Eliminate Data Post-Processing With Datazoom
One of the most time-consuming activities when working with multiple data sources is post-processing. Whether that’s standardizing field names to correlate data sets, contextualizing in app data with backend system data, applying rules to filter out unnecessary data, trying to correlate sessionized, summary level data that’s to abstract to properly unify with other data sets, or transform valuable data into the format needed for downstream systems, any time spent post-processing is time that could have been spent putting the data to use.
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With Datazoom, data processing is built into its real time platform so that standardized, contextualized, relevant data is delivered in real time, reducing or eliminating the need for post processing.
Process Data When It’s Collected, Not When It’s Received
Many businesses struggle with data post-processing. It can be a time-intensive process, especially when it involves multiple data sets. But automating common post-processing activities can have a demonstrable impact on the business.
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By automating data processing activities at collection, such as combining data sources and transforming data points, companies using Datazoom can save countless hours and make their data usable faster
The Four Data Processing Activities Automated Through Datazoom
Datazoom provides for key data processing features that users can turn on at the time of collection. Each of these features reduces the need for manual tasks carried out when data is delivered and provides important business benefits.
Enrich
Data from external sources, such as Google Ad Manager, can be added to the data stream so that the data received by the streaming operator is ready to use.
Filter
There are times when you don’t need every single data point, especially for messages that don't exceed a particular threshold (and may trigger an alarm). To make effective business decisions, you may only need every 10th or 50th data point. Through the Datazoom platform, you can specify just how often a specific data point should be collected.
Tuning and Sampling
Depending on what’s being monitored, measured or reported on, capturing all data can become expensive. Trying to understand the results of an AB test, or trying to monitor Quality of Experience in a very granular way, or link datasets together for Observability, it might not be necessary to capture data from every session, and can still get the insights we need from data sampling.
Transform
Post-processing data can be a time-intensive activity which can, ultimately, impact how quickly the data can be put to use. This can include changing variable types (from number to text, for example), variable names, or even how the values are calculated. With Datazoom, this can all be done at the time of collection.
