How proactive monitoring improves data confidence, reduces risk, and accelerates issue resolution.
WHITE PAPER
Capturing Value Anomaly Detection with
How Proactive Monitoring Improves Data Confidence, Reduces Risk, and Accelerates Issue Resolution
In data-driven organizations,
issues often go unnoticed until they escalate—
impacting reporting accuracy, decision-making,
and customer experiences. One-time validations
or ad hoc checks aren’t enough to catch every
problem. Anomaly detection offers a proactive
solution. By enabling real-time visibility into data
quality and system performance, organizations
can catch issues earlier, respond quickly, and
improve overall confidence in data reliability.
At Concord, we help clients implement scalable
anomaly detection strategies that reduce
downtime, increase operational efficiency, and
strengthen confidence in core processes. This
white paper outlines the mechanics behind
anomaly detection, how it delivers business value,
and real-world success stories from our clients.
Capturing Value with Anomaly Detection
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Catch More Incidents, Reduce Manual Monitoring
Traditional checks miss issues that emerge gradually or
indicate pattern changes. Anomaly detection flags these
subtle changes—like data drift or unexpected pipeline
behavior—before they become serious problems, while
reducing the need for constant manual review.
Why Anomaly Detection?
Accelerate Time to Resolution
By pinpointing when and where anomalies begin,
anomaly detection minimizes diagnostic time. This
enables teams to shift from reactive troubleshooting to
proactive issue resolution.
Increase Confidence in Data and System Performance
Reliable data underpins everything from marketing
performance to operational decisions. Monitoring for
anomalies safeguards your most critical pipelines,
ensuring trustworthy insights and system health.
Capturing Value with Anomaly Detection
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What Makes a Solution “Anomaly Detection”?
1.) Continuous Monitoring at the Right Cadence
Monitoring intervals should match how frequently your data updates and how fast you need to act on issues.
For example:
•
Daily: Validate batch pipeline outputs and segment consistency
•
Hourly: Track business performance metrics or campaign trends
•
Every 5 minutes: Detect outages, fraud, or critical system anomalies.
2.) Defined Thresholds for Identifying Anomalies
Thresholds can be static (e.g., business-defined rules) or dynamic (e.g., based on historical or learned behavior).
Regardless of the method, thresholds must follow explainable logic to ensure trust and actionability.
3.) Automated Alerts and Escalation Paths
Detection means little without action. Alerting mechanisms should feed directly into operations—whether via instant
message, email, or incident workflows—so issues are addressed in real time.
Capturing Value with Anomaly Detection
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Approaches to Anomaly Detection
Below is a breakdown of widely used anomaly detection
methods, including their data requirements, complexity,
Visual Inspection
and ideal applications.
Visual inspection remains one of the simplest and
quickest approaches, often used for exploratory
analysis. While it offers limited capacity for complex
problem solving, it’s useful for identifying obvious
anomalies in raw counts or basic time series data.
Type:
Unsupervised / Supervised
Complexity:
Low
typical use cases:
Basic dashboard reviews, investigating reported
issues, informal campaign tracking, spot checks
during routine analysis
Capturing Value with Anomaly Detection
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anomaly detection methods
Statistical Time Series (e.g., ARIMA, SARIMAX)
Clustering Methods (e.g., DBSCAN)
Statistical time series models are grounded in proven,
Clustering techniques identify patterns by grouping
scientific methods and are often used to forecast trends
data based on similarity. These methods can effectively
and detect deviations. These models are relatively easy
flag outliers by defining what’s "normal." While they offer
to implement and interpret but may struggle with
valuable insights, costs can increase with data volume
irregular processes or multivariate relationships.
and complexity.
Type:
Type:
Unsupervised / Supervised
Unsupervised
Complexity:
Complexity:
Medium
Medium – High
typical use cases:
typical use cases:
Identifying out-of-plan behavior, spotting counter-
Detecting unusual data combinations, identifying
seasonal trends, uncovering marketing opportunities,
suspicious behavior, finding unique selling points,
measuring trend-based metrics over time
understanding natural groupings in data
Capturing Value with Anomaly Detection
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anomaly detection methods
Traditional Machine Learning Classifiers (e.g., Random Forests)
Neural Networks & Deep Learning (e.g., LSTM)
Deep learning models offer unmatched flexibility and
Machine learning classifiers offer scalable, automated
power for detecting complex, non-linear anomalies—
solutions for anomaly detection, especially in
especially in high-frequency or multivariate data.
structured, well-defined environments. These models
However, they require significant training data,
deliver reliable performance with variable costs
computational resources, and ongoing monitoring to
depending on data preparation and infrastructure.
remain effective.
