Maintaining streamlined and actionable enterprise data sets demands systematic updates and control over your digital assets. Even though modern data stacks ensure automated cleansing and formatting consistency, data engineers must look out for data anomalies – deviations stemming from the insertion, deletion, and updates of data tables. After all, the cost of poor data is immense, with an average loss estimated at $12.9M, totaling up to $3.1 trillion per year across all US organizations.
The main causes are considered to be productivity impact, system outages, and high maintenance costs. Many of these issues can be fixed or even prevented by timely identifying and addressing data anomalies.
So, how do data anomalies appear, and what anomaly detection techniques are best to reveal and manage them effectively? Read on, and you’ll find out how to improve your data quality, ensure compliance, and deal with anomalous data.
What Is Data Anomaly? Why Must Enterprise Data Teams Detect Them?
Anomalies refer to inconsistencies in data points or abnormal data behavior compared to the rest of the data set. The anomalies can emerge due to intentional (event-driven) or unintentional (flawed data collection, input errors) causes. Recently, we’ve touched on data quality issues management and shared some highly efficient practices that can help you with it.
Data Anomaly Detection Is a New Normal in Data Science
Data anomaly detection is vital as it helps data teams eliminate distortion in their analytics, catch data issues in a timely manner, prevent overspend, and avoid misleading forecasting for business activities.
3 Types of Most Typical Data Anomalies
Regarding the specific forms of deviation, it makes sense to categorize data anomalies into three types:
- Point anomalies, aka outliers. Outliers are data points that are starkly different from baseline values. Abnormal or distorted data like this is a red flag, highlighting the malfunctioning processing or possible security breaches.
- Contextual anomalies. This anomalous data appears inconsistent or redundant for a particular context, although it might not be a sure outlier. However, when viewed in a specific scenario, it can point out the abnormal activity that falls out of trends.
- Collective anomalies. In contrast to anomalous data points, collective data anomalies constitute arrays of data sets that deviate from the baseline requirements.
Causes of Typical Data Anomalies
One of the most common problems with datasets is that you can have similar records on the same entity (employee, customer, or vendor) stored in different datasets. Therefore, when you modify or delete them, it might affect tables with interdependent data.
Eventually, overlooking such functional dependencies may cause the corresponding data anomalies.
These data anomalies occur when data must be incorporated into an existing set. If the new record doesn’t contain a primary key – a unique identifier specifying the tuple in relational databases – it can't be added to a table.
Another case of such data anomalies stems from redundancy when the same data appears multiple times in the same table. So, when users add new records to the same entity, it can lead to duplicates and disrupt the referential connections.
These data anomalies denote an unintended deletion of correlated records that belong to different tables. The removed record can contain foreign keys assigned that allow external tuples to refer to it. Therefore, when it gets removed, correlated data arrays become unactionable or inconsistent, which hurts data integrity.
Deletion anomalies can also occur within the same table if removed data underlies the downstream calculations.
Data scientists and engineers also have to deal with update anomalies when updating a single record leads to changes in multiple tuples and columns. Such a snowball can lead to unexpected distortion and misleading insights if the data team doesn't grasp actual functional dependencies.
Another textbook example of an update anomaly is when you store customer addresses in different records. So, if you update it in one of the tuples, there will be several addresses saved for the same entity, which can result in inconsistency in data analysis.
The above-mentioned data anomalies, however, can be successfully solved through detection and normalization practices. The data normalization process implies thoughtful data architecture design by separating data sets into smaller and non-redundant tables. Such an approach allows data specialists to logically configure reference connections between co-dependent records by harnessing specified primary and foreign keys.
5 Data Anomaly Detection Practices: How to Identify Anomalous Instances in the Enterprise Database
Anomaly Detection as a Part of Enterprise Data Strategy
Since data anomalies are deviations, it means that a certain pattern and “norm” must exist as a reference. So, before implementing more technical data anomaly detection practices, companies should first:
- Establish data quality standards and data policy.
- Implement data monitoring tools and practices and identify data patterns.
- Analyze and predict the possible data anomalies, prioritize them according to their risk level, develop the anomaly scoring system, and have standardized issue-solving procedures in place.
- Set up a regular process to review and analyze the most typical, recurring data anomalies – as well as one-time non-standard ones that stand out – as they might signal underlying issues that need to be addressed strategically.
