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BI and BA

Business Intelligence (BI): Definition

Past data has great value. Organizations after working for long period time accumulates pile of historical data. Several tools are develops to work on those data and Business intelligences is one of them. Business Intelligence refers to the processes, tools, and technologies used to collect, organize, and present historical business data for decision-making. It focuses on what has happened in the past and what is happening now.

  • BI = "What happened?"

    • Major Characteristics of BI:
    1. It is Descriptive in nature
    2. It uses dashboards, reports, and data visualization
    3. It Helps to monitor daily business operations
    4. It is mainly backward-looking (historical data)
    Example: We can consider retail company to see for what purpose it uses BI: A retail company uses a BI dashboard to see:
    • Last month's sales
    • Top-selling products
    • Daily revenue trends
    • Store-wise performance
    This helps managers understand current performance and take corrective actions quickly. Which products to continue, which to be discontinued. Which products need more marketing?

    Business Analytics (BA): Definition

    Several statistical measures are defined and are of great value in analyzing data. Statistical tools can help describ data, predict future from the existing data. Similary, predictive modelling and data analysis tools can dig data and find new information, knowledge, patterns. Business analytics is one of the tools to work on data and provide future insights. Business Analytics is the use of statistical methods, predictive modelling, and data analysis to interpret data and create insights for future decisions. It focuses on why things happened and what is likely to happen next.

  • BA = "Why did it happen?" and "What will happen next?"

    • Main Characteristics
    1. Predictive and prescriptive (Prescriptive analytics uses data, mathematical models, and algorithms to suggest what actions should be taken to achieve the best result.) in nature
    2. Uses machine learning, forecasting, statistical models
    3. Helps forecast trends and optimize decisions
    4. Forward-looking (future outcomes)
    Example
    1. A ride-sharing company uses analytics to:
      1. Predict peak demand in certain areas
      2. Set dynamic pricing
      3. Recommend driver allocation based on future trends
    2. This helps optimize profit and customer experience.

    1. Bhatbhateni SuperStore - Improving Store Operations

    Problem: Bhatbhateni SuperStore handles thousands of daily transactions and needed real-time visibility into store performance.
    BI Solution:
    1. Bhatbhateni SuperStore built dashboards that show:
      1. Sales by store, region, and product
      2. Inventory levels
      3. Real-time checkout performance
    2. Outcome:
      1. Managers quickly identify low-stock items
      2. Faster decision-making during peak hours
      3. Improved operational efficiency
    3. Why this is BI:
      1. It focuses on what is happening right now using dashboards and reports.

    2. Chiya Pasal - Customer Experience Enhancement

    Problem: Chiya Pasal wants to understand customer purchase behavior.

    BI Solution:
    1. They use loyalty program data to analyze:
      1. Most purchased drinks
      2. Customer visit frequency
      3. Location-wise sales trends
    2. Outcome:
      1. Improved menu planning
      2. Better staff scheduling
      3. Personalized marketing offers
    3. Why this is BI:
      1. It uses existing historical data to describe customer behaviour.

    Business Analytics (BA) - Real-World Case Studies

    1. Netflix - Predicting What Users Will Watch

    Problem: Netflix wants to recommend shows that users are likely to watch.

    BA Solution:
  • Uses machine learning to analyze:
    1. Watch history
    2. Viewer preferences
    3. Completion rate of shows
    4. Time of day watched
  • Outcome:
    1. Personalized show recommendations
    2. Increased viewer engagement
    3. Reduced customer churn
  • Why this is BA:
    1. It predicts future behaviour using predictive models.

    • Pathau - Surge Pricing Predictions

    Problem: Demand changes drastically with time and location.

    BA Solution:
    1. Pathau uses predictive analytics to forecast:
      1. Where demand will rise?
      2. How many drivers will be needed?
      3. When to apply surge pricing?
    2. Outcome:
      1. Efficient driver allocation
      2. Reduced wait time for customers
      3. Increased profit
    3. Why this is BA:
      1. It uses forecasts and optimization models.