Chris DaltonChris Dalton

Cloud Based Business Intelligence Software: What Actually Works and What Costs Too Much

January 19, 2026

I went into this thinking cloud based business intelligence software was basically just fancier spreadsheets with a login. I was wrong, but not in the way the sales demos suggested. I set up my first dashboard pulling from the wrong data source for about three days before Derek pointed out I had the live environment and the staging environment mixed up. The numbers looked fine. They were just not real numbers.

Once I got that sorted, reporting that used to take Stephanie most of a Friday started finishing in around 40 minutes. That part was genuine. The pricing, though, I still don't fully understand what tier we're on. Monday.com helped clean up the project data feeding into it, which made a real difference.

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Understanding Cloud BI: Market Landscape and Growth Trends

The cloud business intelligence market has exploded in recent years. Cloud-based BI dominated 66% of the business intelligence market share, with companies increasingly choosing cloud over on-premise solutions for their analytics needs.

The global business intelligence market reached $38.15 billion and is projected to grow at 8.17% annually through the end of the decade. This growth isn't just hype - it reflects a fundamental shift in how organizations approach data analysis.

Why Cloud BI Dominates Traditional Solutions:

The top vendors - Microsoft, Tableau (Salesforce), SAP, Oracle, and Google (Looker) - control approximately 64.1% of the analytics and BI software market. This consolidation means these platforms set the standards for features, pricing, and integration capabilities that smaller vendors must match.

AI and machine learning integration is transforming cloud BI from descriptive analytics (what happened) to predictive analytics (what will happen) and prescriptive analytics (what you should do). Modern BI platforms now offer natural language querying, automated insights, anomaly detection, and forecasting capabilities that were impossible just a few years ago.

Cloud Deployment Models for BI: Public, Private, and Hybrid

Understanding deployment models matters because it affects security, compliance, performance, and total cost. Cloud BI platforms can be deployed in three primary ways:

Public Cloud Deployment:

Your BI tools and data are hosted on a cloud provider's infrastructure (AWS, Azure, Google Cloud). The provider manages all infrastructure - servers, networking, storage, and security patches. You access everything over the internet.

Public cloud offers the fastest deployment, lowest upfront costs, and easiest scalability. It's ideal for organizations without strict data residency requirements or specialized compliance needs. Power BI, Tableau Cloud, and Looker all primarily operate as public cloud services.

Trade-offs include less control over infrastructure, potential data jurisdiction concerns, and shared resources with other tenants. For most mid-sized businesses, these trade-offs are acceptable given the cost savings and convenience.

Private Cloud Deployment:

Your organization maintains a private cloud environment - either on your own premises or in a dedicated hosted environment. Only your company's BI systems and data exist in this infrastructure.

Private cloud provides maximum control, customization, and security. It's essential for highly regulated industries like healthcare, finance, and government where data sovereignty and compliance requirements are strict. You decide exactly how workloads are deployed, monitored, and secured.

The cost is significantly higher. You're responsible for hardware procurement, maintenance, upgrades, security, and staffing IT teams to manage everything. Scaling requires purchasing additional hardware rather than clicking a button.

Tableau Server and on-premise SQL Server Reporting Services represent private cloud options, though many organizations are migrating away from this model due to cost and complexity.

Hybrid Cloud Deployment:

Organizations split BI workloads across both public and private clouds. Sensitive data stays in a secure private environment while less critical analytics workloads run in the public cloud for scalability and cost efficiency.

Hybrid offers the best of both worlds when implemented correctly. You can keep customer financial data and protected health information in your private cloud while using public cloud for marketing analytics, sales dashboards, and development/testing environments.

Complexity is the trade-off. Managing hybrid environments requires strong governance, integration capabilities, and skilled teams. Without proper architecture, costs and management overhead spiral quickly. Many enterprises use hybrid approaches during migration periods - gradually moving workloads from on-premise to cloud as comfort levels increase.

For BI specifically, hybrid works well when you need to query data that resides in both environments. Modern BI platforms support hybrid connectivity through secure gateways and VPN connections.

What Cloud BI Actually Costs

Pricing for cloud business intelligence platforms varies wildly - from $10/user/month to over $100,000 annually for enterprise deployments. Mid-sized businesses typically spend $70-$350/month on cloud BI, but that's just licensing. Implementation, training, and ongoing maintenance add 15-20% annually.

