PredictHQ built QSPD: the industry standard for event data quality

We created QSPD – Quality Standards for Processing Demand Causal Factors – to provide an industry benchmark of quality for forecast grade intelligent event data. Without these standards, event data will break your models.
QREP

Intelligent event data is complex and costly to create.
QSPD is essential to quality.

Of sources required to get depth for coverage and verification
Of data science time wasted finding and cleaning data rather than spent on data science
Events change location, date, time after their first published date

Gold standard for processing demand causal factors

  • Depth + Variety

    Depth and variety of data sources is critical to confirming event information is accurate.

  • Verification

    Each event needs to be verified for reliable data, as many events are misleading.

  • Accuracy + Detail

    All event details such as geolocation and attendance must be confirmed or corrected.

  • Categories + Coverage

    Meaningful results requires many categories.

  • Spam Removal

    Spam events must be identified and deleted or they will cause fake demand signals.

  • De-Duping

    All duplicate events must be found and deleted. Some event APIs have ~30% duplicates.

  • Rank by Size + Impact

    A high volume of events requires a reliable way to organize by size and impact.

  • Maintenance

    Event data needs constant maintenance as it is dynamic with event details changing often.

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    Demand intelligence is a new tool for businesses, so categorizing PredictHQ and finding comparable services is hard. We've created this Request for Product guide so your team can get their hands on our data and start seeing value ASAP.

    Intelligent Data

    Processing demand causal factors depends on data pipeline capabilities

    Our data scientists have built more than 1000 machine learning models to aggregate, standardize, verify and rank event data from hundreds of disparate sources.

    Data Quality
    Data Enrichment
    Customer
    Prediction