Business intelligence is one of the most familiar categories in the stack, and one of the least tidy. Buyers rarely call it business intelligence — they call it their reporting or visualization tool — and multiple BI products frequently appear within the same company. About one in four companies naming a BI tool in these interviews name at least two, more than in any of the seven neighbouring categories we compared. Some run them together for different jobs; others are moving between platforms or inherited the overlap. And BI is not limited to large enterprises: more than a third of companies under $100 million in revenue in this interview set name one.
What is business intelligence software?
Business intelligence (BI) software helps companies analyze data from multiple business systems — sales and orders, CRM, advertising, web analytics — and present it through reports, dashboards and interactive analysis. The data may be connected directly or prepared upstream in a warehouse or other data platform. Power BI, Tableau and Looker are the names buyers mention most.
Buyers describe it by what it connects: “Power BI is just a visualization tool that helps us bring data from different sources that's not necessarily connected together,” says one energy company. Where there is a warehouse, the BI tool is the layer on top of it — “We use BigQuery on the back end as the data warehouse, and Sigma is just merely the visualization tool.”
The second half of the question is the one buyers can answer that a definition cannot:
BI shows the highest rate of multiple tool mentions of the categories we compared: about one in four companies naming a BI product also name another, more than for web analytics, CRM, email, CDP, warehouse, CMS or A/B-testing tools, and the share climbs with company size, to more than two in five at the largest companies. The interviews establish concurrent use in many cases, although multiple mentions alone do not prove that every named platform remains in use.
And in this interview set BI is nearly as common at small companies as at large ones: the gap is far narrower than for CDPs, CMSs or CRMs, and only email and web analytics are flatter across company size.
What is the difference between BI and web analytics?
Web analytics focuses on activity across websites and apps; BI software supports analysis across many business functions and data sources. Some organizations use BI dashboards as their reporting source of truth, although that reliability depends on the underlying data and how the company defines its metrics. One direct-to-consumer brand uses its BI tool “when we're looking at revenue, anything that's revenue, because it's our source of truth for what actually happens.”
Buyers draw the line by the question being asked. An online grocer splits it cleanly: “It's essentially our source of truth for all financial metrics. Amplitude is our source of truth for product analytics.”
The reporting inside each marketing tool sits on the same side as web analytics: a view of one system. One software company's marketing-technology leader says of its automation platform that “you have to really get that data out and put it in a system like Looker or Power BI to really dig in to see how that data works.”
The vocabulary shows how buyers think about the category. Reporting and dashboards are the everyday words. Buyers say BI far more often than business intelligence, and their most common name for the category is a visualization tool. The technical layer underneath — semantic layers, modelling languages — comes up almost only among data and engineering leaders.
Dashboards, warehouse, source of truth, semantic layer
Dashboards are the unit buyers count and complain about. The warehouse — Snowflake, BigQuery, Databricks — is where joined data usually lives. Source of truth is how buyers separate BI from web analytics. A semantic layer gives tools shared metric definitions, so they calculate consistently when they use compatible data; it is rare in buyer speech.
Who buys and owns BI?
Usually a central data, analytics or BI team builds the dashboards and chooses the platform, and marketing and ecommerce teams consume them. Buyers shopping for a BI tool in these interviews are far more likely than other buyers to hold a data or analytics title.
The structure is plain in how buyers describe it. A media company has “a BI team that manages that from a central function for the company.” A health insurer's digital marketing lead says “My team doesn't use it directly. We consume the data from it.” And the platform is often settled before the user arrives: “I didn't get to choose that. It was a decision that was done by the company before I joined.”
More than a third of interviewed companies under $100 million in revenue name a BI platform, and they tend not to follow the central-team model. Some describe relatively simple arrangements, including a warehouse paired with a single reporting tool: a small restaurant brand's CMO sums it up as “Well, we use BigQuery and then our BI tool is Looker.” The tool tends to change with size — Google's Looker family, including the free Looker Studio, leads below a billion dollars in revenue, and Power BI and Tableau lead above it — though that split is directional, because the Looker figures include Looker Studio users.
Why do companies run more than one BI tool?
Among the companies that explain why they have two or more BI tools — a smaller group than the one in four above, and one in which a company can give more than one reason — some deliberately operate them together, while others describe an unfinished replacement, inherited systems or an included licence. Four routes appear, in this order of frequency:
- Side by side, by team or by job
- Partway through replacing one with the other
- Inherited from a merger, a business unit or history
- An included licence brings a second tool
About half of the explained cases are a move still under way, so two products in one company do not necessarily mean a permanent decision to keep both.
Side by side, by team or by job
Production dashboards in one tool and ad hoc analysis in another, finance and marketing on different tools, or a company-wide tool next to a team's own. Power BI with Tableau is the most common pair.
A telecom company runs both because “We have both of them, and they are both liked by different types of people.” A database software company splits them by job: “The others go into Tableau for production dashboards. For any analysis that an individual analyst might need, we use Looker Studio.”
Partway through replacing one with the other
Mostly Tableau to Power BI, or Looker to a newer warehouse-native tool. The overlap can last years. Where companies end up is on why companies switch BI tools.
One telecom marketer expects that “Tableau is on its way out in the next two years, most likely,” and a fintech describes itself as “kind of like a hybrid between the two, between Looker and Hex at the moment.”
Inherited from a merger, a business unit or history
Departments that chose separately, acquisitions that arrived with their own tool, and BI bundled with an ERP that cannot be removed.
At a large bank, “One department liked QlikView, and another department liked Tableau. Naturally, they took two separate paths.” An industrial manufacturer keeps an ERP's reporting layer because “it's not something we can just remove because it comes with our ERP.”
