Fake data – computer-generated synthetic data that emulates the characteristics of real-life datasets – ensures data privacy for testing/training purposes.
Table of Contents
What is Fake Data?
Why do Organizations Generate Fake Data?
How do Data Teams Generate Fake Data?
Fake Data Generation Use Cases
Fake Data Generation Challenges
Generate Fake Data Based on Business Entities
What is Fake Data?
Fake data, or synthetic data, is data that is generated by computer software yet emulates the mathematical and statistical characteristics – alongside the structure and format – of real-world datasets. Fake data is generated to avoid the limitations associated with using actual sensitive data – like data that contains PII, medical records, financial information and more. There are dedicated synthetic data creation solutions specifically designed to generate fake data to train ML (Machine Learning) or AI (Artificial Intelligence) models, test software as its being developed, or for other purposes that require vast amounts of data drawn from operational, production, or sensitive sources.
Why do Organizations Generate Fake Data?
Enterprises need to generate fake data to effectively train new AI/ML models, while still meeting the requirements of strict data privacy and security regulations, and without overrunning budgets. This is easier said than done, since collecting and labelling a synthetic dataset, that can easily comprise millions of objects, is time consuming and expensive – if it’s technically or legally possible at all.
To reduce costs and avoid legal and regulatory liability, many data teams choose to generate fake data. There is no oversight or liability associated with fake credit cards numbers, fake medical records, or fake PII. As long as the fake data is true-to-life, organizations can use it freely. What’s more, fake data can sometimes produce better results than real data, because the solutions that generate fake data can be adjusted to compensate for bias inherent in real data. For example, a fake dataset can artificially enhance diversity by encompassing rare, but realistic, use cases that would be hard to find in the real world.
Solutions that generate fake data can markedly assist IT departments. The reason? Synthetic (fake) data, that is high-quality, unbiased, balanced and accurately representative of patterns in the original dataset, can offer:
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Efficiency
Generating and using fake data is actually often considered easier than using real data. Fake data can reduce inaccuracies, duplicates, and errors, while enhancing uniformity. After the time-consuming task of collecting real-world or production data, IT teams still need to go through the resource-intensive process of formatting and unifying datasets from their original and disparate formats, and de-duping and filtering them for errors. -
Scalability
Enterprises generate fake data to train AI/ML models and test software in its development lifecycle. Even if a company can handle the monetary and resource investments, sometimes suitably large datasets simply don't exist. Generating fake data is a cost-effective way to reach achieve massive data input, while broadening testing scale. -
Quality
Even if real-world data meets the quality and data masking standard mandated by the company, sometimes there just isn’t enough of it to go around. Models using fake data can suggest next best actions and predict business outcomes with a fair degree of accuracy and quality, by being able to label data types automatically, and complete missing values.
How do Data Teams Generate Fake Data?
Data teams generate fake data by using:
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Intelligent rules
This technique leverages pre-defined business rules as guidelines for fake data generation. By way of example, these rules could encompass minimum, maximum, or average values for the range of ages in an “age” attribute. Intelligent rules examine the relationships between elements, checking variation in the range of each attribute and enabling verification of the dataset’s relational integrity. -
Agent-based modeling
This method generates fake data using agents, which emulate actions or reactions of groups or individuals. This technique is notably helpful in use cases with highly complex interdependencies, like securities trading. -
Statistical models
This technique minimizes bias in fake datasets by deciding which distributions to retain and which to discard. -
AI-based data generators
AI-based data generators create fake data by training the AI model on existing real-life data and then generating new samples that imitate the characteristics, patterns, and structures of the original data. -
GANs
Generative Adversarial Networks (GANs) generate fake data using a data generator (which creates the data) and a discriminator (which seeks out flaws in the data created).
Organizations should determine the best method for generating fake data according to each use case, the processing power of the generator (test data generation, for example), data governance tools, and a thorough cost-benefit analysis.
Fake Data Generation Use Cases
If a company generates fake data correctly, it can be applied to a broad range of use cases, including:
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AI/ML training – Supplements or replacing real data to enable algorithms to train on unusual patterns or events.
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Pre-release application testing – Tests new software or updates when production data is lacking or creating huge datasets for load and performance testing.
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Data augmentation – Creates “filler data” for a given dataset, when real-world data volume is insufficient or non-existent for the task at hand. In some cases, internal corporate policies prevent development, testing, and analytical teams from accessing production data, to maximize compliance with privacy regulations.
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Data security – Protects sensitive data while still retaining a dataset’s usefulness for testing or training.
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Data governance – Validates models by using data points not usually found in real-life data, thus reducing bias.
Fake Data Generation Challenges
Fake data is highly useful, yet there are significant challenges to adopting tools that generate fake data and to creating fake data datasets themselves.
First, it is often difficult to statistically validate whether synthetic dataset is true to the structure and format of the dataset it emulates. Further, whenever a fake data value is used, it must reference a valid, existing value across all systems, making "referential integrity" a primary challenge.
Second, tools that generate fake data can consume significant time and resources to produce viable synthetic datasets.
Third, and most importantly, mistakes in generating fake data – even if that generation was intended to protect sensitive data – can actually lead to exposure of that data. For example, if outstanding or unusual values are effectively used as clues by a hacker, re-identification of anonymized data is possible.
Generate Fake Data Based on Business Entities
Advanced synthetic data generation solutions leverage entity-based data masking technology, where business entities are customers, devices, orders, or anything else important to the business. All the data for each instance of a business entity is managed and stored in an individually encrypted Micro-Database™. Applied together with intelligent business rules and an AI-based data generator, entity-based synthetic data generation delivers more balanced, more realistic fake datasets that are better able to support operational and analytical use cases.
