Empowers with Reasons behind Numbers
This Data Science-powered report presents a forward-looking analysis of the latest company financial results and explains the drivers behind sales and profits at global, segment, product and geography levels. Each important element is visualized and helps in forming a comprehensible understanding of ongoing company processes. The report also contains a high-quality printable dashboard which enables grasping the “whats” and “whys” within a minute. It can be used to support important decisions, to win an executive argument, in a presentation or in strategic analysis.
Additionally, the report includes next-quarter forecasts derived from current company drivers and economic environment and contains expectations for the upcoming six to twelve-month period (when applicable). This knowledge helps not only to be perfectly informed but also continuously expands the circle of competence on companies, products, industries and geographies.
Public companies continually release materials such as earnings reports, industry and investor presentations or management discussions. Taken as they are, earnings reports divulge very little valuable information which is often kept scarce by the companies themselves on purpose.
The publisher's analysis brings meaning to earnings reports by providing the reasons behind reported numbers. The report answers questions such as: “Why revenues increased 56.80%?” or “Why data center revenues increased 161.71%?“
It’s those answers that reveal the processes called drivers that are shaping a company’s future. Being aware of them gives the ability to act preemptively on events that will become known to the public months later.
Using this knowledge, the publisher starts assessing the direction and magnitude of each driver and predicts with a high level of confidence the company’s future performance. Their technology helps to selectively reduce the large volume of data into those nodes of information that carry real value and can empower personal knowledge in an efficient manner.
As an investor, business leader, adviser or money manager this will greatly reduce the margin of error when taking action against the upcoming risks that surround us.
Table of Contents
1. Introduction
2. NVIDIA Q2 Fiscal 2021 Earnings Retrospection
3. NVIDIA Q3 Fiscal 2021 Earnings Analysis
3.1. Revenues
3.2. Operating Income
3.3. Net Income and EPS
3.4. Gross and Operating Margins
3.5. Cash Flows
3.6. Segments
3.7. Products
3.7.1. Gaming
3.7.2. Professional Visualization
3.7.3. Data Center
3.7.4. Automotive
3.7.5. OEM
3.8. Geographic Regions
4. Forecast
5. Appendix: Dashboard
Samples
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Executive Summary
From its core, NVIDIA’s parallel processing technology was meant to efficiently serve petabyte-scale Artificial Intelligence and Machine Learning workloads. The widely promoted Data revolution never materialized though which showed up in NVIDIA’s results as a series of falling revenues and profits up until the middle of 2019, not to mention that the U.S. was a negligible geography on the company’s map.
From the publisher’s perspective though, NVIDIA was clearly the organization best positioned to gain in a market, opening for opportunities by Intel’s slow motion. The publisher assesses NVIDIA’s technological offering as transformational.
Companies Mentioned
- Intel
- NVIDIA
Methodology
The publisher employs advanced Data Science methods such as probabilistic inference and algorithmic reasoning to establish causal relations between outcomes such as company sales and profits, and the hidden processes that drive them. Furthermore, their methodology uses stochastic processes analysis to model and predict company performance in a period of three months ahead and formulate high-probability expectations for the next six to twelve month intervals.
The publisher relies strongly on analyzing inherently uncertain streams of information and all analytical output represents the highest confidence results of a specific study with two or more such results presented in the report whenever they discovered that several factors had similar impact on a target variable.
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