The average cost of a cloud-based BI solution runs around $125,000 per year according to Dresner Advisory Services. For comparison, on-premise solutions average $200,000 annually when you factor in hardware, software, and infrastructure costs.

Cloud-based subscription models accounted for the largest share of BI revenue, representing a fundamental shift from perpetual licensing. You're paying for continuous access rather than owning software outright.

Microsoft Power BI: Cheap Upfront, Hidden Costs Later

I came in thinking we'd need maybe four or five licenses. We ended up with somewhere around fifty because apparently everyone who wants to look at a dashboard counts as a user. I didn't know that going in. Chad didn't know that either. We figured it out after the invoice came back looking wrong.

What worked for us:

What gave me trouble:

Once I stopped fighting the structure and built the model the way it wanted to be built, things moved faster. I was pulling clean reports in about 11 minutes that used to take me close to an hour in the old setup. That part I didn't expect.

The upgrade tier unlocks bigger file limits and more daily refreshes, which we looked at seriously. We didn't pull the trigger because the next step up in capacity pricing is a significant jump and Tory wasn't convinced we'd use what we were paying for. That conversation is still ongoing.

Tableau: Beautiful Dashboards, Premium Pricing

I'll be upfront: the pricing confused me for longer than it should have. I kept thinking the middle tier was the one you needed to actually build anything, but it turns out you need at least one of the top-tier licenses no matter what. I bought the wrong thing first. Chad had to explain it to me twice.

What Worked:

What Didn't:

There's an enterprise tier that adds more admin controls and some training access. There's also a newer bundle on the cloud version with agentic AI features that I didn't test -- pricing on that requires a sales call, which usually means expensive.

The honest version: if your job is building dashboards that a lot of people look at, this is probably the right tool. I had ~11 dashboards running across two departments before it really felt worth what we were paying. If you're mostly doing internal reporting for a small team, the price is hard to justify.

Looker: Google's Expensive Governance Play

I got access through a demo our sales rep arranged. Took about six weeks from first email to actually logging in, which felt long. Chad kept asking if we'd heard back yet. We had not.

The first thing I tried to build was a dashboard pulling from three different tables. I didn't realize I was supposed to define the relationships in LookML before any of that would work. I spent probably two days building things in what I now understand was the wrong order. The dashboard existed. It just wouldn't populate. Derek figured out what I'd done wrong and was nice about it.

Once someone who actually knew LookML set up the underlying model, the explore interface made more sense. I could pull cuts of data without writing SQL, which was the whole pitch. That part did work. I ran about 23 different report variations across two business units before I stopped second-guessing the numbers.

What worked:

What didn't:

This is cloud based business intelligence software built for organizations that have already decided governance is a priority and have the technical staff to execute on that. It is not something you open and figure out over a weekend. Tory asked if we could just use it for one department. Technically yes. Practically, at the price point, probably not worth it unless that department is running very high data volume and needs strict consistency across teams.

The warehouse costs were the thing nobody fully warned us about. Every query runs live against the warehouse. That adds up.

Sisense: Embedded Analytics Specialist

I came into this one expecting something closer to a standard cloud based business intelligence software setup. It's not that. The embedded analytics angle threw me off early -- I kept looking for a dashboard builder and didn't realize I was already in it.

The In-Chip processing is real. I had a dataset that was choking another tool and this one moved through it without much complaint. I clocked the query at around 40 seconds on something that was timing out elsewhere. That part worked.

What didn't work, at least for me: I built the data model backwards. I connected the sources before setting up the ElastiCube structure and spent probably two days untangling it. Derek looked at it and didn't know either. I eventually found the right order in a forum post, not the docs.

The visualizations are fine but not great. I needed a specific chart type and found out it was a paid add-on after I'd already built around it. That was annoying.

If you're a SaaS team shipping dashboards to your own customers, this probably fits. If you're just running internal reports, the pricing doesn't make sense for what you're actually using. I was mostly in the second category and felt it.

Domo: All-in-One Platform with Vague Pricing

I had to ask for a quote three times before I got an actual number. Chad eventually found a figure in some industry thread -- around $1,500 per user, which I could not confirm anywhere on their site. We ended up estimating based on what other companies said they paid. That part was genuinely frustrating.

