It's the new oil. It’s the heart of business. Analyze it correctly and your company will grow. We hear these things over and over when talking about data, but are we aware of it's true value? Probably not, but not because companies do not know what type of data they store and how important it is. Instead, the connections between data and insights it can reveal are not thought of when machine learning is applied to interpret it and find patterns, trends, and correlations, even when some links in the chain are missing. So, here the concept of synthetic data begins to emerge.
It's important to recognize that no matter how much expertise a data analyst has, many patterns can be overlooked since the breadth of the information makes the task overwhelming at times.
So, the arrival of artificial intelligence (AI) algorithms in the world of analytics and big data has given rise to a new type of data: synthetic data. This data is artificially generated through the algorithmic processing that AI and machine learning systems carry out to simulate real-life events.
What is synthetic data used for? Why is it created? What companies are already appealing to its use? It becomes necessary when we want to take a first step towards concept tests and pilot solutions that use machine learning. It is particularly useful for predictive models, as the lack of overall access to data to evaluate the potential of adopting these solutions is a common barrier.
Although organizations often have sufficient historical information, it’s not structured in a useful way, it contains "gaps" or too much noise and outliers, or requires greater variety and casuistry, which is why synthetic data complements it so well in many cases.
In the health industry, for example, it is used when access to complete patient data is not possible due to confidentiality or regulatory compliance purposes. In the insurance industry, it can serve as the counterpart to traditional actuarial calculation, simulating scenarios and implementing digital twins, or to understand how the impact of a low probability unexpected event.
At Softtek, we use synthetic data to create test environments and simulations that allow for concept testing and the evaluation of predictive models in compelling use cases across different industries and business verticals. For example, it may be used to anticipate customer churn, prevent fraud, increase cross-selling, or optimize customer lifetime value, among others.
This approach is also related to the concept of data mesh, whose premise is to empower businesses to obtain new insights and make decisions that can materialize a more effective digital transformation.
In turn, a marketplace of data products is created both intra- and inter-organizationally. The business becomes the owner of the data and holds a business area/unit accountable for keeping it reliable, ensuring it has external sources, creating and interpreting models using the domain of the business expert, and ultimately converting it into a competitive advantage and increased revenue.