All Blogs
Back
Data Analysis

Identifying Patterns and Anomalies in Data Analysis

MarkovML
December 8, 2023
11
min read

Data will be the core factor determining an organization’s success in the next few years. As per McKinsey’s report ‘Data-driven enterprise of 2025’, rapidly accelerating technology advances and the need for data in every aspect of work have already created the pathway for data-driven enterprises.

Data pattern analysis is a fundamental aspect of deriving the right value from data and involves identifying recurring structures, trends, and behaviors within datasets. This process enables organizations and individuals to extract valuable insights from their data.

For instance, data pattern analysis has revolutionized the retail industry. By analyzing customer purchase history and demographics, retailers can identify patterns in product preferences, seasonal trends, and promotional effectiveness. This knowledge enables them to recommend items based on past searches, purchases, and trends.

Amazon, the global online retail giant, generated 29% of sales through its recommendation engine, which analyzes more than 150 million accounts.

Let's explore how data patterns and data analysis anomalies can be a game-changer for your business.

Understanding Data Patterns and Types

Data patterns refer to the consistent and recurring structures, relationships, or trends that can be identified within a dataset. Their significance lies in their capacity to reveal underlying information, guide decision-making, and predict future outcomes.

Data pattern recognition is crucial in various fields, including business, finance, science, and healthcare. Depending on the underlying phenomenon represented by the data, it can be simple, complex, subtle, or obvious.

Types of data patterns include:

  • Descriptive Patterns tell about “What happened?”.
  • Diagnostic Patterns answer the “Why did it happen?”.
  • Predictive Patterns help to predict “What could happen in the future?”.
  • Prescriptive Patterns provide answers to “What can we do next?”.
Data Analytics and its type
Source

Let's look at each in greater detail.

1. Descriptive Patterns

Descriptive patterns are the fundamental type of data patterns that give you a clear snapshot of what has happened. These patterns describe data characteristics, such as central tendency, dispersion, and distribution.

Source

Examples of descriptive patterns include average customer age, standard deviation of product sales, and frequency distribution of employee tenure.

2. Diagnostic Patterns

Diagnostic patterns includes trends comparison or movements and uncovering the correlations between particular factors that can lead to the result. These patterns identify the root causes of anomalies or deviations from the expected behavior and are crucial for troubleshooting and problem-solving.

Diagnostic Data Analytics
Source

Examples of diagnostic patterns include identifying the cause of machine downtime, diagnosing medical conditions based on symptom patterns, determining the source of network traffic spikes, and others. The objective is to find the defects or anomalies and facilitate improvement or quality control processes.

3. Predictive Patterns

Predictive patterns involve using historical data to forecast future trends or outcomes. They play a crucial role in decision-making by providing insights into what is likely to happen.

Source

Examples of this pattern include predicting stock prices or financial portfolios based on past performance, identifying potential fraud based on transaction patterns, and forecasting customer churn based on engagement metrics.

4. Prescriptive Patterns

The prescriptive patterns consider the possible factors in a scenario to provide actionable takeaways and next steps. These patterns suggest optimal actions or recommendations based on the data and are valuable for decision support and optimization.

Prescriptive Patterns
Source

Examples include recommending personalized product offerings based on customer preferences, suggesting optimal resource allocation for project management, and identifying optimal pricing strategies for maximizing revenue.

Identifying Patterns in Data

Now that you understand the various types of data patterns, let's see how data pattern analysis actually works.

Identifying Patterns in Data
Source

Several key techniques and methods can be employed to identify data patterns successfully, each offering a unique perspective on the underlying information within datasets. Some of these processes include:

1. Data Preprocessing - Data cleaning and transformation

Data preprocessing is the initial stage of any pattern identification to ensure data quality and consistency. It involves data cleaning and cleansing methods to remove errors, inconsistencies, and missing values.

Data transformation may also be applied to standardize or normalize data formats for better analysis. This involves converting data into a usable format, making it ideal for further analytics and specific reporting.

2. Data Visualization - Utilizing charts, graphs, and diagrams

Next comes data visualization, which makes use of standardized numerical data to convert it into visual representations, such as charts, graphs, diagrams, and more. These help to make data pattern analysis simple and help identify patterns, trends, and outliers that might not be immediately apparent in raw data.

Steps in Identifying Data patterns
Source

Data visualization techniques include line charts to track stock price trends, heatmaps to reveal website user activity, and scatter plots to explore the relationship between two variables in a scientific study and more.

3. Statistical Methods - Descriptive and inferential statistics

Statistical methods provide a rigorous framework for pattern identification and analysis. They often come in two types:

1. Descriptive statistics

These statistics offer a basic understanding of the data distribution and provide a summary of the main characteristics of a dataset, such as mean, median, and standard deviation.  For instance, in demographic analysis, calculating the average age of a population can provide a central tendency measure.

2. Inferential statistics

This allows you to draw conclusions and predict from data samples. Hypothesis testing and regression analysis are common examples. In a clinical trial, inferential statistics are used to determine whether a new drug is effective based on a sample of participants.

4. Machine Learning Techniques - Clustering, classification, and regression

These techniques allow you to use machine learning algorithms to uncover trends and make data-driven predictions based on data characteristics. Among its many applications, clustering, classification, and regression are the most common types of patterns used.

1. Clustering

It is used to group similar data points. For instance, in customer segmentation, clustering can group customers with similar purchasing behaviors.