An included licence brings a second tool
Power BI arrives with a Microsoft agreement beside a tool the company chose on its merits.
A real-estate developer runs two: “One is Looker from Google, and the other one is Power BI because we're a Microsoft-based company.”
And a few companies cannot say. One health insurer's marketing director admits “I couldn't tell you the history of why we have both” — a hint at why the share is so high, since a second BI tool can outlast anyone who remembers adding it.
Which BI tools do buyers use?
Power BI, Tableau and Looker, including Looker Studio, dominate these interviews. Sigma, Domo, Qlik and Hex follow at a distance, alongside newer warehouse-native tools and older enterprise platforms. Ratings for these tools are on what BI buyers wish they'd known.
The three leaders each arrive with a cloud or a suite — Microsoft, Google and Salesforce — and the lessons page covers how that agreement shapes the shortlist. The pull is real but looser than it looks. Companies on Salesforce's CRM name Power BI as often as Tableau, which Salesforce owns, and BI follows the company's cloud less tightly than email, testing or CMS tools follow their marketing suites.
Arrive with a cloud or suite
Power BI (Microsoft) · Tableau (Salesforce) · Looker and Looker Studio (Google)
Independent tools, old and new
Sigma · Hex · Omni · ThoughtSpot · Metabase · Domo · Qlik · MicroStrategy · Cognos · SAP BusinessObjects · Oracle BI
The structural finding sits underneath the list. Because the leaders come with agreements companies already hold, a second tool can arrive without a separate buying decision — the fourth route above, though the least common of the four.
What does running more than one BI tool cost?
Mostly consistency, not licence fees. The cost buyers describe is numbers that disagree: a large retailer's ecommerce team finds that “we don't always see the same numbers across all three,” and a furniture retailer's CRM director notes that “you can seemingly ask the same question twice and get slightly different answers.”
The fixes buyers describe are about definitions, not tools. A health-care company's data leader is “looking to solve a consistency problem that we have with disparate tools all pointing to disparate data sets that are providing disparate results,” and is building a single semantic layer so every tool works from the same metric definitions. Sprawl is the other cost, and it starts with convenience: “I'm just going to put it into Tableau because I've got a Tableau license.” The licence and dashboard-building costs are on what BI buyers actually pay, including consolidation as the saving, and the frustrations with the tools themselves on top complaints about BI tools.
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One is the goal buyers describe when they raise it; two is what many of them have. A sports streaming company's data team put it simply: “We wanted to have a single data visualization tool for the entire company.”
Three things follow, and they are our recommendations rather than practices buyers describe. First, if you already run two, decide which route you are on: tools split deliberately by team or job can be worth keeping, but a replacement that never finished is a cost with no owner. Second, fix the metric definitions before the tool, since disagreeing numbers are the cost buyers name, and a second tool multiplies them. Third, if you are a small company, evaluate BI on reporting complexity rather than company size alone: the interviews include businesses under $100 million using dedicated reporting platforms, sometimes with a warehouse behind them. AI is starting to change the question — one marketing operations lead has seen “my use of Looker decrease pretty substantially as I've been using Claude and Claude Code connected to Snowflake more often,” while an asset manager sees “an uptick in Power BI again because of the integration with Copilot” — but so far only a few of these buyers describe AI replacing the BI tool outright.
For why companies change BI tools, see why companies switch BI tools; for where they go from each leader, see the Power BI, Tableau and Looker alternatives pages.
Common questions
What is business intelligence (BI) software?
BI software helps companies analyze data from multiple business systems — sales and orders, CRM, advertising, web analytics — and present it through reports, dashboards and interactive analysis, with the data often prepared upstream in a data warehouse. Buyers in these interviews mostly call it their reporting or visualization tool, and Power BI, Tableau and Looker are the names that come up most.
What is the difference between BI and Google Analytics?
Google Analytics measures what visitors do on a website. A BI tool joins that with orders, revenue, margin and CRM data from other systems. Buyers in these interviews often treat their BI tool as the source of truth for revenue and financial metrics — a status that depends on the underlying data and metric definitions, not the tool alone — and web or product analytics as the view of behaviour.
Why do companies use both Tableau and Power BI?
About one in four companies naming a BI tool in these interviews name two or more, more than in any other category we compared, and Tableau with Power BI is the most common pair — though naming two does not prove both are still in use. Among the companies that explain it, about half run them side by side for different teams or jobs, and about half are partway through moving from one to the other; fewer inherited the second tool through a merger or a Microsoft licence. A company can give more than one reason.
Do small companies need a BI tool?
Many in these interviews already have one: more than a third of interviewed companies under $100 million in revenue name a BI tool — an observation from this interview set, not a market adoption rate — and the gap between small and large companies is far narrower than for CDPs, CMSs or CRMs. Smaller companies lean toward Google's Looker family, including the free Looker Studio; larger ones toward Power BI and Tableau.
What are the most common BI tools?
Power BI, Tableau and Looker, including Looker Studio, dominate these interviews. Sigma, Domo, Qlik and Hex follow at a distance, alongside newer warehouse-native tools such as Omni and ThoughtSpot and older enterprise platforms such as MicroStrategy.
Who owns BI in a company?
Usually a central data, analytics or BI team builds the dashboards and chooses the platform, and marketing and ecommerce teams consume them. Buyers shopping for a BI tool in these interviews are far more likely than other buyers to hold a data or analytics title.
Will AI replace BI dashboards?
Not yet, in these interviews, but it is changing how some teams use them. In recent interviews a growing minority of companies discussing a BI tool also mention general-purpose AI such as Claude or ChatGPT, and a few are testing whether they need a BI tool at all; others are using their BI tool more because of AI built into it, such as Copilot in Power BI.
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