Anomaly Detection on a Tactical Level
The actual detection of irregularities and data outliers can be successfully managed through machine learning practices of supervised and unsupervised data anomaly detection.
Supervised techniques operate upon the labeled data to train detection algorithms to distinguish outliers among normal data points. Supervised detection engines can ensure high accuracy in recognizing determined anomalies, but they often fail to detect unknown and non-systematic ones.
Unsupervised anomaly detection doesn’t depend on labeled data. Instead, it assesses statistical fluctuations and measures value gaps to identify the outliers among the rest of the data. Unsupervised algorithms utilize density-based clusterization, which allows them to spot and flag data outliers that exist separately from their nearest neighbors grouped in clusters. This type of data anomaly detection is rather practical for maintaining unbalanced and dynamically changing data sets as it helps to identify non-systematic data anomalies.
In a nutshell, implementing a certain type of data anomaly detection algorithm is always case-specific. It can significantly depend on your data architecture, the integrability of machine learning modules, and their dependency on feedback from the operator.
5 Most Common Data Anomaly Detection Techniques
As an example, let’s review the 5 most common data anomaly detection techniques that help classify normal data and reveal outliers.
1. Isolation Forest
An Isolation Forest is an unsupervised classification rule learning algorithm. Its outlier detection principle is based on analyzing decision trees built around each data point. On average, normal data points have more dense structural connections in the decision tree. As a result, it is harder for the machine algorithm to observe them as isolated ones.
And vice versa: data anomalies can be isolated more easily as they typically stay aside from data clusters. The system flags these poorly integrated data points and compares them to the normal ones. After evaluating how far outliers are from neighbor data points and comparing the measurement to a threshold value, the algorithm assigns an anomaly score.
2. Local Outlier Factor (LOF)
The local outlier factor estimates the local density of an entity by comparing it to those of its neighbors. Therefore, if it detects a lower density of interconnected data points, it concludes that there’s an outlier placed in the area.
The LOF anomaly score is calculated based on the average ratio between the local and global densities. LOF belongs to unsupervised outlier detection techniques and utilizes principles similar to the k-NN Algorithm.
3. Nearest-Neighbour k-NN Algorithm
Nearest-neighbour k-NN data anomaly detection is mainly utilized in finance data computing to recognize and highlight fraudulent transactions. It is a fairly strict and definite classification algorithm running on labeled data.
When it receives unlabeled data, it gets processed in a two-step pattern. First, the algorithm identifies the k-closest neighbor items that contain training data. Secondly, it estimates k-nearest neighbors to conclude how to classify new data points.
The k-NN’s decision on which item is closer comes from the measuring Euclidean distance in the case of continuous data or Hamming distance when comparing discrete data i. The latter is used for measurements involving 2 text strings.
The k-NN data anomaly detection is universally applied in environments with high-velocity updates and requests. They ensure robust and operative isolation and alarming on suspicious observations.
4. Support Vector Machines (SVM)
Another noteworthy example of a supervised anomaly detection mean is a support vector machine. This algorithm helps to operationalize and solidify data classification.
It utilizes a hyperplane to recognize and separate data anomalies from normal instances. The hyperplane is a linear formula derived from the assessment and generalization of labeled learning data.
Acting upon that hyperplane, SVM confines items that fall under normal data behavior criteria in the learning area and separates abnormal data points at the same time.
5. Neural Networks Algorithms
Neural networks stand out because they can recognize data anomalies based on how they don’t fit into particular data patterns. These cutting-edge technologies learn and act upon classification, regression, and prediction operations from a holistic view of how data is structured and processed in real time.
The observation obtained from time series data allows the neural network to identify dependencies that persist across multiple steps and detect the features that historical data affects.
Although neural engines can identify many data anomalies that linear programs can not, they often require greater computing resources. Otherwise, the engine will take more time to process broad databases. This is where cloud-based data observability platforms can save your time and cut data maintenance costs.
How Revefi Transforms for Data Anomaly Detection and Data Observability
Revefi Data Operations Cloud provides more than the traditional data observability value, helping data teams quickly connect the dots across data quality, usage, performance, and spend. Predictive algorithms update you on data anomalies and errors before they affect co-dependent data assets or skew future calculations.
Thanks to Revefi, you can:
- Automatically deploy monitors with no configuration or manual setup needed.
- Proactively prevent data issues.
- Get to the root cause 5x faster compared to manual debugging.
- Ensure the all-time usability of valuable data assets.
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