At Mammoth, we have helped over 850 companies use their data to answer their most critical business questions. One of the most common mistakes we see product and marketing teams make is trying to use a single tool to answer all their analytics-related business questions. With a sprawling tool landscape, it can be difficult to understand which analytics tool should be used in each business scenario.
This guide explains the difference between digital analytics tools like Mixpanel, Amplitude, and Heap and Business Intelligence platforms, like Looker, Tableau, and Sigma - and why the most data-driven businesses use both.
What Are Digital Analytics tools?
Example tools: Mixpanel, Amplitude, Heap
Digital analytics tools are designed to help product and marketing teams understand more about how their users interact with their website, mobile app, and products. These tools generally consume event data (meaning the actions that a user takes on your website) in real time.
Key reporting functions include funnels and conversion reporting, retention analysis, and user engagement reporting, as well as enabling users to self-serve exploratory analysis.
What Is Business Intelligence?
Example tools: Looker, Tableau, Sigma, Power BI, Sisense, Mode
Business Intelligence (BI) platforms provide a summary of KPIs that are critical to the organization. BI tools sit on top of the data warehouse and are the mechanism to utilize all the various data sources that have been extracted, transformed, and loaded (ETL’ed) in the warehouse, including your event data.
In this way, BI tools allow for reporting on numerous data sources in conjunction with one another. Typically, analysis involves the use of executive dashboards that show the core KPIs of your business and its financial health.
Why You Need Both
Most companies have some form of BI tool (it’s no surprise Google purchased Looker and Salesforce purchased Tableau for a combined value of $18bn), but it’s not always well-suited for all their business questions. Oftentimes teams find that a core business health metric on the executive dashboard has moved–and they are perplexed as to why.
If you lack a digital analytics tool, you may have to dig into the data warehouse. That usually requires calling in expert analysts for support and running multiple complex queries in an attempt to diagnose the issues. A digital analytics tool can help you to complete this exploratory analysis much more quickly, and without needing the technical skills to write SQL.
The bottom line is that there are different requirements for the different tools. BI tools are perfect for defining core business metrics while digital analytics tools are better for empowering all employees to run their own exploratory analyses. Let’s explore each of these in more depth:
Defining Core Metrics
BI and digital analytics serve unique but complementary data management and analysis roles. A key distinction is that BI is capable of integrating multiple data sources, such as financial data and third-party data, thereby consolidating various 'sources of truth' and allowing the analyst to pick the most accurate.
On the other hand, digital analytics typically focuses on event data captured on the platform.
This combination of different sources in the data warehouse often leads to increased complexity due to the diverse nature of the data sources, but it also enhances the reliability of the data. For instance, an analytics event may contain data on expected subscription revenue–but data from the billing provider in the data warehouse shows the actual money received.
BI tools allow for the application of business-specific definitions for metrics, creating a bespoke data analysis framework for an organization.
Empowering All Employees
Research from Harvard Business Review, found that 90% of organizations attribute their success to the empowerment of all employees to make data-driven decisions. This approach allows for more rapid response times and a correction of the business’ path if necessary.
Employees can review dashboards, and those with technical skills can make their own analysis in BI tools. However, digital analytics tools tend to surface important event data in a more accessible and flexible way–giving all relevant stakeholders the valuable data they need.
Digital analytics company Mixpanel commissioned a Forrester report on the ROI of using a digital analytics tool. 25% of the financial benefit came from productivity savings with self-service capabilities as non-technical employees don’t have to wait for analysts to make data-driven decisions.
Product analytics tools are specifically designed for analyzing user behavior. For this reason, they are very good at understanding the complexities of the journey that users are taking through your product, including where they drop off. The tools are also designed for rapid exploratory analysis, which means there is minimal overhead when creating new reports.
BI tools are designed to prepare and build reports and analyze things or objects, rather than behavior. The tools ultimately require a skilled analyst to carry out anything non-standard. The usual mode of analysis (SQL) is effective for grouping things but not so much for sequential analysis as it relies on lots of fact-on-fact / self-joins which are computationally expensive.
Teams often find that digital analytics platforms considerably reduce their time to insight when carrying out exploratory analysis. Path flows and sequenced funnels, while not impossible, are often much more challenging to execute in a BI tool, taking a long time to achieve the desired outcome.
Furthermore, certain digital analytics tools have features like heat mapping or session recording, which are completely absent in BI platforms.
These platforms offer powerful segmentation capabilities, often enabling incredibly complex data analysis with just a few clicks. For example, it would be easy to generate a demographic breakdown of male users from the United States who viewed your pricing page–a task that would require a complex query in a BI tool.
Overall, digital analytics tools offer a more efficient approach to user-level data exploration and analysis, resulting in faster insights–even if the data is less precise than that employed in BI tools. You can expect your exploratory analysis to be enhanced by a directional view that equips you to build more informed hypotheses about your most pressing business issues.
Digital analytics tools and BI platforms serve different but complementary purposes. BI platforms are ideal for reporting core company health metrics that use multiple data sources, while digital analytics tools enable a wide range of stakeholders to perform directional exploratory analysis.
The most data mature organizations are well aware of the power and limitations of both–which is precisely why they have both in their tool stack.