Online learning algorithms, also known as incremental learning algorithms, are a type of machine learning which accommodate data input in a serial, or sequential manner. Unlike traditional batch learning methods, online learning algorithms can adapt and learn from new data as it becomes available, offering continual updates to the predictive model.

• Continual adaptation: Online learning algorithms are capable of improving prediction accuracy over time by fine-tuning their model to new data as it streams in.
• Real-time execution: These algorithms are designed to train and update immediately, making online learning techniques suitable for real-time predictions and adaptions.
• Resource efficient: As online learning algorithms only need to process and store new data, they tend to be more memory and computationally efficient than traditional batch learning methods.
• Predictive maintenance: The continual calibration of the model allows for more proactive maintenance in various applications such as industrial machinery, anticipating and remedying issues before they become catastrophic.
• Flexibility: As they can learn from new data, online learning algorithms can adjust to changing patterns and trends in data, which is immensely useful in evolving fields or non-stationary environments.

A wide range of industries utilize online learning algorithms, thanks to their adaptability, efficiency, and predictive power. These fields range from predictive maintenance in manufacturing to customer behavior prediction in e-commerce.

Online learning algorithms require careful planning and consideration for successful implementation. Understanding the nature of data, rate of change, and how quickly the model needs to adapt are significant considerations.

An in-depth analysis of the requirements and resources, as well as the potential trade-off between the flexibility of online learning and the complexity it introduces, must be considered. The careful selection of learning rates, the understanding of the potential influence of noisy data, and the scalable nature of the system should be part of your strategic planning.

• Adaptability: The ability of online learning algorithms to learn and update with new incoming data allows models to adapt to trends and changes in the underlying data distribution swiftly.
• Efficient: As these algorithms process data as it arrives, they tend to be less memory-intensive and computationally demanding than batch algorithms that need to store and process all data at once.
• Real-time prediction: Online learning algorithms are efficient in time-sensitive applications where immediate predictions are required, such as financial trading, health monitoring systems, and autonomous driving.
• Easy to scale: Since online learning algorithms only require the newest data, they are easy to scale for large datasets.
• Minimal dependability on data distribution: Online learning algorithms’ adaptability makes them effective in handling non-stationary data (data distribution that changes over time) such as climate or seasonal trends.

However, like any method, online learning algorithms come with potential limitations:

• Sensitivity to noise: As the model updates frequently, it may become more sensitive to noisy data and outliers, skewing the model accuracy.
• Outdated data effects: If data changes drastically over time, past predictions and models may become irrelevant or misleading.
• Complexity: The continual updating of the model can add complexity in managing and maintaining the systems.
• Rate of learning: Too slow rate of learning can limit the system's adaptability, while too fast can make the model too sensitive to noise and outliers.
• Data privacy issues: In some cases, continual data streaming could raise privacy and security issues.

In conclusion, online learning algorithms represent a highly adaptable and efficient machine learning technique suitable for ever-changing and growing data. Its limitations, mainly sensitivity to noise and rate of learning, require careful strategic handling. A successful implementation relies on understanding the algorithm’s characteristics, the data, and the application for the best results.