Event Data Analytics

Event Data: Everything You Need To Know About Event Data

Summary

Event data is any data that you want to measure about an event. For example, when a customer clicks on a product link, the event data would track the product name, category, and other information about the click.

Event data can help organizations understand how people interact with their products and services, what works well, and where they need to make improvements.

Event data is data that represents an occurrence. This type of data is commonly found in applications that track user interactions, such as web analytics, marketing automation, and customer relationship management (CRM) systems. 

In this post you will get an understanding of what event data is, how it’s used, and some of the challenges related.

What is Event Data?

Events are happening all around us. In our apps, cars, appliances, servers, and so on. An event is an occurrence that can be recorded and tracked

Event data is a type of data that is generated when an event occurs. For example, when a customer clicks on a product link, the event data would track the product name, category, and other information about the click.

Event data is important for business analysis because it can help identify trends and understand customer behavior. In addition, event data can be used to improve marketing campaigns and website design.

Event data is an important source of data for companies today. 

Event Data vs Entity Data

Event data and entity data are two of the most important types of data for businesses. 

  • Event data is data that captures what happened, when it happened, and how it happened. Event data is important because it can be used to track progress, measure outcomes, and identify areas for improvement. 
  • Entity data is data that captures who or what was involved in an event. Entity data is important because it can be used to segment audiences, understand customer behavior, and target marketing efforts.

The choice of which type of data to collect depends on the goals and use for the data. 

Note that one event can have multiple entities. For example, user is the main entity associated with every event performed by a user. But when a user is part of a group or an account, that group or account may also be an entity that needs to be associated with that event. 

Process Mining and Event Data

Process mining is the science of extracting knowledge from event data. The aim of process mining is to understand and improve business processes by discovering patterns and trends in the data.

The process mining process starts with the collection of raw event data. This data can be collected from different sources, such as process logs, databases, or application logs.

Once the data has been collected, it is processed to extract the relevant information. This information is then used to build a model and mapping of the business process. 

Classic data mining techniques such as association rules, decision trees, and sequence analysis are used to analyze these event logs.

The model can be used to identify improvement opportunities, and to predict how changes in the business process will impact outcomes such as efficiency

Process Mining

Image source: Fluxicon

Read More About Process Mining
If you are curios to learn more about process mining, we recommend our Introduction to process mining, or check out all of our posts related to process mining

How is Event Data Used?

Event data contains three key pieces of information:

  1. Action: The thing that is happening
  2. Timestamp: The precise date and time when the event took place
  3. State: All other properties associated with the event (known as event properties)

Event data can be used to track user behavior on your website or app. For example, you can use it to see how many people clicked on a certain link, or how long they stayed on a page. You can also use it to see which pages are most popular, and which ones are causing people to leave your site.

Three Pieces Of Information In Event Data

What is Event Data Processing (EDP)? 

Event data processing (EDP) is the process of managing and analyzing event data. This term usually refers to the capture, storage, analysis, and reporting of events as they happen.

Event data processing (EDP) is a term used in business management to describe the various systems and methods used to collect, organize, and analyze event data. 

Organizations use EDP for a variety of reasons, such as understanding customer behavior, detecting fraud, improving marketing efforts, and more. EDP can be used to process data from a variety of sources, including social media, the web, sensors, and other devices.

The goal of EDP is to turn raw event data into meaningful insights that can be used to make better business decisions.

Why is Event Data Important?

Event data can help organizations understand how people interact with their products and services, what works well, and where they need to make improvements.

For example, some of the benefits of analyzing event data include:

  • Learning what marketing channels are most effective in driving attendance
  • Determining which content is resonating with attendees and using it to inform future event planning
  • Gauging attendee sentiment and satisfaction levels in order to improve future events

Examples of Event Data

Event Data in Vehicles: How Cars use Event Data

Event data recorders (EDRs) are devices in some automobiles that record information related to vehicle crashes or other incidents. EDRs can be used by vehicle owners and manufacturers to help determine what happened during an incident, and whether the vehicle’s systems performed as expected.

EDRs collect data such as vehicle speed, throttle position, engine RPM, and whether or not the driver was wearing a seatbelt. This data can be used to reconstruct the events leading up to a crash, and can be helpful in investigating and preventing future crashes.

Event Data Cars

Event Data in Process Mining: How Event Data Fuels the Process Models

Event data is a record of all the activities that take place within a system. It includes information on who did what, when they did it, and how long it took. 

Event data is stored in log files, which are generated by systems automatically. The data can come from different sources, such as ERP systems, CRM systems, Web logs, and so on. 

Process mining uses event data to create models that visualize business processes, for example, with BPMN

Process Mining

If you are curios to learn more about process mining, we recommend our Introduction to process mining, or check out all of our posts related to process mining

Where Do You Get Event Data?

Event data can be collected from a variety of sources, examples are: 

  • Web analytics (ex Google Analytics)
  • Customer Relationship Management (CRM)
  • Chatbots
  • Social Media
  • On-site systems
  • Communication tools
  • IoT sensors
Event Data Analytics

Challenges With Event Data

Unstructured Data

One of the biggest challenges with event data is that it can be very unstructured. Events can happen anywhere, at any time, and they can be triggered by anything. This makes it difficult to track and analyze event data.

Volume

Another challenge with event data is that it can be very voluminous. A single event can generate a lot of data, and if you’re tracking multiple events it can compound to enormous amount of data

Multiple Sources 

Event data can be spread across multiple systems, making it tough to get a complete picture. Each source may use a different format, and the data may be spread across multiple files. This can make it difficult to combine the data into a single dataset.

It is also common for data to be collected from multiple sources at different times. This can create problems with timestamps, as events from different sources may not be synchronized.

Finally, data collected from multiple sources may be of different quality. Some sources may be more reliable than others. 

Conclusion: Event Data in Data Analysis

Event data is a valuable source of information for businesses, as it can help them understand how customers interact with their products and services.

Event data has three key pieces of information: :

  1. Action: The thing that is happening
  2. Timestamp: The precise date and time when the event took place
  3. State: All other properties associated with the event (known as event properties) 

Furthermore, event data is collected from various sources, for example, web analytics, IoT sensors, CRM systems, social media, and many more. The challenges include, unstructured data, enormous volume of data, and finally, that data is often collected from multiple sources. 

FAQ: Event Data for Analytics

What is event in data analysis?

Event data is any data that you want to measure about an event. For example, when a customer clicks on a product link, the event data would track the product name, category, and other information about the click.

Event data can be generated from a variety of sources, including web analytics tools, server logs, and application monitoring tools. In order to be useful, this data must be collected, processed, and analyzed.

What are the three components of event data?

Three key pieces of information in event data

1. Action: The thing that is happening
2. Timestamp: The precise date and time when the event took place
3. State: All other properties associated with the event (known as event properties)

How is event data collected? 

There are a number of different ways to collect event data, but the most common method is to use web analytics tools. These tools can track user interactions with a website or application, and provide detailed information

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Eric J.
Eric J.

Meet Eric, the data "guru" behind Datarundown. When he's not crunching numbers, you can find him running marathons, playing video games, and trying to win the Fantasy Premier League using his predictions model (not going so well).

Eric passionate about helping businesses make sense of their data and turning it into actionable insights. Follow along on Datarundown for all the latest insights and analysis from the data world.