In a world increasingly driven by data, businesses are forever chasing after insights that can propel them ahead of the competition. Yet, amid the buzzwords like “big data”, “AI”, and “machine learning”, there’s one crucial aspect that often gets overshadowed or even overlooked. That is exception management, or managing by exception.
In this blog, we will delve into the critical role exception management plays in modern business performance management and why it’s the under-appreciated hero of data-driven decision-making.
What is Exception Management?
Exception management refers to the process of identifying, assessing, and managing outliers or anomalies in data sets. While conventional wisdom tells us to focus on general trends, exceptions – those data points that deviate from the norm – often hold the key to transformative business insights. They serve as red flags that signal underlying issues, or gold mines that offer unprecedented opportunities to improve the management efficiency of a particular entity within your business.
The Traditional Approach to Data
Traditionally, businesses have employed a reactive approach to data analytics. They’d collect data, analyse it, and create reports that describe what happened in the past. However, this method often fails to provide actionable insights for the current state and for the future. Why? Because it generally lacks a mechanism for quickly identifying and addressing exceptions.
Why Exception Management is Crucial
By utilising the power of current technologies, you would decide which metrics, key performance indicators, targets, strategic objectives etc. you would want to manage and report on. Based on that selection, the applicable ratios and data points would be set, and the system would then proactively report on any fluctuations, up or down. This would allow your organisation to visually, and very easily, determine the day-to-day performance of those specific metrics.
By adopting this approach, your organisation would accrue many benefits, some of which are defined below:
• Detect Operational Inefficiencies
Exception management helps in identifying operational bottlenecks that are often invisible when we only focus on averages and general trends. Imagine a supply chain system where 95% of orders are processed within 24 hours. Sounds like a highly efficient operation, right? However, a keen eye for exceptions might reveal that the remaining 5% are taking up to a week! This could be the difference between a satisfied repeat customer and a lost client.
• Uncover Business Opportunities
In sales and marketing, catching the outliers can signal untapped opportunities. Maybe a particular demographic is showing exceptionally high engagement rates with your product but has not yet been targeted in your campaigns. Ignoring this anomaly means leaving money on the table.
• Risk Mitigation
Fraud detection, compliance monitoring, and quality assurance are fields that benefit immensely from robust exception management. Anomalies in transactional data could be early warning signs of fraudulent activities, while inconsistencies in product quality metrics could indicate a significant risk to both customers and the brand.
The Practical Steps to Implement Exception Management
1. Data Collection:
Gather data from all relevant sources. The more comprehensive the data, the more accurate your exception reports will be.
2. Data Cleaning:
Preprocess the data to remove any noise or irrelevant information. This step is crucial for the algorithms to work efficiently.
3. Target Identification:
Set up triggers that alert you to changes in the ongoing performance of your defined KPIs, targets and key metrics.
4. Exception Alerts:
Utilise statistical techniques or machine learning models to identify exceptions automatically. These alerts would be set up as key ratios and metrics and the alert would provide proactive insight into any fluctuation in the data.
5. Exception Evaluation:
Not all exceptions are valuable. Assess the identified exceptions to determine their business relevance.
6. Action and Review:
Develop strategies to manage these exceptions. This could be an automated response or could involve human intervention for more complex scenarios.
7. Feedback Loop:
Continuously monitor the effects of your actions to adjust your Exception Management processes accordingly.
Conclusion
Exception management is more than just a data analytics practice; it’s a mindset that urges you to look beyond the obvious, to seek the extraordinary amid the ordinary. When effectively integrated into your business performance management, it doesn’t just offer a safety net for risk mitigation but also serves as a trampoline for unprecedented growth opportunities.
In today’s competitive landscape, where everyone has access to similar kinds of data, exception management offers a unique edge. So, the next time you’re buried deep in spreadsheets and dashboards, remember – sometimes it’s the odd one out that makes all the difference.
Ready to make your business truly exceptional? Start embracing exception management in your data analytics journey today. Contact Canvas Intelligence to learn more.
