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The Neverending Need for Quality Data

Data scientists can all agree: there is never enough data. Even with the enormous amount of data that companies are accumulating, there never seems to be enough of the right data- the necessary data to train algorithms to perform specific tasks. In our recent article, Data, Data Everywhere, But Not a Drop to Drink, we discuss precisely this. That no matter how much data you have, it is never enough.

Gartner found that the average cost of poor data quality on businesses amounts to between $9.7 million and $14.2 million annually. That is a lot of data. Even when the data is of excellent quality,  companies are still needing more of it-rounding up to an estimate of 10,000 label data points needed to provide sufficient information to develop an algorithm that can extract insights and generate predictions.

That said, data can be difficult to collect. The extraction process can be a strenuous one and more time-consuming than building the actual machine learning models. This leads data scientists to use synthetic data. 

Synthetic data is precisely that; it is data that is artificially created and based on possible scenarios. 

The Importance of Synthetic Data

Synthetic data is not created without conscious efforts. As previously mentioned, it is based on possible scenarios or outcomes. It uses statistical properties from real datasets.

Here is an example of the effective use of synthetic data. A healthcare insurer needed to calculate how frequently customers with kidney disease file claims and for what reasons. After lacking sufficient internal data, they needed to integrate synthetic data to better support the algorithm. The synthetic data was created from made-up, but possible scenarios related to ailments of those with chronic kidney disease. The added data was also created by using the initial dataset as a guide.

It is a cycle- synthetic data needs real data to be created, which continuously feeds itself to become more accurate for better algorithms.

There is Never Enough Data

Synthetic data can help develop computer vision apps. For example, an urban planner needs to identify how many eight-wheelers use a specific stretch of highway each year. The computer vision application will be tested by identifying each one in an image. If no images are available, one can create a 3D model and strategically place it in possible locations. This model will aid the computer vision app by training it to identify the differences between eight-wheelers and smaller trucks or cars.

Data scientists have found that by combining a quality base model and synthetic data, the algorithms can become highly accurate and add bigger value.

Additionally to adding value and generating effective AI, synthetic data is also proving to be the solution for privacy concerns. Regulated industries, such as healthcare, finance, and banking, must be on the lookout for their data privacy. This can become an obstacle when that sensitive data is needed to generate accurate outcomes for algorithms. 

By using synthetic samples from the real datasets, companies can take advantage of key characteristics of the original datasets without causing privacy concerns. 

Four Tips for Successfully Leveraging Synthetic Data

Not all data is created equal- and this includes synthetic data. Its role is necessary and growing. Here’s what you can do to ensure the quality of your synthetic data and if it’s adding value to your algorithms by making them more accurate and effective. 

  1. It’s all in the base. Ensuring the quality and accuracy of your original datasets will allow the algorithm to get smarter as you add more of the same info.
  2. Consider the source. Partnering with a synthetic data provider with experience in the full cycle of AI development will guarantee you a partner that understands the importance of clean data, how much is needed, and the role of testing.
  3. It takes more than a tool. Creating synthetic data requires human knowledge. It is a complex process that also requires advanced frameworks, which require specific talent trained on the system. 
  4. Look for data that goes deep. When sourcing data, target those specific to your cause. The best synthetic data is that which is aligned with a specific issue.

The growing need for training data that creates better-informed machine learning algorithms will lead businesses and data scientists alike to synthetic data to produce more accurate solutions. And since algorithms are never satiated- there will always be a need for more data. Make sure you find the right one.

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