What worked: The drag-and-drop data prep tool was the thing I used most. I connected maybe 11 sources before I realized I'd been pulling duplicate tables the whole time -- that was my fault, but it took a while to untangle. Once I figured it out, Linda could clean and reshape data without looping in IT. That part actually worked the way it was supposed to.

What didn't: I duplicated a report to test some changes and it edited the original. I didn't know the copies were live-linked. Derek noticed before it went anywhere, but that could have been a problem. The SQL editor also rejected some syntax that runs fine everywhere else. I rewrote the same query four times before it accepted it.

It tries to handle everything -- storage, prep, dashboards, apps. For a large team with budget, that probably makes sense. For us, it added complexity we didn't need and cost we couldn't fully justify.

ThoughtSpot: AI Search for Analytics

I typed a question into the search bar like I was Googling something. "Show me sales by region last quarter." It actually worked. I don't know what I expected but I was ready to be disappointed and I wasn't. Derek saw it over my shoulder and asked if I'd set up something special. I hadn't. That was just the thing.

Where I lost time was the data modeling side. I connected our tables in what I thought was the right order and the search kept returning weird totals. Took me probably three sessions to figure out I had the relationship backwards. Once I fixed it, the automated insight feature started surfacing stuff that made sense, maybe six or seven anomalies flagged in the first real run that were actually worth looking at.

It's not a dashboard builder. I kept trying to use it like one and it kept not being that. Linda uses a separate tool for the formatted reports. This handles the questions in between.

Pricing I don't fully understand. There's a per-user number on the site and then a much larger number that shows up in contract conversations. I just forwarded it to Stephanie.

If your data is messy, fix that first. This won't fix it for you.

Emerging Platforms and Alternatives

Qlik Sense: Holds approximately 4% of the BI market. Known for associative data exploration and in-memory processing. Pricing comparable to Tableau but with different strengths in data relationships and exploration.

SAP BusinessObjects: Commands 20.49% market share in analytics platforms. Strong in enterprise environments already using SAP ERP systems. Complex pricing and implementation but deep integration with SAP ecosystem.

MicroStrategy: Released updates enabling AI-driven data insights embedded directly into web and mobile applications. Strong in large enterprise deployments with complex security requirements.

GoodData: Launched GoodData Cloud on AWS with API-first architecture and global scalability. Focus on embedded analytics for multi-tenant SaaS applications.

Tellius: AI-driven decision intelligence platform unifying data analytics with natural language querying and machine learning. Bridges traditional BI and advanced analytics for trend discovery and automated insights.

For sales and marketing data specifically, Lemlist provides campaign analytics that feed into your BI dashboards, helping track outreach performance alongside other metrics.

What Actually Matters When Choosing Cloud BI

The first thing I checked was how many of our existing tools it could actually pull from. We had a CRM, an ERP that was older than most of our team, and a couple of spreadsheets that Linda had been maintaining since before I joined. Some platforms handled this fine out of the box. One of them made me build a custom connector for the ERP. I spent probably three days on that before Derek mentioned there was a native option buried in the settings I hadn't found. That was on me, but still.

Real-time data was something I assumed would just work. It didn't always. One of the tools I tested was doing scheduled refreshes every four hours and I thought it was live. I built a whole dashboard around it before Stephanie pointed out the timestamp in the corner. Worth checking before you commit to anything.

Licensing was the part I kept getting wrong. I kept budgeting for our actual team and forgetting that Chad and Tory and the three people in ops who "just want to see the numbers" all need some kind of access too. When I finally counted everyone who'd asked me for a login in the previous month, it was almost three times what I'd planned for. The per-user model that looked cheap at first stopped looking cheap pretty fast. Capacity-based pricing made more sense for us once I ran the real numbers, but it came with a minimum commitment that made me nervous.

There's also a difference between who builds the dashboards and who just looks at them. I didn't know that distinction mattered for pricing until I'd already set everyone up the same way. Fixing it took about half a day and saved us something like 40 percent on the annual cost. I wish someone had told me that earlier.

The visualization versus governance question sounds abstract until you're in it. I cared about governance more than I expected to. We had three different definitions of "active customer" floating around depending on which dashboard you were looking at. Jake pulled a number in a meeting that contradicted what I had. That was an uncomfortable ten minutes. The tools that enforce centralized metric definitions are worth more to me now than the ones with the prettier charts.

