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Analytics

1. Overview

1.1. Definition and Scope

  • Analytics refers to the systematic computational analysis of data or statistics.
  • It is used for the discovery, interpretation, and communication of meaningful patterns in data.

1.2. Types of Analytics

1.2.1. Fundamental

  • Descriptive Analytics: Analyzes historical data to understand changes over time.
  • Predictive Analytics: Uses statistical models and forecasts techniques to understand the future.
  • Prescriptive Analytics: Suggests actions you can take to affect desired outcomes.

1.2.2. Application based

  1. Business Intelligence (BI):
    • Purpose: Involves the use of data analysis tools to provide historical, current, and predictive views of business operations.
    • Components:
      • Data mining
      • Process analysis
      • Performance benchmarking
      • Descriptive analytics
    • Outcomes: Helps in making informed business decisions by highlighting trends and insights through dashboards and reports.
  2. Operational Analytics:
    • Purpose: Focuses on monitoring and analyzing current business operations to improve efficiency and effectiveness on an ongoing basis.
    • Components:
      • Real-time data processing
      • Forecasting for operational efficiency
      • Optimization of business processes
    • Outcomes: Provides actionable insights to refine and optimize internal operations, enhancing productivity and minimizing waste.
  3. Embedded Analytics:
    • Purpose: Integrates analytic capabilities directly into existing applications and business processes.
    • Components:
      • Seamless integration into software applications
      • Accessible analytics within operational workflows
      • Enhanced user engagement through context-specific insights
    • Outcomes: Offers users the ability to interact with analytics in real-time within the context of their daily tasks, driving informed decision-making as part of their standard operations.
  4. Connections:
    • BI vs. Operational Analytics: While BI is more about strategic insights and long-term decision-making, operational analytics zeros in on the day-to-day workings and is more tactical.
    • Operational vs. Embedded Analytics: Both deal with real-time data, but embedded analytics specifically focuses on integrating insights directly into the operational tools users are already employing.
    • BI vs. Embedded Analytics: BI typically stands alone as a platform that requires user interaction to analyze data, whereas embedded analytics brings the insights into the tools and systems the user already interacts with daily.

1.3. Challenges

  • Data Quality: Ensuring the accuracy and completeness of data.
  • Data Integration: Combining data from different sources.
  • Privacy and Security: Protecting sensitive data from breaches and misuse.

1.4. Connections:

  • The type of analytics used often correlates with business objectives, such as forecasting demand (predictive) or improving operational efficiency (prescriptive).
  • Tools and technologies are selected based on the data complexity, volume, and specific use cases within an organization.
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