23 Feb, 2024
Understanding how to use and leverage big data has become a critical success factor for most enterprises. Data has seen exponential growth recently with industry analysts estimating global data volumes will reach approximately 180 zettabytes (1 zettabyte = 1 billion terabytes) by 2025 with nearly 80-90% of that being unstructured data (more on that below). This article will explore the distinction between structured and unstructured data and highlight how PT78 uses tools like Power BI and Microsoft Purview to help mine valuable insights for our customers in a secure, efficient way. So, what is the difference between Structured and Unstructured Data and why should that matter to you? Let’s start with identifying characteristics of each and understanding why such a large percentage of information is unstructured. Structured Data Structured data refers to any data that can be easily organized, stored, and retrieved in a fixed format. It is typically managed in databases or spreadsheets, where the data is tabulated or arranged in a way that is easy to understand and manipulate. Examples are a customer relationship management (CRM) system, spreadsheets, accounting/invoicing system, human resource information systems (HRIS), and can help in search engine optimization (SEO), data interoperability using common data schemas. Unstructured Data Unstructured data is data that doesn’t have a predefined format or organization, making it harder to collect, process, and analyze. This includes documents, emails, text, images, videos, social media posts, etc. Some seemingly structured content like surveys, healthcare data, and content in collaboration software would fall in this category with increasing complexity and lack of organization. It’s not a huge surprise that today’s distributed, remote workforce using mobile enabled devices are generating more and more unstructured content. The trick lies in integrating these disparately formatted data sources with existing enterprise information to form a single, comprehensive source of truth. Robotic process automation (RPA) or optical character recognition (OCR) improved access but cloud-based AI/ML services that provide sophisticated analysis capabilities using advanced technologies like natural language processing (NLP) and pre-built machine learning algorithms truly deliver business intelligence securely and affordably. Harnessing Big Data with Tools and Strategies Data Visualization, Sharing and Governance PT78 implemented a data governance and information transparency capability for our data-centric customer by researching and implementing the latest tools, processes and strategies while establishing a feedback loop with their end-users. Our team then deployed a multi-step governance process that included performance management/tuning, taxonomy development, external data interface development and project management. This also included establishing and hosting a collaborative working group to assist the successful transformation of our customers processes and culture. We implemented Power BI, a tool for aggregating, analyzing, visualizing, and sharing data. The interface allows users to create reports and dashboards without deep technical skills. Power BI's strength lies in its ability to connect to a wide range of data sources, including both structured and unstructured data, enabling users to derive insights from diverse datasets. To maintain data oversight and allow Power BI to access the relevant data, PT78 implemented Microsoft Purview, a unified data governance service that helps organizations manage and govern their data across on-premises, multi-cloud, and software-as-a-service (SaaS) applications. It provides a comprehensive set of capabilities to discover, classify, protect, and govern sensitive data across an enterprise's landscape. Additionally, to maximize our application modernization work delivered using Power Platform, a low-code application development platform, PT78 migrated legacy data to Dataverse, a secure, data-as a-service connector-based service in Azure that provides tables, columns, rows, and pre-defined relationships. This combination of data aggregation and governance provided: Simplified data analysis and interpretation. Shared insights across the organization through easy-to-understand dashboards and reports. Provided informed decisions based on real-time data analytics. Took advantage of Microsoft platform adjacency to use Data and Cloud Services, AI/ML, and security capabilities to quickly deliver targeted business intelligence. Additional visibility into the customers data estate. Provide DLP and oversight over AI Applications. Automated data governance and compliance processes. Protected sensitive information from unauthorized access and leaks. Provided records management, retention, and validation. What’s Coming Next In addition to the significant changes to data types and volume, advancements in AI/ML and large language models (LLMs) can be leveraged to simplify user interactions. Using natural language interfaces to power Conversational AI queries takes advantage of the enterprise search capabilities and natural language processing (NLP) algorithms allows any end user to search using regular conversational language. Using this approach, we developed the PT78 Data Intelligence Service (DISe) that showcases the integration of multiple AI/ML features in our Cloud environment, Microsoft Azure Gov. This includes a separately hosted LLM foundational model called the Azure Open AI Service and leverages Azure AI Search for data retrieval. DISe uses a Retrieval Augmented Generation (RAG) design pattern with Azure GPT models that provides a natural language interface that we have embedded in an internal PT78 Microsoft Teams channel. Optional interfaces include Virtual Agents, web browsers, or developing a custom Conversational AI using the Microsoft Bot Framework. Azure AI Search simplifies data ingestion, transformation, indexing, and multilingual translation. Chat interface users can customize settings like temperature and persona for personalized interactions and features like explainable thought processes, referenceable citations, and direct content for verification are available. Azure OpenAI combines artificial intelligence, natural language processing, and machine learning to comprehend, generate, and interact with human-like text. Using the GPT architecture, models are pre-trained on large and diverse data sources and then fine-tuned on specific tasks or domains, like understanding industry-specific language or extracting text from images/documents. We combine a data transformation and integration layer that automates the data orchestration pipeline for structured and unstructured data, speeding up access to data insights by leveraging pre-built machine learning models that deliver advanced capabilities that previously required specialized software, hardware, and data science subject matter experts. DISe provides: · Lexical analysis using the Standard Lucerne Analyzer · Data chunking and Vectorization · Hybrid (Vector and Keyword) and Embedded Vector Search · Fuzzy search · Secure Azure Open AI GPT language model integrations · Enables the use of GPT without training or fine-tuning the foundational model(s) · Tailoring the content of the Conversational AI by combining pre-trained knowledge with your data source Conclusion The explosion of data in recent years has underscored the importance of effective data management and analysis. Understanding the difference between structured and unstructured data and leveraging powerful analytical tools like Power BI and Microsoft Purview can accelerate access to critical insights and help demystifying big data. These tools not only help in managing and governing massive amounts of data but also play a pivotal role in extracting actionable insights that can drive mission goals and innovation. Integrating a conversational AI capability improves access to data, reduces the resources needed to provide information to the end-user, and provides an intelligent interface that supports a conversation-like insight experience. Dennis Taylor Sr. Business Analyst/Alliances Platinum Technologies