Training took longer than I budgeted. I assumed a few hours. For the tool that required its own modeling language, I was probably at 30 hours before I felt like I wasn't guessing. The platforms aimed at business users were faster to start but hit walls faster too. My honest benchmark: I ran about 11 connected reports across four departments before the logic of the whole thing actually clicked for me.

Performance was fine until it wasn't. Small data, no problem. When I pulled in two years of transaction history across multiple regions, one platform took 28 seconds to load a single dashboard. That's not usable. I ended up running a test with our actual data volume during the trial period, which I'd recommend doing before you sign anything.

The AI features look good in demos. In practice, I only found one of them genuinely useful on a regular basis, which was the natural language query tool. I could type something close to a real question and get something close to a real answer. The anomaly detection flagged things that weren't anomalies three times before I turned it off.

Mobile access matters if your team is ever not at a desk. The quality difference between platforms is bigger than I expected. One of them was essentially the full desktop experience on a phone. Another one felt like a read-only version with half the filters missing. Test it on an actual phone before deciding it's fine.

If you're thinking about embedding dashboards into something customer-facing, that's a different product decision entirely. The standard licensing on most of these tools is not built for that. There are specific editions and pricing structures for embedded use that I didn't know existed until I tried to do it the normal way and it didn't work.

Choosing the right cloud based business intelligence software mostly came down to what I actually needed versus what looked good in the feature list. Those were not the same thing.

Hidden Costs Nobody Warns You About

Nobody told me implementation was a separate bill. I assumed it was included. It wasn't. We're a mid-sized team and by the time someone explained what "professional services" meant on the quote, we were already past the point of turning back. I think we ended up somewhere around $22,000 for setup, which felt like a lot for something I thought was just logging in and connecting a spreadsheet.

Training was its own thing on top of that. Linda went through the certification program first. I think she said it was around $1,200. Then Derek did it. Then they told me I should probably do it too. Multiply that out and it adds up faster than you expect. And even after, there's this period where everyone technically knows the tool but nothing is actually getting done with it. For us that was closer to three months. The dashboards existed. Nobody was opening them.

The data warehouse costs caught me the most off guard. I didn't realize every time someone loaded a dashboard, it was running a query, and that query cost money. We went from around $2,300 a month to just under $9,000 over about six weeks without changing anything we were doing. Tory figured out it was a report Chad had set to refresh every 15 minutes. Against a large table. All day. Every day. We changed it to twice daily and the bill dropped almost immediately. I still don't fully understand why 15 minutes was even an option someone could set without a warning.

Support costs more if you want someone to actually respond. The standard tier is technically included but I sent a ticket in on a Tuesday and heard back the following Monday. We ended up on a higher plan. I don't remember the exact number but it was not small.

The part I wish someone had said out loud earlier: if you pick the wrong platform and need to switch later, you are essentially starting over. Dashboards don't transfer. Training doesn't transfer. The work Stephanie put into governance doesn't transfer. Jake looked into what a migration would run and came back with a number that made everyone go quiet for a second. We're not switching.

Cloud based business intelligence software has real value but the sticker price on the subscription is probably 60% of what you'll actually spend in year one, if that.

Data Preparation: The Hidden Time Sink

Nobody warned me how much time I'd spend before the actual software even mattered. I thought we'd connect our data sources, build some dashboards, and start making decisions. That's not what happened.

The first two weeks were mostly me and Derek trying to figure out why our membership numbers didn't match between systems. Turns out one source was counting cancelled accounts differently. We spent probably 60% of our time just on that -- not on analysis, not on dashboards. On figuring out what the data even meant before we could use it.

I had set up the transformation step in the wrong order. I was cleaning the data after merging the tables instead of before, which meant duplicates were getting baked in. Once Stephanie pointed that out, the numbers stabilized. I don't know how long I would have kept running it wrong.

The tool itself has a visual prep interface that I actually liked once I understood it. But I kept building one-off queries for every new dashboard instead of building something reusable. By the third dashboard I realized I'd done the same calculation four times. That was on me, not the software.