2. Classification

It assigns data points to predefined categories or classes. In email spam detection, classification algorithms can categorize emails as spam or not.

3. Regression models

They predict a continuous value based on input data. In real estate, regression models can predict property prices based on features like square footage and location.

Recognizing Anomalies in Data

The crux of the entire exercise comes from data analysis anomalies. Data anomalies are irregularities or deviations in a dataset that do not conform to the expected patterns or behaviors. They can be caused by errors in data collection, measurement, or recording, and their identification is crucial for maintaining data integrity and ensuring that accurate insights are drawn from the data.

Data anomalies are primarily of three types:

1. Point anomalies

These are Isolated data points marked differently from the surrounding data. They are often easily identifiable and can be caused by data entry errors or sensor malfunctions.

2. Contextual anomalies

Data points that deviate from the expected behavior based on specific contextual information, such as time, location, or user behavior. For instance, a sudden spike in network traffic at an atypical time might indicate wrongful activities.

3. Collective anomalies

These anomalies are groups of data points that exhibit abnormal patterns altogether. They may suggest underlying trends or relationships that are not apparent when examining individual data points.

Techniques to Identify Data Anomalies

The following techniques can be employed to identify data anomalies:

1. Statistical methods

Statistical methods involve calculating summary statistics, such as mean, standard deviation, and z-scores, to identify data points that deviate significantly from the expected distribution. These help to identify outliers that fall outside the expected ranges of a graph.

2. Machine learning-based approaches

Machine learning algorithms can be trained to distinguish between normal and abnormal patterns in data. They can detect complex anomalies that may not be readily apparent with statistical methods.

These include isolation forests, one-class SVM, and deep learning methods, which can be trained to distinguish between normal and abnormal data points based on features and patterns within the dataset.

3. Visualization for anomaly identification

Data visualization techniques, such as scatter plots, box plots, and heat maps, can help visualize data distributions and anomalies. Visual inspection of data can reveal patterns that may not be apparent through numerical analysis alone.

Case Study: Anomaly Detection in Financial Transactions

Background

In the world of finance, anomaly detection plays a pivotal role in safeguarding against fraudulent activities. As per Juniper Research, online payment frauds in e-commerce, airline ticketing, money transfer, credit cards, phone banking, and other banking services can cumulatively be around $200 billion between 2020 and 2024.

This is because fraudsters have started using highly sophisticated methods to collect sensitive personal details, hack into vendor and institutional data centers, and gain access to information.

Problem Statement

Fraudulent transactions often deviate from established patterns regarding transaction amount, location, time, or other relevant factors. Identifying these anomalies in real-time is essential to prevent fraudulent activities and minimize financial losses.

Solution

Financial institutions implement anomaly detection systems by integrating them with their transaction processing systems. These systems collect transaction data, apply anomaly detection algorithms, and generate alerts for suspicious transactions that require further investigation.

Machine learning algorithms, mainly unsupervised learning techniques like clustering and outlier detection, are employed to identify anomalous transactions. These algorithms can effectively capture complex patterns and relationships within transaction data, revealing anomalies that are not readily apparent with traditional rule-based systems.

Outcome

Anomaly detection systems have significantly reduced fraudulent activities in the financial sector. These include risk models, algorithms, human actions, and technology collaboration to identify any deviations or anomalies in the system.

As per findings, the machine learning model alone can reduce unexpected losses in payment channels by 15%, and expected losses can be reduced by 52%. Since it uses advanced self-learning techniques, ML algorithms have also helped to reduce the rate of false positives to 0.4%. This means that the alerts detected by ML algorithms in real-time are accurate, helping businesses reduce fraud and risks occurring in the financial industry.

Conclusion

As you have seen, data pattern analysis is crucial to help organizations make data-driven decisions and derive value from their data. It can help you gain a deeper understanding of customers, products, processes, and networks by effectively identifying patterns and anomalies.

As the volume and complexity of data continue to grow, the role of data analysis and pattern recognition will become increasingly crucial for organizations across all industries.

If you are struggling with your data management processes, check out Markov, a No-Code AI platform that allows you to transition from model-centric AI to data-centric AI.  With features like No-Code Auto EDA, Collaborative Reporting, and Intelligent Data Catalog, you can seamlessly organize your organizational data into ML workflows. So, use AI-based data intelligence and let the platform do the heavy lifting for you!

For more details, book a demo now!

FAQs

1. How do you identify data anomalies?

To identify anomalies in data, data scientists can analyze data output using different statistical techniques, such as mean, median, quantiles, and others. This can be done using various data visualization and exploratory analytics tools.

2. What is pattern and anomaly detection?

Anomaly detection is the process of data pattern analysis to identify faults, rare events, suspicious activities, and other deviations from expected or standard behavior. Recognizing anomalies empowers organizations to make informed decisions, predict future trends, and improve strategic planning.

3. What are the common types of data anomalies?

The most common type of data anomalies are:

  • Point anomalies - isolated data points that deviate significantly from the surrounding data
  • Contextual anomalies - deviate from the expected behavior based on specific contextual information
  • Collective anomalies - groups of data points that exhibit abnormal patterns together.

From Data To GenAI Faster.

Easily Integrate GenAI into Your Enterprise.
Book a Demo
AUTHOR:
MarkovML

A data science and AI thought-leader

Create, Discover, and Collaborate on ML

Expand your network, attend insightful events

Join Our Community