Type:
Type:
Supervised
Supervised / Unsupervised
Complexity:
Complexity:
High
Very High
typical use cases:
typical use cases:
Fraud detection, segment-level outlier classification,
High-frequency anomaly detection, multivariate time-
automated anomaly scoring in production systems
series forecasting, modeling nonlinear or contextual
patterns, adaptive anomaly systems in dynamic
environments
Capturing Value with Anomaly Detection
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Types of Anomaly Detection Problems
TYPE
WHEN TO USE
DETECTION METHOD
EXAMPLE USE CASES
When anomalies are known or well-defined
Labeled inputs, often based on business logic
Fraud detection, bot activity, likely buyer identification
Supervised
Unsupervised- Univariate
Monitoring a single key metric over time
Detects values outside expected range
Transaction volume spikes, unique visitors per minute, unusually high attribute counts Downtime across systems, suspicious behavior based on multiple features, market trends
Use case success depends
on correctly aligning the
Identifies deviations from normal patterns
Unsupervised- Multivariate
Detecting unusual combinations of data points
detection type with your
data context.
Capturing Value with Anomaly Detection
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Key Business Considerations
Selecting the right anomaly detection approach requires more than just technical evaluation. Organizations must weigh
factors like speed, cost, and tolerance for error to ensure their monitoring strategy aligns with business goals.
Speed of Detection: How quickly do we need to be alerted?
E X AMPLE US E CA S E S
ALERT TIMING
M onitor data pipeline health, validate audience segment counts
Daily
The urgency of detection directly influences the complexity
and cost of your monitoring solution. As the demand for
Track top-line metrics, identify emerging trends
Hour ly
real-time insights increases, so does the need for more
W i th i n 5 m i nutes
Detect system downtime, flag potential fraud
advanced—and often less accessible—tools and infrastructure.
Return on Investment (ROI): What’s the tradeoff between cost, effort, and benefit?
The right anomaly detection strategy can yield substantial
•
Saves time otherwise spent manually identifying anomalie s
returns by improving efficiency, reducing risk, and enabling
R educes exposure to costly risks like fraud or lost revenu e
•
faster response times. Efficient monitoring isn’t just about
Enables faster, more informed responses to performance issues
•
saving time, it’s about protecting revenue and reputation.
Error Tolerance: What’s the cost of missing or misidentifying an anomaly?
ERROR TYPE
POTENTIAL IMPACT
M inor user friction, unnecessary course corrections
F al se Pos i t i ve (Type 1)
Different use cases have different risk profiles. Knowing what
kinds of errors are acceptable—or unacceptable—can guide
F al se Neg a t i ve (Type 2)
P otential data breaches, customer churn, lost revenue, or missed opportunities
the design of your detection approach.
Capturing Value with Anomaly Detection
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Data Quality Monitoring Concord in Action:
CLIENT:
Financial Services
Challenge:
Persistent data discrepancies between the customer data platform (CDP) and experimentation systems created confusion, with no clear path to diagnose the source of errors.
Approach:
Implemented rules-based univariate monitoring with daily automated alerts to track key metrics and highlight inconsistencies.
outcome:
The introduction of a daily dashboard enabled timely detection and resolution of data parity issues, restoring confidence in data integrity and accelerating troubleshooting.
Capturing Value with Anomaly Detection
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Top-Line Metric Monitoring Concord in Action:
CLIENT:
Financial Services
Challenge:
Analysts spent over 40 combined hours weekly performing manual checks on top-line performance metrics, detracting from strategic analysis.
Approach:
Deployed hourly statistical time series models to compare current performance against historical trends, automating anomaly detection and flagging deviations in near real time. Because missing a real drop in performance could be far more costly than chasing a false alarm, the system was designed to prioritize sensitivity and catch issues early.
outcome:
Monitoring effort was reduced by 75%, enabling more frequent and timely detection while allowing analysts to focus on high- value activities with clear, actionable alerts.
Capturing Value with Anomaly Detection
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Rapid Anomaly Alerting Concord in Action:
CLIENT:
Financial Services
Challenge:
Manual alert escalation processes delayed issue response by over an hour, increasing the risk of customer impact and attrition.
Approach:
Developed and deployed an LSTM-based forecasting model for anomaly detection at five-minute intervals, integrated with Slack for immediate alerting and escalation. In this high-stakes environment, speed mattered more than perfection—so the model favored quick detection, even if it meant surfacing new false positives.
outcome:
Monitoring effort was reduced by 75%, enabling more frequent and timely detection while allowing analysts to focus on high-value activities with clear, actionable alerts.
•
Reduced response times by 92%
•
Successfully detected all 3 critical incidents in a blind test
•
Identified over 10 minor incidents previously missed by manual monitoring
Capturing Value with Anomaly Detection
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Anomaly Detection is a Competitive Advantage
Monitoring is not just about ensuring uptime. It is about building trust in your data, reducing time spent on manual
diagnostics, and enabling teams to opportunities with speed and confidence. At Concord, we work with organizations to
implement scalable anomaly detection strategies that combine proven statistical techniques with advanced machine
learning—tailored to your business goals and data environment.
Whether you are focused on improving data quality, protecting critical systems, or identifying issues before they impact
operations or customers, our experts can help you design and execute the right solution.
Let’s Connect
Looking to stay ahead of data issues before they become business risks?
Reach out to learn how Concord can help you build a strong and proactive anomaly detection strategy.
Contact Us
Concord | concordusa.com
952-241-1090
info@concordusa.com
Capturing Value with Anomaly Detection
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