Augmented data pertains to a collection of data processing techniques that enable the expansion and enhancement of available data for training machine learning models and conducting various forms of data analysis. These techniques aim to increase both the quantity and quality of the data utilized in these processes.
Augmented data is generated through a range of techniques, including data synthesis, data blending, rotation, translation, cropping, inversion, and noise augmentation, among others. These techniques facilitate the generation of new data samples based on existing ones, thereby enhancing both the diversity and quantity of data available for training machine learning models.
Augmented data proves particularly valuable when the original dataset is limited in size or lacks diversity. It also plays a crucial role in uncovering hidden patterns and features within the data that might remain concealed otherwise.
In the realm of sales, augmented data serves to amplify the volume and diversity of available data for the purposes of sales analysis and forecasting. Consider an online retail company with limited information regarding customer purchasing behavior throughout various times of the year. To enhance the precision of sales predictions, the company can employ augmented data techniques to generate new observations from existing data.
The Sales Glossary is a compendium of all the most commonly used terminology in sales strategy. Many of the concepts listed here are used when implementing a CRM system or a digital sales funnel, no matter if they are legacy systems or an online CRM. See also our blog that deals with sales techniques, marketing and sales culture.