However, managing oceans of data from disparate sources is challenging. Furthermore, it’s vital to access valuable insights in a timely manner. That’s where data augmentation comes to the rescue.
This article focuses on how data augmentation can help you automate organizational processes, foster informed decision-making, save costs, and more.
Data augmentation is a progressively popular data analysis technique that increases the generalizability of overfitting data models. By marginally changing and making copies of existing data or by creating new synthetic data from existing data, data augmentation helps increase the quantity of representative data.
Now, why is data augmentation important?
As machine learning (ML) rapidly expands, data augmentation makes it easier to make sense of new information. It increases operational performance, ripens data, and boosts the accuracy of ML models.
Augmented data management uses artificial intelligence (AI) to help data analysts and scientists enhance data management tasks. Manually, these tasks are time and resource intensive.
Automated data management includes but isn’t limited to the following:
Data augmentation can help data models with visual transformation protocols recognize objects and help make intelligent decisions.
Data augmentation creates new synthetic images by leveraging generative adversarial networks (GANs) and unity game engines. Moreover, color space and geometric augmentation diversify existing image models to assist ML tasks like the following:
Data augmentation techniques help better manage language models on a character and word level. For example, we can leverage data augmentation for translation purposes or to insert random words, characters, and sentences.
Data augmentation can also be used to:
Sometimes, data scientists have to extract meaningful data from long audio files. Below are scenarios where data augmentation comes in handy in identifying, editing, and enhancing such auditory data.
Augmented data sets can be more than a handful to analyze individually and manually. Companies must invest in top data talent or build evaluation systems to appraise quality. This is quite challenging, as data augmentation multiplies available data with variations or added finesse.
Advanced applications demand augmentation domains to research, learn more, and create new and valuable data. For example, generating high-resolution imagery using GANs will be challenging without data augmentation.
Original data often contains bias. As such, augmented data could carry forward the same tendency. Data scientists and engineers must identify and prioritize data augmentation strategies to eliminate data bias from the source.
As data contains structured and unstructured information, data scientists must mine hidden, unrecognized data trends to forecast behavior. By using data augmentation, enterprises can increase model prediction accuracy.
They can achieve this by:
Collecting data takes time, and it costs money. Identifying raw data such as images, texts, and videos and labeling them for management purposes also requires extra focus. Data augmentation provides a business advantage because of its automation capabilities. It significantly helps organizations cut data collection and labeling costs.
Rare event predictions prepare companies for rare or low-probability occurrences. For example, automobile manufacturers can predict failures in their automated production sequences. Data augmentation enables such rare event predictions, creating space to manage anomalies earlier on.
As AI and ML protocols evolve, data augmentation will be at the heart of natural language processing and image recognition. It will drive the connected car industry, the medical imaging arena, and much more.
However, data augmentation is far from a straightforward turnkey solution. It has its advantages and disadvantages but is extremely useful when used appropriately. This is because data augmentation can increase the performance of data models significantly.
Going forward, we can expect data augmentation present new opportunities, especially for companies that rely heavily on data for business operations.
The healthcare industry is using data augmentation to find tumors. Self-driving cars use this technique to improve their machine learning process with real-world simulations. What comes next in the months ahead remains to be seen.
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