After we cleaned up the pipeline structure, our reporting prep time dropped from around 9 hours a week to closer to 2. That felt significant. But honestly most of that gain came from fixing the data source, not from anything inside the cloud based business intelligence software itself.

If I were starting over, I'd put more budget into the pipeline side before touching the BI layer at all.

Self-Service BI vs. Governed BI: Finding the Balance

There's a setting I ignored for the first two weeks that ended up being the whole point. The platform lets you build your own reports without going through IT, which I liked. I built maybe six dashboards before Chad pointed out that three of them were calculating revenue differently. Not wrong exactly, just inconsistent. That was my fault for not starting from the certified datasets.

Once I figured that out, I rebuilt everything from the approved data foundations and it took about 40 minutes to get the same dashboards back in a cleaner state. The numbers matched Linda's version for the first time.

The governed side of things is stricter than I expected. I had a Viewer license for the first month and kept hitting walls trying to pull anything exploratory. I thought something was broken. Tory had a Creator license and could do things I couldn't. I didn't fully understand the license tiers until I asked someone. I still don't totally understand the pricing behind them.

The balance between open access and locked-down reporting is real but it isn't automatic. Someone has to set the guardrails. In our case that was Derek, and until he finished the role-based access setup, the cloud based business intelligence software was a little chaotic. After that it mostly made sense.

What to Actually Do

Here's what I'd actually tell someone starting out with cloud based business intelligence software: don't buy more than you need on day one. I did. It took about three months to figure out we were paying for seats nobody was logging into.

If you're under 50 people, start with the cheaper Power BI tier. The $14/user option handled everything we needed for the first several months. I upgraded us to the next tier because I thought we needed faster data refreshes, and honestly I'm still not sure we did. The dashboards looked the same to me. Chad said they loaded faster. Maybe they did.

If your team lives in Google products instead of Microsoft, there's a free option worth trying before you spend anything. I didn't know it existed until Stephanie mentioned it. Would have saved us about six weeks of evaluation time.

For mid-sized teams, the math gets weird around 250 to 300 users. There's a flat-rate capacity option that sounds expensive until you realize it covers unlimited viewers. I ran the numbers wrong the first time because I forgot to account for viewer licenses separately. Ran them again and the breakeven was somewhere around 280 users for us. If you're close to that number, just do the multiplication before you commit.

If your team does a lot of executive presentations or sends reports to clients, the more expensive visualization platform is probably worth the gap in price. I think it was around $75 versus $14 for a creator seat. Derek pushed for it and he was right. The difference in how the charts look is noticeable when you're putting something in front of a customer.

For larger organizations, the governance-focused platforms make sense when you've got multiple teams pulling different numbers and arguing about whose version is correct. We had that problem for a while. Linda and Jake were each running revenue reports that never matched. The more structured platforms force everyone to use the same definitions. That alone is worth something.

If you're building analytics into a product you sell, the embedded options are a different category entirely. I went down the wrong path on this for about two weeks before someone explained that the per-hour pricing model I was looking at was designed for continuous use, not the on-demand thing I was trying to do. Ended up being more expensive than I expected. Ask the sales team to walk through your specific usage pattern before you sign anything.

Try it with your actual data. Every platform has a free trial. I tested one with a cleaned-up sample dataset and it performed great. Loaded it with our real data, around 4 million rows, and the query times were rough enough that we switched platforms before going live. The trial is the only thing that matters.

During the trial, ignore the demo dashboards they set up for you. Build one thing yourself, with your own data source, and see how long it takes. It took me about 40 minutes to connect our database and get a working report on my first try. That felt reasonable. On a different platform it took most of an afternoon and I needed IT involved. That told me everything.

Watch your adoption numbers before you expand. We hit about 34% active users out of licensed seats before we caught it. That's a lot of money sitting unused. Fix adoption in one department before you roll anything out company-wide. Tory figured out most of our non-users had never gotten past the login screen.

Industry-Specific Considerations

Healthcare: HIPAA compliance requires strict data security and audit trails. Private or hybrid cloud deployments often necessary. Looker and Power BI support healthcare compliance with proper configuration. The healthcare BI market posts 12.92% growth rates through the end of the decade - fastest across verticals - driven by value-based reimbursement and precision medicine analytics.

