How do you explain exceptions to periodic trends?

1 BI Basics

Business intelligence involves analyzing data to inform business decisions. In this context, periodic trends refer to patterns that occur at regular intervals within your data. Think of sales that spike every quarter or website traffic that dips on weekends. Understanding these trends is essential because they help predict future performance and guide strategic planning. However, when data deviates from these trends, it's a signal that something out of the ordinary is happening, warranting a deeper dive to understand the 'why' behind the anomaly.

2 Trend Analysis

When you spot an exception in a periodic trend, the first step is to confirm it's not a data error. Once verified, trend analysis comes into play. This involves looking at historical data to see if similar exceptions have occurred before and under what circumstances. You'll want to examine external factors that may have influenced the data, such as market changes or new competitors. By understanding the context, you can begin to explain why this exception occurred.

3 External Factors

External factors can heavily influence business data. For example, an unexpected economic event could disrupt buying patterns, leading to a sudden change in sales trends. It's your job to identify these external influences and assess their impact on your data. This might involve looking at economic indicators, industry news, or changes in consumer behavior. By correlating these factors with your data exception, you can provide a more accurate explanation for the deviation.

4 Internal Dynamics

Internal dynamics within a company can also explain exceptions to periodic trends. Changes in strategy, operations, or internal processes can all result in data anomalies. Perhaps a new marketing campaign was launched, or there was a shift in the sales team's approach. Scrutinizing these internal changes can reveal how they have influenced the data and led to the observed exception.

5 Data Context

Understanding the context of your data is paramount when explaining exceptions to periodic trends. This means considering the data's source, collection methods, and any transformations it has undergone. Sometimes, a change in how data is gathered or processed can lead to apparent exceptions. Ensuring that you have a complete picture of the data lifecycle will help you accurately interpret any deviations from expected patterns.

6 Future Predictions

Finally, explaining exceptions is not just about understanding the past; it's also about predicting the future. By analyzing the exception, you can adjust your predictive models to account for similar occurrences going forward. This might involve updating algorithms or re-evaluating assumptions about how certain factors affect your data. The goal is to refine your BI tools so they can better handle exceptions and provide more accurate forecasts.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?