Traditional Business Intelligence vs. Product Analytics

You’ve probably heard a hundred times that making data-driven decisions is critical for faster growth. There’s less talk about the need to use the right kind of data — data that is current, relevant, and accurate. 

While data derived from traditional Business Intelligence (BI) models is undoubtedly important, it may not offer the depth and value of insights that a Product Analytics platform can provide, especially when it comes to truly understanding what your users need. 

In this blog post, we discuss the key differences between Business Intelligence and Product Analytics and explore why relying exclusively on BI within your organization is no longer enough for strategic growth.

The difference between traditional BI and Product Analytics in a nutshell

Business Intelligence focuses on metrics across various aspects of the organization, such as Revenue, Customer Success, Operations, and Marketing/Growth. BI metrics are typically sourced from the organization’s data warehouse and are usually interpreted via tools such as Looker or Tableau. 

In contrast, Product Analytics is a newer discipline that specifically caters to Product, Marketing, and Growth teams, offering advanced insights into user engagement within digital products. Product Analytics Platforms enable users to access data in a self-serve model. It is ‘event-oriented’, focusing on data around user behavior, collected from platforms like apps, websites, and other connected devices.

Different purposes: BI shows you past metrics, PA reveals real-time trends

While Business Intelligence focuses on past metrics, Product Analytics is able to showcase real-time trends, providing invaluable insights into user behavior, with details around user journeys, paths, points of friction, and areas for optimization. 

Unlike BI, Product Analytics tools can provide answers to questions such as:

• How fast are users adopting new features, and what impact does it have on retention?
• Which features are most frequently utilized by users, and which ones see less engagement?
• What are the prominent patterns and most common workflows?

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Product Analytics solutions offer rapid access to relevant, accurate user data

In organizations that rely solely on conventional BI models, Product Managers and Marketers often have to wait for the data team for answers, leading to lengthy back-and-forths and delays. 

In contrast, Product Analytics tools empower them to answer questions themselves. Quick, self-served responses in Product Analytics platforms not only accelerate the process of analyzing the data but also ensure accuracy, as chart details are accessible, allowing for double-checking for errors at each step. 

Another limitation of Business Analytics is that verifying the accuracy of reports also requires the data team, resulting in a bottleneck. Teams in a rush often skip the verification process, leading to unnoticed mistakes until it’s too late.

Adopting Product Analytics leads to more independent teams & better decision-making

With the right Product Analytics tool, Product and Marketing teams are no longer dependent on the Data team for timely insights, which benefits everyone across the org.

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The results speak for themselves: faster access to critical insights, highly accurate reports, and a better understanding of user behavior. 

For teams considering adopting a Product Analytics tool: now is a great time. The current data landscape makes it easy to integrate Product Analytics with existing data sources, enabling Growth and Product teams to leverage real-time, relevant data to make well-informed decisions to drive the organization forward with purpose and precision.

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