27 November 2024 Structured Data vs Unstructured Data Share this message The difference between structured and unstructured data is fundamental to data management and analytics. Here’s an overview of the two types: 1. Structured Data Definition: Structured data is well ordered and adheres to a specified structure or standard. It is kept in a way that allows for easy search and analysis. Examples include relational databases, spreadsheet tables, financial transactions, and customer records. Characteristics: Format: Tabular (rows and columns), with each column having a specific data type (e.g. integer, string, date). Storage: Typically stored in relational databases (e.g., SQL databases), which manage and retrieve data using structured query languages (SQL). Searchability: The preset structure makes it easy to search using queries. Analytics: Structured data is simpler to analyze with statistical and mathematical models, making it perfect for dashboards and reporting. 2. Unstructured Data Definition: Unstructured data has no specified format or organization, making it more difficult to handle and analyze. This sort of data does not fit easily into tables and requires more advanced ways of analysis. Examples include text documents, emails, social media postings, audio, video, photos, and sensor data. Characteristics: Format: Numerous unstructured data types and formats exist, which differ greatly in terms of content and storage methods. Storage: Databases built for non-tabular data, such as NoSQL databases (e.g., MongoDB) or data lakes. Searchability: Difficult to find and evaluate without expert tools. Analytics: Derive insights through increasingly advanced processing, such as machine learning techniques. Key Differences FeatureStructured DataUnstructured DataFormatTabular (rows and columns)Text, images, audio, video, etc.StorageRelational databases (SQL)NoSQL databases, data lakesSearchabilityEasily searchable and queryableRequires specialized toolsAnalyticsStraightforward, quantitativeRequires AI and machine learningExamplesSpreadsheets, CRM data, SQL tablesSocial media, videos, emails 3. Semi-structured data Semi-structured data falls somewhere in the center. This type does not fit nicely into structured databases, yet it retains certain organizing qualities. JSON and XML are popular formats used in online data and APIs, where data is kept in key-value pairs that provide some structure but not to the level of a relational database. Semi-structured data has greater flexibility than structured data and is simpler to deal with than unstructured data. Make Better Decisions with Generative AI-driven Answers from Unstructured Data. Using Qlik Answers. Contact us for a demo Mireille Erasmus Mireille Erasmus joined Bitmetric in June 2022 as a Marketing Manager after immigrating from South Africa. She loves working with people and firmly believes in the power of positivity. Mireille focuses on creating effective campaigns and building strong connections with customers. With a passion for using data to drive results, she combines creativity with a positive mindset to develop marketing strategies. When she’s not busy at work, you’ll find Mireille spending time with friends and family, walking her dog, or visiting local markets. Qlik How can we help? Barry has over 20 years experience as a Data & Analytics architect, developer, trainer and author. He will gladly help you with any questions you may have. Call us Mail us 23 April 2025 When Everyone Has Different Numbers: Why Data Alignment Matters Different teams, different data, different results. This post explores how misaligned data leads to confusion, and how TimeXtender helps bring everyone back to the same page. TimeXtender 22 April 2025 Qlik Cloud Learn Qlik Cloud introduces the new Learn page in activity centers, replacing Getting Started. Learn offers new learning paths for onboarding and a variety of resources to help users get up to speed with Qlik Cloud. The Learn page is available in Analytics, Insights, and Administration activity centers. New Release Qlik 22 April 2025 Data Flow: Expanded processors with RegEx support, multi-output forks, and number conversion Data Flow in Qlik Cloud now offers expanded no-code processors, including RegEx support for advanced string manipulations, a new Convert to number function, multi-output forks for flexible branching, and improved math operations. The Strings processor also features a new replace option to handle null values. New Release Qlik
23 April 2025 When Everyone Has Different Numbers: Why Data Alignment Matters Different teams, different data, different results. This post explores how misaligned data leads to confusion, and how TimeXtender helps bring everyone back to the same page. TimeXtender
22 April 2025 Qlik Cloud Learn Qlik Cloud introduces the new Learn page in activity centers, replacing Getting Started. Learn offers new learning paths for onboarding and a variety of resources to help users get up to speed with Qlik Cloud. The Learn page is available in Analytics, Insights, and Administration activity centers. New Release Qlik
22 April 2025 Data Flow: Expanded processors with RegEx support, multi-output forks, and number conversion Data Flow in Qlik Cloud now offers expanded no-code processors, including RegEx support for advanced string manipulations, a new Convert to number function, multi-output forks for flexible branching, and improved math operations. The Strings processor also features a new replace option to handle null values. New Release Qlik