Dimensionality Reduction, a fundamental aspect of big data analytics, is a process designed to mitigate the complication of data analysis by reducing the complexity and size of data. When dealing with big data, datasets often comprise a high number of dimensions or variables, leading to increased computational expenses and the risk of extraneous or misleading information. As such, Dimensionality Reduction techniques are employed to simplify the data without losing important or relevant information.

• Efficiency improvement: One of the prime reasons for using Dimensionality Reduction is the enhancement of data processing speed. The reduction of irrelevant or redundant features in Big Data simplifies data management and improves computational efficiency.

• Data visualization aid: Making sense of multi-dimensional datasets is difficult. By reducing the dimensions, it becomes easier to visualize and understand the data, leading to insightful interpretations.

• Noise elimination: Dimensionality reduction removes unnecessary variables leading to less noisy and more accurate data. This process improves the model’s overall performance and provides a more accurate analysis.

• Reduced storage: By eliminating insignificant or superfluous dimensions, data space is reduced significantly, leading to less storage requirement and reduced cost.

• Inference augmentation: High dimensional data often incorporates a risk of overfitting. By effectively reducing dimensions, models can be made more general, thus making predictions and inference more reliable.

Several industries routinely use Dimensionality Reduction techniques for big data due to its ability to simplify and increase the efficiency of data interpretation and analysis, without the loss of essential data or information.

• Improved Speed: One of the most significant advantages of reducing data dimensions is the speed at which data-based tasks can be achieved. By reducing the complexity and size of data, computational tasks are less intensive and faster.

• Reduced Storage Space: With less data to store, businesses can reap the benefits of the reduced storage space requirements and the associated costs. This reduction is especially important in industries where real-time data analysis is required.

• Enhanced Data Visualization: With fewer variables to consider, it's easier to visualize data patterns and trends. This clearer understanding leads to more accurate strategic decisions.

• Reduced Noise: Reduction techniques aim to disregard irrelevant or redundant data, effectively minimizing the "noise" and enhancing the accuracy and reliability of the data models.

• Protection Against Overfitting: Overfitting, a common issue in machine learning, happens when models capture random noise in the data instead of the actual patterns. By reducing dimensions, the risk of overfitting is significantly minimized.

However, like all data processing techniques, Dimensionality Reduction is not without its disadvantages:

• Loss of Information: While the goal is to only remove irrelevant or excessive information, there's always a risk of losing some potentially important information in the process.

• Understanding Complexity: The techniques used in Dimensionality Reduction, such as Principal Component Analysis (PCA), aren’t always easy to understand or interpret which can lead to complications.

• Difficulty in Inverse Transformation: Though data can be reduced to lower dimensions, restoring it back to its original state is not always possible or easy—a factor that can be problematic in certain analyses.

In implementing Dimensionality Reduction in big data analytics, proper understanding and application are vital. The process involves identifying the right technique suited for the dataset and use case, executing the reduction, validating the results, and finally, making use of the simplified data in subsequent analytic processes. In all, understanding the benefits and challenges are essential in successful application of Dimensionality Reduction techniques in big data.