In data-driven decision making, the process of examining data sets to draw conclusions is known as Data Inference. This involves analyzing patterns and trends within the data to make informed decisions.

A moving average primarily smooths out random fluctuations or noise in time series data. This helps highlight underlying patterns and trends by reducing the impact of short-term, erratic movements.

In a relational database, data is organized and stored in tables. Each table consists of rows and columns, where each row represents a record and each column represents an attribute. This tabular structure allows for efficient retrieval and manipulation of data using SQL queries.

The Binary Search algorithm is known for its efficiency in searching elements in a sorted array. It repeatedly divides the search interval in half, reducing the search space and making it a logarithmic time complexity algorithm.

Time Series Analysis would be most appropriate for predicting future customer churn in a retail context. This model considers the temporal aspects of data, allowing the company to identify patterns and trends over time that may indicate potential churn. Logistic Regression, Decision Trees, and Clustering are valuable for different scenarios but may not be the best fit for predicting time-dependent events like churn.

For C-level executives, dashboards should focus on presenting data at an appropriate level of granularity, ensuring relevance to strategic decisions. While aesthetics are important, the granularity and relevance of data contribute significantly to effective decision-making.

Smoothing is a technique used in time series data to handle missing or irregular data points. It involves reducing noise and capturing underlying patterns. Interpolation, extrapolation, and resampling are other techniques used in time series analysis.

Power BI is known for its strong integration with Microsoft products and services, making it a popular choice for organizations that rely on Microsoft technologies.

To effectively communicate complex data analysis to a non-technical audience, it's crucial to avoid technical jargon and use plain language. This ensures better understanding and engagement from the audience.

'Inventory Turnover' is a vital metric in supply chain management that measures how many times a company's inventory is sold and replaced over a specific period. A higher turnover ratio indicates efficient stock management.

Imputation is a common strategy to handle missing values by replacing them with estimated values based on the available data. This ensures that the analysis is not compromised due to missing information.

A Random Forest reduces variance by aggregating predictions from multiple decision trees. Each tree is trained on a different subset of the data, and their predictions are averaged, leading to a more robust and less overfit model compared to a single decision tree.