Accurately estimating the snowpack in key mountainous basins is critical for water resource managers to make decisions that impact local and global economies, wildlife, and public policy. Currently, this estimation requires multiple LiDAR-equipped plane flights or in situ measurements, both of which are expensive, sparse, and biased towards accessible regions. In this paper, we demonstrate that fusing spatial and temporal information from multiple, openly-available satellite and weather data sources enables estimation of snowpack in key mountainous regions. Our multisource model outperforms single-source estimation by 5.0 inches RMSE, as well as outperforms sparse in situ measurements by 1.2 inches RMSE.

In the Finance Industry, Alternative Data is used to give investors an information advantage. Quantitative Hedge Funds have used trading models based on Alternative Data for many years. The most common Alternative Data signal used in quantitative trading and quantitative investing is based on text data from the Internet, and the trading models can broadly be defined as algorithmic trading models and as statistical arbitrage models. It has been suggested that text analysis is the key to success for the most successful money manager of all times. The trading model can use text data and sentiment data as the only, or as one of several, inputs, and it can be the main strategy, or one of several strategies, in a hedge fund. Some traditional funds use text-based signals to build the models they use as an overlay to other strategies and as a risk indicator for tactical asset allocation.

Jim Simons is the greatest moneymaker in modern financial history, and no other investor – Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros – can touch his record. His firm has earned profits of more than $100 billion, and between 1994 and 2004, its signature fund, The Medallion Fund, averaged 70 per cent annual return. Medallion's returns don't seem to correlate with known factors and the only thing most people get to know is that the strategy is "statistical arbitrage". People are confounded by the fact that the proliferation of other quantitative hedge funds in recent years hasn't caused Medallion's performance to deteriorate. Last year, there was a very readable book about Jim Simons: On the man who solved the markets – How Jim Simons Launched the Quant Revolution, Penguin 2019.

Finding data for your analytics and machine learning initiatives has generally not been a problem for most organizations. Enterprise organizations collect data as an operational part of doing business. There are transactions, customer records, ERP, CRM, financials, human capital management, and more. Your organization has gathered metrics from web site visits and marketing email responses. There's plenty of data you already have that can fuel your data, analytics or machine learning initiatives.

Wall Street is big business, and it is about to become even bigger with the rise of big data. It is every investor's dream to have prior knowledge of the direction of the market before it happens, which is why financial investment firms are driven to mine for data rather than for gold in the information economy. Traditionally, investors have based their decisions on fundamentals, intuition, and analysis drawn from traditional data sources, such as quarterly earnings reports, financial statement filings to the U.S. Securities and Exchange Commission (SEC), historical market data, institutional research reports and sometimes the so-called "expert networks." The new data-driven paradigm, fueled by new alternative data sources, high performance computing and predictive analytics, offers a more robust framework to generate data-driven investment theses. Data – from satellite images of areas of interest, automated drones, people-counting sensors, container ships' positions, credit card transactional data, jobs and layoffs reports, cell phones, social media, news articles, tweets, online search queries – is now the most valuable commodity for Wall Street.