Businesses have long been increasingly inundated with an unprecedented volume of data. The challenge now is not just about storing ample data but managing, classifying, and transforming this structured and unstructured data into fuel for the engine of business.
The critical role of data structuring becomes even more evident when considering the opportunities for generative AI, and other AI applications.
This publication will explore how structured data is not just a necessity but a transformative agent in the realm of artificial intelligence, turning raw data into actionable AI insights, levers for automation, and as fuel for the engine of AI as a co-creator in the work that we produce.
Structured Versus Unstructured Data in AI
What is unstructured data?
Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. It is typically text-heavy, but may also contain dates, numbers, facts, meaningful sounds, and diagrammatic or other visual information.
Unstructured data may have implicit structure, or structure that relies entirely on human behavior for consistency. For example, editorial conventions for how to structure articles or formal emails may have structure, but that structure is generally not enforced by a system either in the use of structural elements, or most especially, in the information that populates these structural conventions.
Examples of sources of unstructured information include: social media posts, videos, photos, audio files, PDF documents, PowerPoint presentations, webpages, emails, random notes in a CRM contact record not selected from a drop-down list, and many others.
What is structured data?
Structured data is highly organized and is generally more easily searchable by simple, straightforward search engine algorithms or otherwise easier to consistently access and operationalize. It is typically stored in databases and is arranged in rows and columns, making it easy to enter, query, and analyze.
Examples include: Excel spreadsheets, SQL databases, network logs, transaction data, customer relationship management (CRM) system data types and drop-down menu options, and many others.
Why is structured data important for Generative AI? A helpful analogy.
Let’s use an analogy. How about food? Food is popular.
Imagine you’re a chef trying to cook a complex dish in a kitchen where all the ingredients are mixed up in a big pile. This chaotic kitchen is like unstructured data: everything is there, but it’s all jumbled together, making it hard to find what you need, or even know what is really there in the first place.
Unstructured data, like random piles of ingredients, can still be useful. It’s like having a variety of foods from different recipes all over your kitchen. But, if you don’t manage this unstructured data – say, by sorting out what ingredients are there and how they can be used – you can’t effectively incorporate them into your dish. You are trying to make a good curry and all you can find are spices for pasta. You might have the best spices hidden somewhere in the pile, but if you can’t find them when you need them, they’re of no use.
How can we extend this analogy into the realm of data?
Sorting and labeling: If you don’t sort and label your ingredients (structuring your data), you can’t efficiently cook your dish (effectively run your AI application).
Findability and Operationalization: If your ingredients (data) are sorted, labeled, and stored in the right places, you know where the spices are, which drawer holds the utensils, and where to find different types of vegetables (findability and operational access).
Quality Management, Business Goals: Furthermore, you are better able to properly manage your ingredients (data management) for effective cuisine (business goal achievement). For example, ensuring you are using the best ingredients (accurate data), not letting your vegetables rot (e.g., cleansing outdated data, refreshing with new data).
Risk Management: Rigorous quality management includes achieving health and safety goals (compliance and regulatory) such as not poisoning your customers (violating privacy or providing misinformation), avoiding harm or being shut down by the health department (litigation, regulatory enforcement, PR crisis).
How Does Structured Data Benefit Generative AI?
Structuring data is beneficial for both the training and the application of generative AI. Let’s explore key principles with examples from three different industries.
Structured Data Enhances Data Quality
Structured data ensures consistency and accuracy, crucial for training reliable models. For instance, a structured database of images tagged with specific attributes (like color and shape) allows Generative AI to learn these features accurately.
Pharma: Structured clinical trial data allows for precise modeling of drug interactions and efficacy.
Energy: Structured data from IoT sensors helps in accurately track and model energy hardware operating envelopes. Structured site data from aerial imagery can help identify optimal volume and direction of solar panel placements or erosion vulnerabilities in specific areas.
Manufacturing: Precisely structured quality control data, such as defect patterns in images, enables AI to identify and learn from defects, improving defect prevention strategies.
Structured Data Enables Efficient Data Processing
In a scenario like language model training, structured data (such as a corpus with annotated grammar and syntax) enables more efficient parsing and understanding of linguistic rules, speeding up the training process.
Pharma: Structured genetic and molecular data accelerates the process of identifying potential drug compounds.
Energy: Organized data from weather and energy usage facilitates faster AI analysis for predicting energy needs.
Manufacturing: Structured production line data aids in rapid processing and analysis for optimizing manufacturing processes.
Structured Data Supports Better Feature Extraction
Feature extraction is the process of simplifying and converting complex data, like images or text, into a format that’s easier for computer programs to understand and use for tasks like recognizing patterns or making predictions.
Pharma: Structured patient data enhances feature extraction for personalized medicine development or prescription, by simplifying and analyzing structured patient data, such as age, gender, medical history, lifestyle choices, and genetic information.
Energy: The ability to identify HVAC systems on building rooftops from aerial imagery helps calculate the volume of rooftop solar arrays that can fit a given structure.
Manufacturing: Analysis of video data can help safety managers identify unsafe practices on the manufacturing line such as recognising if people are not wearing hearing protection, or are standing too high on ladders.
Structured Data Helps to Identify Relationships Between Data Types
In financial market prediction, structured data (like historical price and volume data in a time-series format) helps in identifying trends and correlations essential for predictive modeling.
Pharma: Structured data on drug interactions facilitates understanding of complex relationships within pharmaceutical and other types of data, such as how certain genetic markers correlate with drug efficacy or how lifestyle factors impact disease progression. Data from advisory board proceedings may contain insights about healthcare access barriers that enrich patient outcomes and population health data.
