companies that effectively leverage unstructured data unquestionably gain a competitive edge. That’s because by analysing customer sentiments, product reviews, and social media interactions, they can better understand their target audience and tailor their products and services to meet their needs.
Innovation
Unstructured data fuels innovation by providing the raw material for developing advanced technologies such as natural language processing (NLP), image recognition, and machine learning algorithms. These technologies enable automated analysis and interpretation of unstructured data at scale, unlocking new possibilities in various fields, including healthcare, finance, and marketing.
Real-world Use of Unstructured Data
Unstructured data significantly impacts businesses across various industries and sectors in several ways. Let’s look at its impact on a few sectors.
Healthcare
Unstructured data like medical records, clinical notes, and imaging results are extremely important in healthcare. Analysing this data can lead to improved patient outcomes, more accurate diagnoses, and personalized treatment plans. For example, healthcare providers use natural language processing (NLP) to extract insights from unstructured clinical notes to identify trends, predict disease progression, and enhance patient care.
Finance
Financial institutions deal with vast amounts of unstructured data, including transaction records, emails, news articles, and social media feeds. Analysing this data helps detect fraudulent activities, assess market trends, and make informed investment decisions. For instance, hedge funds and trading firms leverage sentiment analysis of news articles and social media posts to gauge market sentiment and adjust trading strategies accordingly.
Retail
Unstructured data from customer reviews, social media mentions, and product images can provide retailers with valuable insights into consumer preferences, buying behaviors, and trends. By analysing this data, retailers can optimise pricing strategies, personalize marketing campaigns, and enhance the overall shopping experience. Companies like Amazon and Walmart use machine learning algorithms to analyse customer reviews and recommend products to users based on their preferences and purchase history.
Unstructured Data Challenges
Challenges in managing and analysing unstructured data include:
Volume and Variety
Unstructured data comes in various formats and from multiple sources, making it challenging to manage and analyse effectively. Businesses must invest in robust data storage, like Pure Storage® FlashBlade®, which was built to handle unstructured data, and analytics infrastructure to handle the sheer volume and variety of unstructured data.
Complexity
Unstructured data often contains noise, inconsistencies, and ambiguities, making it difficult to extract meaningful insights. Businesses need advanced data preprocessing techniques and machine learning algorithms to clean, normalize, and structure unstructured data for analysis.
Data Privacy and Security
Unstructured data often contains sensitive information like personal identifiers or proprietary business data. Ensuring data privacy and security is essential to protect against unauthorized access, breaches, and regulatory non-compliance.
Bias and Fairness
Unstructured data analysis can inadvertently perpetuate biases present in the data, leading to unfair or discriminatory outcomes. For this reason, it’s extremely important to address biases in data collection, preprocessing, and algorithmic decision-making to ensure fairness and equity.
Data Quality and Veracity
Unstructured data is inherently noisy and may contain errors, inconsistencies, or misleading information. Ensuring data quality and veracity is crucial for obtaining reliable insights and making informed decisions. This requires careful data cleaning, validation, and verification processes to identify and correct inaccuracies in the data.
Regulatory Compliance
With the increasing focus on data privacy and protection regulations such as GDPR, CCPA, and HIPAA, organisations must adhere to stringent compliance requirements when collecting, storing, and processing unstructured data. Failure to comply with these regulations can result in hefty fines, reputational damage, and legal consequences.
Strategies for Managing Unstructured Data
Effective management of unstructured data requires a comprehensive, unified approach that encompasses data classification, organisation, storage, governance, security, and compliance.
Here are strategies to manage unstructured data effectively:
1. Data classification and organisation
Attach descriptive metadata tags to unstructured data to categorize and classify it based on attributes such as content type, source, creation date, and relevance. You can also use automated tools and algorithms, such as NLP and machine learning, to analyse the content of unstructured data and extract relevant information for classification. Another best practice is to develop taxonomies and ontologies to define hierarchical structures and relationships between different types of unstructured data, facilitating easier navigation and retrieval.
2. Data storage
Having a powerful, flexible, unified data storage system is key for managing and making the most of your unstructured data. A great way to do this is through a system that can do unified object and file storage. Pure Storage offers unified fast file and object storage that consolidates native fast file and fast object storage with an architecture that delivers simplicity, scale, and performance. You can also use things like tiered storage architectures to optimise storage efficiency and cost-effectiveness.
3. Data governance
It’s very important to establish data governance policies and procedures to define roles, responsibilities, and processes for managing unstructured data throughout its life cycle, including creation, storage, access, and disposal. You should also implement granular access controls and authentication mechanisms to restrict access to sensitive unstructured data based on user roles, permissions, and data sensitivity levels. Finally, encrypt unstructured data at rest and in transit using strong encryption algorithms and protocols to protect against unauthorized access, data breaches, and interception.
Emerging Trends in Unstructured Data Analysis
Important trends in unstructured data analysis include;
Advanced NLP and Deep Learning
As NLP and deep learning techniques continue to evolve, we can expect more sophisticated algorithms for understanding and processing unstructured text data. This includes improved language understanding, sentiment analysis, and context-aware processing.
Multimodal Data Analysis
The proliferation of multimedia content, such as images, videos, and audio, is creating a growing trend toward multimodal data analysis. This involves integrating techniques from computer vision, speech recognition, and NLP to analyse unstructured data and get insights from it.
Edge Computing and IoT
Edge computing and the internet of things (IoT) now generate vast amounts of unstructured data at network edges. Analysing this data in real time presents new challenges and opportunities for deploying lightweight algorithms and models for on-device processing and decision-making.
Explainable AI
As AI systems become increasingly pervasive in analysing unstructured data, there’s a growing demand for explainable AI techniques that provide transparent and interpretable explanations for the decisions made by these systems. This is particularly important for applications such as healthcare and finance where trust and accountability are critical.
Best Data Platform for Unstructured Data Storage and Analysis
The better companies get at analysing and using unstructured data, the better they’ll get at providing what their customers need and want. The link between the two is undeniable.
And yet, technology-wise, the IT world is still very much in a growth phase when it comes to fully taking advantage of unstructured data.
That’s where a data platform like Pure Storage comes into play.
With a focus on reducing costs and complexity without sacrificing performance, Pure Storage addresses increasingly thorny IT issues like unstructured data analysis.
How does a data platform like Pure Storage help you take full advantage of your unstructured data?
Pure Storage offers a unified, scale-out data platform with an innovative architecture that delivers efficiency, simplicity, and performance. FlashBlade was designed to deliver cutting-edge capabilities without complexity for the highest-performance file and object workloads.
But don’t just take our word for it. See why Pure Storage has been named a Gartner® Magic Quadrant™ Leader for Distributed File Systems and Object Storage for three years in a row.
