Step Into SAS Data Maker: A Practical First Look
Accumulating real-world data for model development and system testing is often time-consuming, costly, and fraught with challenges especially when privacy concerns, limited representation among sensitive groups, or the need for rigorous testing come into play. Synthetic data offers a strategic alternative by replicating the statistical properties and patterns of real datasets, enabling efficient, ethical, and scalable data generation. That’s exactly what SAS Data Maker brings to the table. It’s a web-based application purpose-built for synthetic data generation from structured, tabular datasets. It empowers users to produce high-quality synthetic data through an intuitive low-code/no-code interface, making it accessible to both technical and non-technical users. SAS Data Maker empowers teams to work faster and smarter. By removing dependencies on live data and lengthy approval processes, it accelerates innovation and ensures data security remains intact. In short, it’s not just a tool, it’s an enabler of secure, scalable, and agile analytics.
As part of SAS’s broader Generative AI (GenAI) initiative, SAS Data Maker supports key stages of the AI lifecycle enhancing productivity, accelerating innovation, and democratizing analytics across organizations.
The Data Problem
At first glance, the rise of artificial intelligence (AI) seems perfectly aligned with the era of big data, where massive volumes of information are being generated every second. However, in practice, AI has also created a paradox of data scarcity.
Real-world data is gathered by actual systems, such as medical tests, banking transactions, or web server logs. However, this data could be limited in size, hard to access, and may not represent the complete spectrum of possible values or behaviors, making it challenging to manage and analyze. Currently, the data problem is often around suitability and not essentially the quantity. This is because modern AI models especially advanced machine learning and deep learning algorithms do not just require large amounts of data, but also high-quality, well-labelled, and domain-specific datasets.
Challenges in Gathering Suitable Data
Data plays a critical role in the development of AI applications. However, collecting and accurately annotating real data can be costly, especially on a large scale. Additionally, real-world data can be messy, requiring significant time for cleaning or feature extraction or both. There are also instances where you have enough data, but it may not be directly relevant to the problem you are addressing. Another challenge is dealing with imbalanced data, where the event of interest is often rare, making it more difficult to train effective models. The quick availability of data presents a challenge due to strict privacy regulations, as organizations must ensure compliance with laws governing data collection, storage, and usage. This leads to increased data security risks, limitations on data analytics, and restrictions on cross-border data transfers.
The Solution: Synthetic Data
While real-world data is typically collected through direct interactions with individuals or business systems, synthetic data is generated by AI algorithms that create entirely new and artificial data points.
In simple terms, synthetic data is algorithmically produced data that closely mirrors the statistical characteristics of real data without replicating any actual records. It can be created on demand through self-service methods, using rules or algorithms derived from a smaller sample of real data. This ensures the resulting data set maintains statistical fidelity while protecting sensitive information.
With synthetic data, organizations can generate realistic representations of financial transactions, medical records, or customer behavior patterns. This emerging technology provides a safe and scalable way to train and test models, preserve privacy, and bridge data gaps where real-world data is limited or inaccessible. Cross-border data sharing is often complicated by privacy regulations, legal restrictions, and organizational security concerns. Synthetic data provides a powerful solution to this challenge.
The SAS Data Maker
Leverage SAS Data Maker, a low-code/no-code interface to produce synthetic data from structured, tabular, and non-temporal real datasets, allowing you to accomplish your analysis goals more efficiently.
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SAS Data Maker Capabilities
- Generate data on demand- can generate data dynamically, as needed, for various purposes such as testing, analysis, or model training.
- Replicate data characteristics- mirrors the underlying patterns, relationships, and statistical properties found in real data sets. This feature ensures that the generated data behaves similarly to the original data in terms of key statistical attributes, which is essential for testing, analysis, and model training
- Evaluate synthetic data quality- offers tools to evaluate the quality of synthetic data through various visual evaluation metrics. This is a crucial step to ensure that the synthetic data generated is not only realistic but also suitable for its intended use.
SAS Data Maker Process
The SAS Data Maker process consists of three main phases: Plan, Prepare, and Produce.
- In the Plan phase, the focus is on onboarding the data, selecting variables, and pre-processing the original data to set up a solid foundation for generating synthetic data. Each of these steps is crucial for ensuring the synthetic data aligns with the intended use cases and mimics the original dataset effectively.
- The Prepare phase is focused on configuring the generation parameters and training the models that will be used to produce the synthetic data. This step is where the system learns how to generate data that mimics the original data set, based on the configured generation parameters.
- The Produce phase is the final phase where the synthetic data is generated, evaluated, and validated to ensure its quality and usefulness for the intended applications.
But why just read about it when we can see the magic unfold? SAS Data Maker is slated to hit the stage soon, and when it does, it’s set to redefine how teams create, manage, and scale synthetic data. But why wait for the launch to imagine its impact? Up next, let’s walk through a sneak peek demo and get a glimpse of what’s coming. Trust me, this is one tool you’ll want to keep on your radar.
Additional Resources
- A Checklist for Assessing your Synthetic Data
- Working with synthetic data? Ask these 6 questions first
- 5 myths about synthetic data – and what’s actually true
- A Human Generated Introduction to Generative AI, Part 1: Synthetic Data Generation
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