How to build more meaningful machine learning models with third-party data

Valerie Williams
Content Marketing Manager

Businesses across industries are increasingly relying on machine learning

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While most organizations acknowledge machine learning can improve many functions and decisions across their business, many find that it's just not as easy as it sounds. There are a common set of challenges companies face when using machine learning models, the main one stemming from a single problem: relying solely on first party data to power their models. This approach often leads to a lack of data and issues around data quality, making it difficult to ensure the models remain accurate.

Why companies should leverage third-party data in their machine learning strategies

Incorporating third party data is an important strategy that many businesses have turned to for strategic insight from outside their four walls. A successful data strategy is made up of turning important internal insights into gains, as well as incorporating external data that is key to understanding the full impact of external factors that influence their business. 

Let’s face it, times are uncertain and much of what has happened over the last couple of years is unprecedented. Executives and data teams are tasked with building more resilient teams and strategies, and becoming more proactive to be able to react to the uncertainties. 

Businesses are increasingly seeking better insights through third-party data. This can include everything from location insights, to foot traffic data, to weather data. Today we’re here to talk about third party event data. 

Many organizations across industries integrate third party event data into their machine learning models to better understand what's going on in each of their industries at scale. 

For example, delivery company Favor ingests external event data that is relevant to their supply and demand into their models. Rather than solely using this own first-party data, they relate this event data to each neighborhood they’re forecasting for to ensure they are getting the most complete picture of demand. 

How can companies access third-party data? 

As most businesses know, the volume of available data is increasing tenfold, and having access to timely relevant third-party data is crucial for success. Whether it's via API or a data exchange like AWS ADX, companies need to make sure they’re clear on how they’ll consume the third party data.

Check out the virtual event below to hear from leaders at AWS Data Exchange, PredictHQ, and Favor about the trust and the explainability external data, including event data, brings to forecasts and how companies can easily ingest the data. They cover the importance of having quick and seamless access to discover and use third party data.

The session covers: 

  • Real-world examples of third-party data benefiting ML models

  • How AWS services, such as Amazon SageMaker and Amazon QuickSight, can make building ML models easier and more accurate

  • Ways that PredictHQ’s demand intelligence can help improve forecasting accuracy

  • How Favor improved its mean absolute percentage error (MAPE) by incorporating features based on PredictHQ’s data with Amazon SageMaker

Watch the on-demand webinar here.