Why is Synthetic Data Gaining Popularity in Adoption of Artificial Intelligence and Machine Learning Systems
Though synthetic data has its limitations, it is believed to have the potential to democratize artificial intelligence and machine learning. It is already being used in several fast moving industries to accelerate testing as well as adoption of ML and AI algorithms.
Data continues to captivate the interest of technologists, analysts, developers as well as entrepreneurs alike as executives, applications, tools and frameworks constantly demand data for business insights and decision making, responses to market conditions, evaluation of software and scripts, standardization and benchmarking, computing various metrics, irrespective of domain and industry. It is important to note that the software applications and tools can be accredited only when used in conjunction with data. With ever increasing volumes of business data, enterprises try to make the most of big data and of late even fast data. However, they not only face challenges to access and extract what they need, they also face difficulties in legal compliance with privacy laws as well as other security related concerns.
Over last decade, different segments of business have been disrupted with technological advancement, specifically the AI and ML suite and it has boosted the data demand even further. Just to recapitulate, in my last article, I had explained that Artificial Intelligence and its entire subset of technologies like Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning and even Robotics need data to train the algorithms. So can Synthetic Data be considered as the proposition to address the massive data requirement, and train the algorithms in really quick time? Though synthetic data has its drawbacks, it is believed to have the potential to democratize artificial intelligence and machine learning. It is already being used in healthcare, banking, crime detection, manufacturing, telecom, retail and several other fast moving industries to accelerate testing as well as adoption of ML and AI algorithms.
Data that is artificially manufactured by a computer rather than measured and collected from real-world situations, is called synthetic data. The data is anonymized and created on the basis of user-specified parameters so that it’s as accurate as possible in comparison to the properties of data from real-world incidents or events.
There are two methods to create synthetic data. One method is to use real-world data but strip the identifying aspects such as names, emails, social security numbers and addresses from the data set so that it is anonymized. The other way is to have a generative model that can learn from real data, to create a data set that closely resembles the properties of authentic data. As technology keeps improving, the gap between synthetic data and real data diminishes.
Synthetic data comes handy and is useful in many situations. Similar to how a research scientist might use rats and guinea pigs instead of human clinical trials to complete experiments at low risk, data scientists can leverage synthetic data to minimize effort, time, cost and risk. In some cases, there isn’t a sufficiently large data set available to train a machine learning algorithm effectively for every possible scenario, so creating a data set can ensure comprehensive training. In other cases, real-world data cannot be used for testing, evaluation, training or quality-assurance purposes due to privacy concerns, because the data is considered sensitive or it is for a highly regulated and controlled industry.
Benefits of Synthetic Data
Artificial Intelligence or Machine Learning algorithms and Deep Learning machines that are required to help solve complex and challenging issues thrive on huge data sets. Companies such as Google, Apple, Microsoft and Amazon have always had a competitive advantage due to the massive amount of data they generate daily as part of their business routine. However, Synthetic data provides opportunities to organizations of every size and resource levels to also draw benefits from learning that is powered by deep data sets which ultimately can democratize machine learning.
In many cases, it is more efficient and cost-effective to create synthetic data rather than to collect real-world data. Synthetic data can also be created on demand, based on specifications rather than waiting to collect data once it occurs in reality. Synthetic data can also complement real-world data so that testing can occur for every imaginable parameter for which even a valid example may not exist in the real data set. This allows organizations to accelerate the testing of system performance and training of new systems.
The limitations for using real data for learning and testing are reduced when using fabricated data sets. Recent research suggests that it is possible to get the same results using synthetic data as you would with authentic data sets.
Drawbacks of Synthetic Data
Generating data that closely resembles the actual data may be enticing for the business community. However, it can be challenging to create high-quality synthetic data especially if the system is complex. It’s essential for the generative model creating the synthetic data to be of excellent quality or the data it generates will be adversely impacted. If synthetic data isn’t nearly identical to a real-world data set, it is likely to compromise the quality of decision-making that is being done based on the data. Even if synthetic data is really good, it is still a replica of specific properties of a real data set. A model looks for trends to replicate, so some of the random behaviors might be absent.
Synthetic data is considered as a useful tool for testing the scalability of algorithms and the performance of new software applications. It may be tougher to use synthetic data for research purposes on full-fledged fashion, as it only aims at reproducing specific properties of the data. Producing quality synthetic data is complicated because the more complex the system, the more difficult it is to keep track of all the features that need to be similar to real data. Therefore, creating synthetic data of good quality maybe very expensive.
Applications of Synthetic Data
For scenarios wherein privacy concerns are pivotal such as in the life sciences, healthcare and banking industries or an enormous data set is required to train machine learning algorithms, synthetic data sets can accelerate progress. Here are just a few applications of synthetic data:
1. Record-level synthetic data is used from healthcare organizations to inform care protocols while protecting patient confidentiality. Simulated X-rays are combined with real X-rays to train AI algorithms to identify conditions.
2. In banking fraudulent activity detection systems are being tested and trained without exposing personal financial records.
3. DevOps teams use synthetic data to test software and ensure quality.
Even in food and beverage industry, machine learning algorithms are often trained with synthetic data. Common use case is to identify and segregate poor quality ingredient.
4. Uber and Waymo tested autonomous vehicles by driving on real roads plus simulated streets augmented by synthetic data.
Synthetic data is definitely an important tool to intensify machine learning algorithms when real data is too expensive to collect, inaccessible due to privacy concerns or incomplete in certain ways. With privacy regulations getting tougher, it is becoming essential for data owners to prepare themselves for restricted access to private data. As big data tools become increasingly popular and widespread, an investment in simulating real data is critical. Whether it is to build an in-house generator or pay for ad-hoc development, enterprises will have to incorporate these new veracities in their strategic planning to help themselves embrace Artificial Intelligence and other exponential technologies to their business advantage.