Featured Providers

 

Unacast is an award-winning human mobility data company that harnesses anonymous device location data, map data, and strategic intelligence to tackle business challenges for the retail, real estate, tourism, transportation, and marketing industries. With its flagship product “The Real World Graph®“, it provides innovative solutions and insights to operational challenges for companies of any size or shape. Unacast was founded in 2014 with offices in New York and Oslo, Norway. In 2019, Unacast was awarded the #1 small company to work in NYC for by Built In NYC and received Street Fights’ Most Innovative Use of Geospatial Technology award.

Possible use Case: Understanding how the commercial and residential landscape has changed in recent months and the temporary or permanent nature of these changes.

  • Identify areas that are growing vs. decreasing in population
    • What are the unifying characteristics of the areas with an increasing population vs. decreasing population?
    • What are the resident and visitor type of these areas?
    • Based on this information, can we predict which areas will become commercial hubs vs. those that are more susceptible to economic depression
  • In commercial hubs, are there enough services to support the influx population? If not, which services / industries have expansion opportunities?
  • In areas with depleting populations, what does the remaining landscape look like? How have the retail stores been impacted? What is the chance for recovery and how does it differ by industry?

 

The Kochava Collective is an independent mobile data marketplace with more than 8 billion unique devices globally. Marketers utilize it for affinity audiences, interest targeting, and audience demographics.  Our privacy-first data, which complies with user data privacy and consent regulations, including the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR), is anonymized and aggregated to activate and expand collaborative audience data through data enrichment and competitor audience analysis, while also providing a channel for data monetization, target market segmentation with seamless activation options. All premium third-party suppliers are thoroughly vetted for data integrity and must pass appropriate privacy string and consent signals.  

 

Crux is a data delivery and operations platform and managed service that helps companies reliably get the data they need, how they need it and where they need it. By working directly with suppliers and serving many consumers across a multitude of data sources, Crux unlocks economies of scale that benefit the entire industry. They deliver data at a lower cost, with a faster time to market, in a standard easy-to-use way, and at a consistently high level of service and security. Crux currently delivers 107 datasets into the Amazon Data Marketplace covering various sectors, including:

  • Macroeconomic
  • Agricultural Indicators
  • Climate and Environment
  • Economic Outlook
  • Economy and Finance
  • Education
  • Employment
  • Flows
  • Global Production
  • Population Statistics
  • Research and Development
  • Science and Technology
  • Taxation

 

CSRHub offers a comprehensive global set of consensus ESG (Environment, Social, and Governance) rating. CSRHub’s big data business intelligence system ingests data from a wide variety of sources, including Wall Street analysts, crowd sources, government databases, and non-governmental groups (NGOs).

 

Swarmode - Apply your ML and data science skills to model the financial markets and extract alpha. With Swarmode's datasets, data scientists would be able to build finance Machine Learning models at scale with Amazon SageMaker. Use our hi-quality historical data from 1000+ US equities trading signals to build ML models and unlock new alpha-generating trading strategies.

Potential Use Cases: By using their QSignals data sets to train your Machine Learning models, you would be able to:

  • Run binary classification models, such as XGBoost, to validate and predict daily stock trading signals with higher returns 
  • Apply regression models, such as Linear-learner, to historical data signals to forecast stock prices and volume
  • Perform clustering algorithms, such as K-means to identify stocks with similar performance to apply in pair-trading and long-short trading strategies

Use case example with AWS Data Exchange, SageMaker in Github repository: https://github.com/edenciso/qsignals-AutoML-SageMaker. This includes Jupyter notebooks and a couple of demo videos.

 

With Dun & Bradstreet’s Strategic Marketing Archive Data, you can deliver insights your customers need to understand their audience, prioritize campaign strategy, and reduce wasted marketing spend. This layout contains over 180 elements for building traditional marketing models for precision targeting done at the business site location level. The monthly periods allow for time-series data analysis that you can put into a machine learning algorithm to back-test and train your models. Some relevant use-cases focused on driving marketing performance include: targeting qualified prospects, acquiring best new customers, driving demand response, and retaining more customers.