A variety of activities lead to employee upskilling, from company-funded online courses to regular knowledge sharing sessions. “To ensure personal and organizational growth, Draup encourages self-learning as well as peer-to-peer learning. The only way we can stay ahead of the curve is to keep learning,'' says Kashish Jajodia, CTO, Draup. Technology is changing at a rapid pace – What is new and path-breaking today will become redundant and legacy tomorrow. One person from the team is given a chance to talk about and make others learn from their recent project, a MOOC they completed, or any personal projects. ''Every sub-team at Draup, conducts a weekly group study of one hour. Unsurprising when you consider that Draup has also built one of the leading AI-powered reskilling solutions out there. Draup's initiatives when it comes to upskilling their employees has fostered a company-wide culture of continuous learning & reskilling. With continuous exposure to the latest technologies, employees at Draup are expected, encouraged and supported to refine their skillsets continuously. However, at Draup, this mindset is also aimed at building their employees' careers as well. Fostering an Environment to Learn & Grow The hustle mindset at startups is a well-known phenomenon. Despite all these technologies in use, Draup's DNA is still driven by their best-in-class Engineering and Data Science teams.
This data is then used to generate human-like intelligence for the platform through Artificial Intelligence. The team's operational task involves working with various teams at Draup to collect, analyze and create insights on large volumes of data gathered through multiple sources. The Data Science team at Draup works on cutting-edge ML models to develop NLP-based models that power our platform. Draup's Machine Learning & Data teams have built a robust sales & talent intelligence platform that Fortune 1000 companies rely on for their critical business decisions. And when you have huge volumes of inbound data that need to be ingested, analyzed & presented in intuitive forms, you can't help but build innovative solutions. They work on extraction and processing terabytes of data daily and creating a fast and easy-to-consume interface for internal and external stakeholders. The Engineering team at Draup develops solutions that leverage Big Data analysis, data processing, and application development while working with distributed systems. ''This is then analyzed, interpreted, and presented in various forms on the platform, research reports, tailored insights, and develop machine learning models, statistical models, and algorithms,'' he elaborates.
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''We as a team, work with various cross-functional teams such as product managers, designers, researchers, software engineers, data scientists, and other roles to provide them with data,'' says Chandan Kumar, a Data Architect at Draup. And this is precisely what powers the Draup Platform. Needless to say, such an ambitious endeavor requires the use of bleeding-edge tech backed by experts.
The Technology & The Teams Behind Draup On any given day, Draup's sales & talent intelligence platforms pull in over 10 Million data points from 8000-plus sources which are fed into their 70(and growing) custom machine learning models. Today, Draup is a key enabler of revenue growth and the trusted intelligence platform for enterprise sales teams that generate over USD 400 Billion in global revenue and HR leaders who manage a global workforce of over 9 Million employees. It helps decision-makers anticipate key trends in the sales & talent domains across industries. Draup's context-rich data in an easy-to-use, natural language interface helps go-to-market teams identify new opportunities and understand what's top of mind for customers along with their strategic investment priorities. In the case of decision-making, that happens to be the integration of cutting-edge AI, ML, Data Science & Cloud technologies into one holistic platform – Draup.
And when demand increases, innovation follows. This is evident in the fact that the market for global decision support software is expected to touch $9 Bn by 2026, spurred by a rise in demand among middle and upper middle-market entities looking to optimize their decision-making capabilities. While good human judgment has always been at the core of good business decisions, in today's economy of scale & complexity, data-backed decision-making systems are preferred by enterprises everywhere.
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