The path to autonomous vehicles is accelerating. Medical institutions are using leading edge AI to improve patient outcomes. And financial institutions are redefining the way they control risk. From how we work to how we live, data continues to touch every aspect of human existence. And this is just the beginning.
But the data-driven AI explosion doesn’t come without real challenges. Finding, validating and sometimes generating data for machine learning is a complex, often inaccurate task. In fact, Gartner estimates that, by 2022, 85% of AI projects will deliver incorrect outcomes due to biased data.
How do data scientists get the data they need, at the scale they need, without compromising on quality, balance and accuracy? The answer: synthetic data.
What is synthetic data?
Synthetic data is data that you can create at any scale, whenever and wherever you need it. Crucially, synthetic data mirrors the balance and composition of real data, making it ideal for fueling machine learning models.
What makes synthetic data special is that data scientists, developers and engineers are in complete control. There’s no need to put your faith in unreliable, incomplete data, or struggle to find enough data for machine learning at the scale you need. Just create it for yourself.
Advantages of synthetic data
For data scientists, the real or synthetic nature of data is irrelevant. What really matters are the characteristics and patterns inside the data – its quality, balance and bias. Synthetic data allows you to optimize and enrich your data, unlocking several key benefits.
Increased data quality
Real-world data isn’t just hard and expensive to source. It’s also prone to errors, inaccuracies and bias that can severely impact the quality of your machine learning model.
With synthetic data generation, you get increased confidence in data quality, variety and balance. From auto-completing missing values to automated labeling, it’s a way to dramatically increase the reliability and accuracy of your data and, in turn, the accuracy of your predictions.
Fueling the machine learning economy takes a huge amount of data. Few data scientists can access exactly the data they lack on the scale they need to test and train powerful predictive models. Synthetic data can close that gap.
Many data scientists supplement their real-world records with synthetic data, rapidly scaling up existing data – or just the relevant subsets of this data – to create more meaningful observations and trends.
Finally, synthetic data is refreshingly easy to generate. With real-world data, developers need to:
- Ensure privacy and confidentiality
- Label data in a uniform way
- Filter out duplicate data
- Remove erroneous records
- Collate data from multiple sources, often in multiple formats
With synthetic data, you can control how the resulting data is structured, formatted and labeled. That means a ready-to-use source of high-quality, dependable data is just a few clicks away.
Final thoughts: synthetic data unlocks new possibilities
Compared to real-world data, synthetic data generation is faster, more flexible, and more scalable. By adjusting parameters, it can also be an effective way to model and generate data that doesn’t exist out in the real world.
In finance, anticipating markets and trends is vital. Modeling a potential financial crisis could allow you to make robust plans and forecasts long before they are needed.
Synthetic data allows data scientists to feed machine learning models with data to represent any situation. Synthetic test data can reflect ‘what if’ scenarios, making it an ideal way to test a hypothesis or model multiple outcomes.
Yes, synthetic data is a more accurate and scalable replacement for real-world records. But it’s bigger than that. Synthetic data gives data scientists a way to do new, innovative things that are impossible with real-world data alone, feeding the models that will affect the way we all live in our data-driven future.