Choosing the right business intelligence tool


By Tianyou Gu

This is our second post in a series about integration categories. Revisit our post on choosing the right performance monitoring tool to read about our series objectives.

Unlocking your business data

Business Intelligence (BI) software enables access to and analysis of information used to inform business decisions. Through automation and visualization, these tools should provide a better understanding of your organization’s key metrics and drivers of those metrics. 

In this post, we’ll provide a framework for evaluating BI tools, profile a few of the key players in the market and discuss which tools might be best for you based on your primary objective.

What matters to you?

BI tools are geared primarily toward either business users or analytics users. Some tools, first and foremost, enable business users to explore data, while others enable real analytical powerlifting but require more technical prowess. (Of course, any tool can and should be used and maintained by both groups—this distinction simply refers to the primary audience.)

Business-user-focused tools

Business-user-focused tools (like Tableau and Looker) require some initial technical setup via an intermediate semantic layer (i.e., logic that maps complex data to familiar business terms, like customer or revenue) to define dimensions and measures. After set-up, fewer technical chops are needed on an ongoing basis. The tools allow a business stakeholder—say, the head of marketing—to build their own analyses without structured query language (SQL). More robust end-user functionality—like drag-and-drop, filtering, drill-downs and computed fields—enable this. 

However, companies generally require dedicated resources to maintain and translate the underlying data, so there can be a time lag when business users require new metrics or fields to be defined in the modeling layer. For instance, if your company were to shift focus from Gross Revenue Retention to Net Revenue Retention, an analyst would need to revisit the semantic layer to map complex underlying data to the new business metric. These tools really shine for dashboards, tracking high level KPIs and showing customer specific usage but are less fitting for ad hoc, one-off questions that require deep analysis. 

Here’s just one example of how you could use a business-user-focused tool:

  • Build a report of weekly sales by region, including a map visualization. Show various cuts of the data within each region, e.g., product, product category, industry, etc.

  • Automatically refresh the report each week and then send to sales managers

  • Allow each sales manager to explore trends in the data themselves, e.g., drag and drop additional fields to see what could have driven a change in sales.

  • When something changes on the business side—we might, for example, want to change the categorization of our headphone product from “Accessory” to “Audio Add-On”—an analyst will need to be looped in to update the semantic layer. 

Analyst-focused tools 

Analyst-focused tools (like Periscope Data and Mode), on the other hand, are “code-first,” powerful data exploration layers that sit on top of databases. These tools are generally used by analytics teams; although business users can refresh existing reports, code is needed to create and customize them. With these tools, analytics teams are empowered to be data explorers instead of reactive, database-maintainers. In those cases when business users may not know the right questions to ask, data analysts can step in and use these powerful tools to investigate more deeply. Over time we expect these tools to add more native visualization features, but for the moment they’re best suited for SQL pros. 

Here’s just one example of how you could use an analyst-user-focused tool:

  • The VP of Customer Support notices a big uptick in tickets from small and mid-sized business (SMB) customers and asks the analytics team to investigate further. 

  • An analyst creates a SQL query, joining data from a user database, product usage database, support database, etc.

  • The analyst then creates basic visualizations within the platform to understand what’s going on in the data and where the analysis should be fleshed out.

  • The analyst can share the results back with the VP to show her that the tickets correspond to a new feature that was rolled out without enough explanation to SMB customers.

  • The VP can refresh the analysis in two weeks to ensure that the problem was resolved.


Tool profiles


Tableau is one of the most mature BI tools available with robust analyses features. Tableau is known for best-of-breed visualizations that are accessible to all types of business end-users once analytics has performed the data modeling. Its drag-and-drop interface allows users to create dashboards and in-depth analyses, but first-time users will need a good deal of initial training. 

Top features:

  • Connect, query and visualize your data without writing code

  • Use a drag-and-drop interface to create powerful, in-depth analyses or simple dashboards that can be optimized for desktop, tablet or phone

  • Set up alerts and subscriptions for reports and share across your organization

Great for:

  • Company size: Mid-market (MM) and Enterprise companies 

  • Role: business teams across the organization with a data analytics team to support


Looker is also geared toward giving business users the ability to do self-service analytics, but it’s even more sophisticated and transparent under the hood. Looker uses an intermediate data modeling layer to define business metrics once for consistent use across the entire organization. (It’s coded in a SQL-like language called LookML that makes version control and debugging easy.) Once an analytics team has set up the environment via data modeling, business users can generate their own drag-and-drop reports and use visualization features to explore data without much Looker training. Each analysis generated through drag-and-drop also creates a SQL query that explains what’s going on behind the scenes, so it’s not a black box. 

Top features:

  • Access, explore and operationalize data using Looker’s library of visualizations or by creating your own

  • Create a consistent set of data definitions in Looker using LookML

  • Connect Looker to other best-of-breed solutions via the Looker R SDK

Great for:

  • Company size: MM and Enterprise companies 

  • Role: business teams across the organization with a data analytics team to support

Periscope Data

Because it bundles hosting, data connectors and visualization layers all in one, Periscope Data is great if your organization is data driven but unable to dedicate a lot of resources to spinning up an Extract, Transform and Load (ETL) pipeline and warehouse. Periscope Data also offers embedded functionality and allows a high degree of control and customization for visualization. Periscope Data’s data cacheing (loading customer data into multi-tenant Redshift clusters) enables much faster querying. 

Top features:

  • Plugs directly into your databases and lets you run, save and share analyses over billions of data rows in seconds

  • Easily explore data and perform basic calculations and aggregations

  • Create rich visualizations and share dashboards using a drag-and-drop interface

Great for:

  • Company size: Small and midsized business (SMB) and MM companies

  • Role: analysts 


Mode is the most technical and flexible tool for analytics powerlifting. Because it is very easy to get up and running, it’s a particularly good choice for both smaller companies that want to move quickly and for analytics or data engineering teams that prefer coding. With its HTML editor, Mode offers users full access to a custom environment and the option to embed a white-label version in your site.

Top features:

  • Query data using SQL and Python

  • Visualize data with interactive reports and share across the organization

  • Integrate custom, interactive analytics into your application using White-Label Embeds

Great for:

  • Company size: SMB and MM companies

  • Role: SQL analysts and data scientists 

Making your decision

As you evaluate which BI tool will work best for your company’s data needs, we encourage you to consider who will be responsible for initially setting up the tool (along with a data warehouse and ETL process, if you don’t already have one). Whoever maintains the tool, will also need to be proficient in SQL for any of these options. You’ll also need to decide whether you want to enable business users to create custom analyses within the confines of a pre-defined environment or whether you want to give your analytics team the horsepower to answer any sophisticated, ad hoc business questions that may arise. Keep in mind, some companies choose to set up a mix of these tools so they can pair a business-user-focused one with an analytics-first platform. 

If you’re interested in setting up a BI tool for your customer data, we’re here to help. Segment makes it easier for you to collect data from a range of sources and send it safely to your data repository or any of our 200+ integrations

We currently offer native integrations with each of the BI tools described here, plus more. With our partnership with Looker, you can even enrich Segment data with customer cohorts defined in Looker. 

To get started with Segment, you can create a free account here or sign up for a premium, business account demo here.

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