Anomaly detection within time series data can be likened to finding a needle in a haystack; it refers to the process of identifying data points that deviate significantly from the expected pattern. As the prevalence of big data grows, so too does the importance of anomaly detection, particularly in industries like finance, health, IT, and many more where outlier values may indicate pivotal changes, errors, or critical incidents.

Anomaly detection in time series consists of several defining features:

• Predictive Accuracy: Anomalies, or outliers, represent deviation from an established norm. Hence, the accuracy in predicting these data points is a significant measure of the performance of anomaly detection solutions.
• Scalability: Data sets typically subjected to time series anomaly detection are extensive and continuously growing. Therefore, the scalability of the detection system is crucial.
• Flexibility: Anomaly detection needs to adapt to evolving data behavior and patterns due to shifting business landscapes, consumer behavior, or external factors.
• Robustness: False positives or trivial anomalies pose significant challenges to diagnosis, particularly for large data sets. Therefore, anomaly detection solutions need to be robust to avoid the risks of faulty identification.
• Real-Time Operation: Given the time-sensitive nature of certain data types (like financial transactions), anomaly detection in real time is critical.

Effective anomaly detection in time series data offers several key benefits:

• Operational Efficiency: Quick and accurate identification of anomalies can help organizations rectify operational inefficiencies, reduce errors, and save costs.
• Risk Mitigation: Detecting anomalies can signal potential threats, which allows for quick action and risk mitigation.
• Improved Decision Making: Anomalies commonly represent essential insights that can help managers make strategic decisions.
• Better Customer Experience: In customer-centric industries, anomaly detection can help identify and solve issues proactively, leading to improved customer experience and retention.
• Predictive Maintenance: In industries like manufacturing, anomalies can predict machinery or system breakdowns, enabling preventive maintenance activities and minimizing downtime.

Despite its numerous advantages, anomaly detection isn't without challenges:

• Data Quality: Poor data quality, including missing values, inconsistent formatting, or noise, can impact anomaly detection performance and accuracy significantly.
• Setting Thresholds: Determining what constitutes an 'anomaly' in data can be challenging. Setting the threshold too high might miss subtle but important anomalies, while a low threshold could lead to many false positives.
• Changing Data Behavior: Over time, what once was considered normal can change, requiring continuous adaptation of the detection model.
• Complexity: Designing a model that captures complex relationships in data while remaining interpretable can be complicated.

Successful anomaly detection requires an in-depth understanding of the data, followed by comprehensive anomaly detection solution design. It's essential to choose the most suitable techniques for the specific data type and application to improve accuracy and minimize false-positive rates. Regularly updating the models to reflect changing norms is equally vital. Also, there should be a clear action plan for responding to detected anomalies timely and adequately.

In the era of big data, anomaly detection in time series presents both significant challenges and opportunities. To turn challenges into opportunities, careful planning, strategic deployment, and continuous evolution of the anomaly detection systems are required. Nevertheless, the rewards in the form of operational efficiency, risk mitigation, improved decision-making capabilities, and enhanced customer experience make anomaly detection a valuable tool in today's data-driven world.