Energy: Structured data from diverse sources (e.g., consumption, diverse energy source types, storage, equipment degradation, weather patterns) helps in identifying efficiency-enhancing or ROI-positive relationships.
Manufacturing: Correlating structured data from different stages of the manufacturing process reveals critical dependencies and process improvements across the supply chain and production lifecycle.
Structured Data Supports Scalability and Reusability
Scalability and reusability in the context of AI model training refer to the ability of AI algorithms to efficiently handle increasing amounts of data (scalability) and to be applied effectively to different but related data sets (reusability). This means that an AI model, once developed and trained, can adapt to and process larger or evolving datasets without significant redesign or loss of performance, and can also be applied to various scenarios or domains with minimal modification.
This dual capability is crucial in a data-driven world, where the volume of information and the diversity of applications are constantly expanding, requiring AI models to be both versatile and robust in handling different types and scales of data.
Pharma: Structured genomic data enables scalable drug discovery processes across multiple diseases.
Energy: Uniformly structured data from various green energy projects allows for reusable AI models in different geographical locations or for different energy source input types for data that applies across mixes of inputs.
Manufacturing: Consistent structuring of production data across different facilities enables scalable defect analysis and solution deployment.
Three Techniques Used for Structuring Data
Tagging data
Tagging data involves attaching labels or keywords to data elements to make them easily searchable and identifiable.Tags can be applied to a wide range of data types, including text documents, digital images, and web pages. Tagging Enhances data discoverability and organization, especially useful in content management systems and for SEO purposes.
Cataloging data
Cataloging refers to the systematic arrangement of data into categories and subcategories. It’s essential in database management, where data is organized in a structured format, like tables with rows and columns. Cataloging facilitates easier data retrieval and management, especially in large datasets.
Metadata
Metadata is data about data; it provides information about or documentation of other data managed within an application or environment. It includes details like the source of the data, date of creation, author, file size, and format. It’s crucial for data management, archiving, and retrieval; it helps in understanding the context and origins of data.
Each of these techniques plays a vital role in making data more organized, accessible, and useful, particularly in large and complex datasets where quick retrieval and efficient processing are necessary.
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Can You Automate the Structuring of Data?
The short answer is yes, data structuring can indeed be automated, and this process is increasingly common. Automation in data structuring involves using sophisticated algorithms and AI technologies to classify, tag, and organize vast amounts of data without human intervention. This approach offers several advantages:
Efficiency: Automation drastically speeds up the process of organizing large datasets, a task that is time-consuming and prone to errors when done manually.
Consistency: Automated systems can apply uniform rules for data structuring, ensuring consistency across datasets which is crucial for accurate analysis and reporting.
Scalability: Automated data structuring can easily scale to handle growing volumes of data, making it highly adaptable to the needs of expanding businesses and complex projects.
Advanced Capabilities: Modern AI-driven tools go beyond basic structuring; they can extract meaningful patterns, relationships, and insights from unstructured data, adding significant value.
It’s important to note that while automation can handle a significant portion of data structuring tasks, human oversight is still necessary. Experts in knowledge management are needed to define the rules and parameters for automation, ensure quality control, and interpret the structured data in meaningful ways. Additionally, the evolving nature of data and continuous advancements in AI and machine learning algorithms mean that the approach to automated data structuring is also constantly evolving, requiring ongoing adjustments and refinements.
The effective management of both structured and unstructured data is crucial in harnessing the potential of Generative AI and other AI technologies. It is imperative that IT directors view structured data as the well-organized kitchen that enhances AI’s efficiency, accuracy, scalability, and creative effectiveness.
This is particularly vital across diverse industries, where such data aids in streamlining processes, improving feature extraction, and uncovering essential relationships for applications in nearly all process lifecycles and for all segments of business, government, and civil society organizations.
However, the value of unstructured data, with its inherent richness, cannot be understated. IT directors should actively seek to transform this data into a structured format, and use techniques to harness implicit structures within unstructured data, in order to unlock its vast potential. Implementing techniques such as tagging, cataloging, and effective metadata usage is essential in this transformation process.
To this end, IT directors should consider:
Investing in Data Structuring Tools: Allocate resources to tools and technologies that aid in converting unstructured data into structured data.
Focus on a Blended Approach With Automated Data Structuring Tools: Allocate resources to sophisticated algorithms and AI technologies that can efficiently classify, tag, and organize data. While leveraging automation, maintain human oversight to ensure quality control and meaningful interpretation of data.
Focus on Data Management Strategies: Develop robust strategies for data management that emphasize the organization, quality, and accessibility of data.
Train Teams in Data Handling: Ensure your teams are skilled in the latest data structuring techniques and understand the importance of structured data in AI applications. This includes collaborating closely with knowledge managers in your organization – they are your in-house experts in managing content as a form of data.
Regularly Review Data Processes: Continuously evaluate and update your data management processes to keep pace with the evolving landscape of AI and data science.
Train Teams for Effective Supervision: Equip your teams with the skills to oversee automated systems, understand their outputs, and refine the automation rules and parameters as needed.
Regularly Update Strategies: In light of continuous advancements in AI, regularly review and update your data structuring strategies and tools to stay ahead in the field.
By focusing on these areas, IT directors can lead their organizations in leveraging data not merely as a challenge to be overcome but as a powerful enabler of AI-driven innovation and business success.
This proactive approach in managing and structuring data will lay the foundation for advanced AI applications, driving efficiency, innovation, and competitive advantage in the marketplace.
