Digitization, bulk document scanning, corporate document scanning services – these are terms heard often in this era of advanced digital technologies that are transforming the way businesses function. AI and automation are the future. Organizations have become big data driven and valuable insights can be gained from large data sets that have to be extracted and utilized effectively. For this, specialized software and scripts are developed that calculate the relevant metrics on the raw data. This software must be tested, the results authenticated, performances benchmarked, and its capability to identify and correct erratic data determined. To test the software, you need proper data to run the system. However, using actual data is not advisable because of the privacy laws and legal hurdles involved. The solution lies in synthetic data, or data that is artificially created based on real data.
How can you create synthetic data?
A reproductive workload model is used to create synthetic data from the original dataset, whereby synthetic copies that closely resemble the original data are produced. This generative or reproductive model can learn from real datasets and ensure that the output accurately resembles the original real data. Anonymity is a major benefit of synthetic data because personal information is removed, and the data cannot be traced back to the original owner. This helps avoid copyright and privacy infringements.
www.riaktr.com provides a good example of how synthetic data is created. They take the example of Call Detail Records or CDRs – the data record produced by a telephone that documents the details of a phone call or text message. If the objective is to reproduce the same telecom activity level over time as a real customer base,
- The real distribution on CDRs is observed
- An artificial customer base is created
- The calls from these customers are simulated with time stamps, respecting the observed distribution, all other fields in the CDR are randomly generated.
So, what are the actual applications of synthetic data?
- It can be used to examine current system performances, and to train new systems/scenarios
- Business owners can do simulations that allow them to predict consumer behavior and implement winning strategies according to the requirements.
- Synthetic data can be used to develop and improve big data tools and advanced analytics applications that are of great importance today to extract valuable insights from large datasets. For instance, it can be used for visualization purposes and to test the scalability and robustness of new algorithms.
- Availability of synthetic data will help businesses with sensitive data use crowdsourcing competition platforms such as Kaggle that require companies to publish data sets if they want to use the platform.
David Schatsky, managing director at Deloitte LLP, and Rameeta Chauhan, a senior analyst at the firm suggest that synthetic data can be used to train machine learning and AI models. Synthetic data could prove valuable in situations where data is sensitive, restricted, or subject to regulatory compliance. Synthetic data could be used:
- In computer vision technology that helps machines recognize faces or identify objects in digital photos. Researchers in this field use a 3D-digital model of a human face to generate as many permutations of facial expressions or eye positions as they want. This can be done quickly and cheaply.
- To train robots to carry out agile and complex tasks such as picking up objects or manipulating objects of different sizes and shapes. For this, a human model is used to demonstrate the action. The entire set of actions is digitally captured so that the images can be easily manipulated. The digital model of human behavior can be reproduced in innumerable ways with different backgrounds, at different angles and so on. This will help avoid having a human do the action repeatedly.
Neuromation – a Synthetic Data Platform
Neuromation is a distributed synthetic data platform for deep learning applications. It uses three key AI elements – talent, data sets and computing power. The Neuromation platform offers an exchange and ecosystem where participants can either contribute or buy the components of AI system like models, real data and synthetic data generators. It supports synthetic data sets creation that yield compelling results. Use of synthetic data in machine learning helps to lower cost of AI development and opens the door for widespread AI adoption.
For any organization, efficient use of data with the support of data processing services is critical, and with artificial intelligence impacting almost all businesses, big data and analytics have become almost indispensable. Synthetic data is a very useful tool to safely share data for various purposes. With extensive use of big data tools and analytic apps, investment in synthetic data generation could be critical for businesses.