What is Unstructured Data? A Guide to Storage, Processing, and Analysis
Much of the data we have used for analysis in traditional enterprises has been structured data. It’s easy for humans to break down, understand, and, in turn, find insights from it. However, much of the data that is being created and will be created comes in some form of unstructured format.
However, the digital era has brought an explosion of unstructured data. Whether it’s social media text, images, videos, PDFs, or even audio files, unstructured data represents a wealth of untapped insights that can provide context beyond what traditional transactional data offers.
Despite its potential, unstructured data presents a unique challenge: it’s difficult to categorize, organize, and process. New approaches, tools, and storage solutions are emerging to address these complexities, making it possible to access this goldmine of information in ways that drive business insights, improve products, and personalize customer experiences.
Unstructured Data Storage
There are several common solutions teams pick to manage unstructured data. The most obvious solutions are S3, Google Cloud Storage(GCS), or Azure Blob storage. These allow you to store files in any format you’d like.
Now, I’d like to point out that this is one of the issues. You can store your unstructured data without any thought to its organization. This is a great opportunity to actually put some effort into storing data. The simplest method I have come across that is helpful is to organize data, regardless of whether structured or unstructured, in solutions such as S3.
Domain -> System/Source(Outside vendor, Salesforce, etc) -> Entity(Customer, Patient, Eligibility, Orders) -> Date broken down as required.
This makes it easy for both humans and machines to traverse your unstructured data. Because either you make it very easy to manually dig into specific data to look for some information, or you’re giving an agent or automated system the ability to gain metadata about the information they are looking for.
Vector Databases
Vector databases are a relatively new approach to storing data(compared to traditional databases). They were designed specifically to handle the storage, retrieval, and similarity search of unstructured data by representing it as vectors. Unlike traditional databases that store data in tables, vector databases are optimized to store high-dimensional vector embeddings.
These embeddings capture the semantic meaning of unstructured data types by transforming them into vectors.
For example, when storing images in a vector database, each image is converted into a vector that reflects its key attributes and patterns. When another similar image or query is introduced, the database can quickly identify the most similar images by calculating the distance between their vectors. This capability is extremely valuable in applications like recommendation engines, fraud detection, personalized search, and natural language processing.
Key Features and Benefits of Vector Databases:
- Semantic Similarity Search: By comparing vectors, these databases can find similarities between unstructured data objects without relying on exact matches, enabling nuanced search capabilities.
- Scalability: Built to handle high-dimensional data, vector databases scale to accommodate the massive volume of unstructured data being generated.
- Integration with Machine Learning Models: Vector databases complement machine learning workflows by providing a storage and retrieval layer that works seamlessly with models generating embeddings.
Common Vector Databases:
Popular solutions include Pinecone, Milvus, and Weaviate, each offering features tailored for specific use cases, such as fast similarity search, and flexible indexing.
Using Machine Learning to Process Unstructured Data
Machine learning is pivotal in making unstructured data usable. Whether we are talking LLMs or more traditional(that’s weird to say) approaches to machine learning, Using ML to transform unstructured data from a raw format into insights is key.
Now, there are a few common approaches we’ve seen. Prior to LLMs, data science teams would create notebooks or scripts to parse out key information. For example, when I worked at Facebook, there was a whole set of pipelines focused on parsing out resume information in steps.
Parse out information, classify information, map information.
For instance, it’d have to make sure that the information was correctly mapped. The word “Harvard” in a person’s resume doesn’t mean someone went to Harvard for a bachelor’s degree.
They could have gotten an online certificate, gone to Harvard prep school, or been part of an association with the word Harvard in it. Meaning your system must be able to correctly discern between these differences. Now, perhaps with LLMs today, this is a simple issue, but in my experience, using fuzzy logic usually isn’t good enough.
As mentioned above, a common approach now is to use an LLM. Now, simply using an LLM chatbot approach isn’t sufficient because an end-recruiter in this example doesn’t just want a list of 10 candidates that the LLM decides is good enough. You could unknowingly be screening out some candidates, or after a long enough time, the LLM might just keep putting out the same 100 or so candidates(kind of like many LLMs do today with how they like to start their articles with “In today’s fast-paced tech environment”)
Just a quick plug: this is why I like Roe.AI’s approach, which is more focused on making unstructured data accessible to humans for purposes beyond just chat retrieval. So that if you’re building data analytics or operational workflows, you can access the information and its lineage more fully.
Key ML Techniques for Unstructured Data Processing
- Natural Language Processing (NLP): For text-heavy data, NLP models enable functions like sentiment analysis, topic extraction, and entity recognition. A great example of a tool to use here is AWS Comprehend. Which I have used in the past to pull out entity information.
- Computer Vision: Image and video data can be processed using ML algorithms that detect objects, identify facial features, recognize text, or even assess emotional expressions. You could also use it to more broadly and effectively parse PDFs.
- Speech-to-Text and Audio Analysis: Converting audio data into text and analyzing spoken words with NLP models allows companies to extract insights from recorded calls, video meetings, or customer service interactions.
Using machine learning to process unstructured data not only extracts insights but can also streamline workflows by automating tedious processes. However, the success of ML models heavily depends on access to high-quality training data and the resources to maintain and update these models over time.
Unstructured Use Cases
The above is great to understand. But what can you do with all this theory?
Well here are a few examples of both real and possible use cases you can get from unstructured data.
Marketing Research
Many companies need to perform marketing, sales and product research from internet data sources. If you’ve ever worked with top sales teams, they are always on top of what industries have gotten recent government funding, what companies are currently growing, and so on. Much of this comes from dozens of data sources that can be difficult to wrangle and instead, many sales teams just manually manage this research or pay for aggregators.
One recent example of a marketing and product team that was facing a similar problem was Keboola. They operate in the data and AI world offering an all in one data stack. In order to keep on top of all the data and AI trends they utilized a manual process. But it was far too hectic, requiring nearly half a dozen tools to get all the information processed. That’s why they turned out to be Roe.AI.
The integration with Roe AI led to significant improvements in both efficiency and accuracy. The time required for market research tasks was reduced by 30%, allowing teams to shift their focus from data collection to strategic analysis. Moreover, the accuracy of structured insights increased by 50%, making the research process not only faster but more reliable.This can be expanded into Customer 360 – being able to aggregate the feedbacks / complaints from omni channel like social media, news wire, blog posts. Traditional software do the data aggregation in a templated way, but now data analyst can drill in these unstructured data and extract arbitrary insights.
In-Person Store Analysis
Recently there was the outing of Amazon Go’s struggle to truly integrate AI to allow for cashier-less store sales. But there are perhaps some more reasonable first iterations of using in store video to analyze customer behavior in stores that could have been a better first step. For example, one use case that is worth digging into is tracking videos of customers and how they interact in your store. Are there places they leave certain items, or perhaps locations that they spend more time in and buy a cheaper version of a product. Really this all boils down to, can you analyze the video data of in-store behaviors to increase your average purchase size.
How Can You Use Unstructured Data?
Unstructured data can provide a whole new set of insights. The data is often richer but more difficult to handle. It becomes worth doing when you have good business questions to ask that could change the business. So yes, we’ve talked a lot about tools in this article. But it all comes down to the use case. Does it make sense for your business to invest in processing and handling unstructured data? If the answer is yes, then these tools can help. In fact, AWS comprehend made one project I worked on with a client so much easier, and you can see in the use case section a few examples where clients have used unstructured data with tooling to help simplify their workflows.
So feel free to try a few out in the process.
Disclosure: Seattle Data Guy does have a stake in Roe.AI
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