The need for using data-driven business decisions in different sectors saw the need for data mining. Through data mining, big data collected over time is analyzed to show trends and patterns connected to connections and interaction. Thanks to big data, businesses have been able to cut costs significantly, increase revenue, and competitiveness.
Data mining and big data have had a buzz for quite some time. Recently, synthetic data has been receiving a significant share of attention. Initially, only raw data was used in data mining. Unlike raw data, synthetic data does not risk any personally identifiable information. The data is artificially created mimicking real data, but diminishes its risk.
Here is more you need to know about synthetic data.
Synthetic Data versus Anonymized Data
For a long time, the prevailing way to get utility out of data without risking data leaks was anonymized data, which is essentially removing all information that could be personally identifiable. However, security and privacy are weak for anonymized data as there is a high chance of re-identification.
Fully synthetic data, on the other hand, doesn’t contain any real customer information. It is generated artificially by machine learning algorithms that maintain the overall user patterns without risking any actual user information. This not only gives synthetic data a greater degree of privacy and security over its anonymized counterpart, it makes for more accurate and useful data.
Synthetic data eliminates the limitations of data compliance
Large corporations and financial institutions have their reservations in data mining. This includes the sharing, use, and monetization of data, not allowing third-parties to build onto their innovation. The main concern is threat to the privacy of customers when sensitive information is shared. The limitations to the availability and use of data have held back many corporations and agencies.
Synthetic data has, however, changed everything. Confidentiality in data security has increased because synthetic data meets regulatory and compliance standards.
Synthetic data is being more widely used in machine learning applications
One of the significant applications of synthetic data is machine learning. In highly regulated industries, machine learning teams often have to wait months and even years to get permission to use raw data, which means they are often training their models on stale data. Synthetic or artificially generated information can be used much more quickly to train a model. It can also be used to pre-train machine learning models even before real data exists. With this, it has been able to solve data issues in AI.
How is synthetic data generated?
The method you choose to generate synthetic data will be determined by the synthetic data you want to have. Synthetic data is generated by either getting numbers from a given distribution or through agent-based modeling.
Generating numbers from a distribution involves the observation and analysis of real distributions and reproducing fake data from that statistical distribution. Agent-based modeling, on the other hand, consists of the creation of a model that explains observed behavior, and the reproduction of data using that same model. Synthetic data is promising, and there is a lot to look forward to in this light.
