We live in a data-centric world today with oceans of data around us. They come from various sources like static and dynamic web pages, IoT devices, autonomous cars, to name a few. With the tremendous adoption of Deep learning applications, data in the form of voice, images and videos are also valuable. Today’s enterprises are becoming increasingly aware of the competitive edge data can provide to them. However, data collection is a difficult task needing a lot of automated and human resources. They would rather outsource this task to reliable and experienced vendors who not only understand how to collect the data but can help with preparing and enriching it. 

With a large pool of analysts, we gather relevant and qualitative data from diverse sources within a specified time. We use contemporary tools and methods for an assured delivery of quality data to our customers on time.

Data collected from public sources are typically in unstructured format needing cleansing, consolidation, normalization and validation, before they are ready for business use. For an eCommerce platform, maintaining a clean and reliable data is fundamental to improved sales and customer retention.

Quality of the data collected and prepared for machine learning will clearly affect the output of the machine learning models. However, the data needs to be further enriched with features being identified for the AI/ML model. Also, multimedia data like images, audio and video need to be annotated with metadata like bounding boxes, keypoints, etc, before being used for training an AI / ML model. At the core of an AI/ML project, the following three key phases determine the quality of the data and the performance of the model.