What are the 6 types of data analytics?

Prescriptive Analytics: Prescriptive analytics goes beyond predicting future outcomes by suggesting possible actions or interventions to achieve desired outcomes. It combines historical data, predictive models, and optimization techniques to recommend the best course of action. Prescriptive analytics helps answer questions like “What should we do?” and “What is the best decision or strategy to pursue?”

Diagnostic Analytics: Diagnostic analytics aims to identify the root causes and reasons behind past events or outcomes. It involves digging deeper into data to understand why something happened. Diagnostic analytics seeks to answer questions like “Why did it happen?” and “What were the contributing factors?”

Cognitive Analytics: Cognitive analytics involves leveraging advanced technologies like artificial intelligence (AI) and machine learning (ML) to simulate human intelligence. It focuses on extracting insights from unstructured data sources such as text, images, audio, and video. Cognitive analytics helps in tasks like sentiment analysis, image recognition, speech recognition, and natural language processing.

Text Analytics: Text analytics, also known as text mining or natural language processing (NLP), involves extracting valuable information and insights from textual data. It includes techniques like sentiment analysis, topic modelling, text categorization, and named entity recognition. Text analytics helps analyse large volumes of unstructured text data, such as customer reviews, social media posts, emails, and documents.

Spatial Analytics: Spatial analytics focuses on analysing data that has a geographic or spatial component. It involves understanding patterns, relationships, and trends within spatial data to derive insights. Spatial analytics techniques include geocoding, spatial clustering, spatial interpolation, and network analysis. It finds applications in urban planning, logistics, environmental analysis, and location-based services.

Social Network Analytics: Social network analytics, also known as social network analysis (SNA), examines the relationships and interactions between individuals, groups, or entities within a social network. It explores patterns of connections, influencers, and information flow within a network. Social network analytics can help identify key influencers, measure network centrality, detect communities, and predict behaviour within social networks.

Real-time Analytics

Real-time analytics focuses on processing and analysing data as it is generated in real-time or near real-time. It involves monitoring data streams, detecting patterns or anomalies, and taking immediate actions. Real-time analytics is crucial in applications like fraud detection, network monitoring, sensor data analysis, and real-time recommendations.

Behavioural Analytics: Behavioural analytics involves analysing and understanding patterns of human behaviour based on data. It focuses on capturing and interpreting user interactions, actions, and preferences to gain insights into behaviour patterns. Behavioural analytics is used in areas such as marketing, user experience optimization, fraud detection, and personalization.

Web Analytics: Web analytics deals with the collection, measurement, and analysis of data related to website usage and performance. It includes tracking website visitors, analysing clickstream data, measuring conversion rates, and optimizing web content and user experience. Web analytics provides insights into website performance, user behaviour, and marketing effectiveness.

Network Analytics: Network analytics focuses on analysing data related to networks, such as social networks, communication networks, or computer networks. It involves studying the structure and dynamics of networks, identifying influential nodes, detecting patterns of connections, and optimizing network performance. Network analytics is used in areas such as cybersecurity, social media analysis, and network optimization.

Video Analytics: Video analytics involves extracting insights and information from video data. It uses techniques like object detection, facial recognition, motion tracking, and video summarization to analyse video content. Video analytics finds applications in surveillance systems, automated video tagging, video recommendation, and video content analysis.

Financial Analytics: Financial analytics focuses on analysing financial data to gain insights into financial performance, risk management, and investment decisions. It involves techniques like financial forecasting, ratio analysis, trend analysis, and portfolio optimization. Financial analytics is used in areas such as banking, investment management, insurance, and financial planning.

Health Analytics

Health analytics deals with analysing healthcare data to improve patient outcomes, optimize healthcare processes, and identify patterns and trends in public health. It involves analysing electronic health records, medical imaging data, genomic data, and health insurance claims. Health analytics helps in areas like disease surveillance, personalized medicine, clinical decision support, and healthcare resource optimization.

Marketing Analytics

Marketing analytics focuses on analysing data related to marketing activities and campaigns to measure their effectiveness, understand customer behaviour, and optimize marketing strategies. It involves techniques like customer segmentation, campaign performance analysis, customer lifetime value analysis, and marketing mix modelling. Marketing analytics helps in areas such as customer acquisition, customer retention, and marketing ROI optimization.