Finance: SOX compliance and regulatory reporting demand governed analytics with audit trails. Looker's LookML ensures consistent definitions across reports. SAP BusinessObjects integrates deeply with financial systems. Data residency requirements may mandate private cloud deployment.

Retail: Real-time inventory analytics and customer behavior tracking require fast refresh rates and large data handling. Power BI Premium or Tableau Cloud work well. Seasonal traffic spikes benefit from public cloud scalability.

Manufacturing: IoT sensor data from factory floors creates massive datasets. Sisense's In-Chip technology handles large-scale manufacturing data efficiently. Supply chain analytics optimize operations and reduce waste.

Telecom: Network capacity analytics and churn reduction focus areas. Real-time streaming data requires specialized capabilities. Cloud BI enables analysis of call detail records, network performance, and customer usage patterns at scale.

Multi-Cloud and Best-of-Breed Strategies

Organizations increasingly adopt multi-cloud strategies, using services from multiple providers to avoid vendor lock-in and optimize costs. This applies to BI platforms as well.

A best-of-breed approach might use:

This complexity requires strong integration capabilities and skilled teams to manage. Without proper architecture, costs and management overhead spiral.

The alternative is all-in-one platforms like Domo or Microsoft Fabric that combine data integration, warehousing, BI, and analytics. These reduce complexity but create vendor lock-in and may not excel at any single function.

Evaluate your team's capabilities honestly. Best-of-breed delivers superior results with skilled teams. All-in-one platforms work better for teams lacking specialized expertise.

Future Trends in Cloud BI

Augmented Analytics: AI automates data preparation, insight generation, and natural language explanations. Users ask questions in plain English and receive answers without building queries. Power BI, Tableau, and ThoughtSpot lead this trend.

Real-Time Analytics: Organizations expect instant insights rather than overnight batch processing. By the end of the decade, 70% of organizations will leverage real-time analytics for decision-making, up from 40% previously. Streaming data from IoT, clickstreams, and transactions drives this requirement.

Data Democratization: Organizations promoting data democratization (making analytics accessible to all employees) outperform peers on key business metrics by 20%. Cloud BI platforms lower technical barriers through intuitive interfaces and self-service capabilities.

Embedded and Operational Analytics: Analytics integrated directly into workflows and applications rather than separate dashboards. Alerts trigger actions. Insights appear in context where decisions happen.

Data Governance and Privacy: Organizations fully invested in data governance outperform peers financially by 20%. As data volumes grow and regulations tighten (GDPR, CCPA), governance becomes competitive advantage rather than compliance burden.

Continuous Intelligence: Automated, real-time data ingestion and analysis identify opportunities, trends, and irregularities that might otherwise remain unknown. This shifts from periodic reporting to always-on intelligence.

The Bottom Line on Cloud BI Software

Power BI won by default for us. It was already in the budget because we were paying for Office 365 anyway, and Chad had it connected to our data in about a day. I set up my first dashboard backwards -- I had the filters applying to the whole page when I only wanted them on one chart. Took me a while to figure out why everything kept changing at once. Once I sorted that, it was fine. Not exciting, but fine.

Tableau is genuinely prettier. I'm not going to argue with that. Stephanie pulled together a deck for a client using it and it looked like something a design team made. The cost is real though. I didn't fully understand the licensing when we trialed it and ended up with seats we couldn't use. I think we paid for six and only three people ever logged in.

The one with the proprietary data modeling language -- the expensive enterprise one -- we looked at it and closed the tab. Someone sent me a quote and I had to read it twice. Linda said it made sense if you had a whole data team. We do not have a whole data team.

The embedded analytics platform geared toward SaaS products is genuinely good at what it does. We were not building customer-facing dashboards. We were just trying to see our numbers. I kept bumping into features that clearly weren't meant for someone in my situation. Not bad software, just the wrong tool for what we needed.

The all-in-one platform that doesn't list prices anywhere -- I spent probably 45 minutes trying to find a number on their site before filling out a form. Someone called Derek the next morning. He said he wasn't ready to talk to sales yet. That was the end of that.

The search-based one where you type a question in plain English and it builds the chart -- I actually liked that part. I ran about 23 queries before I hit something it couldn't handle cleanly. It was a year-over-year comparison with a specific filter and it just returned something sideways. Not wrong exactly, just not what I asked for.

The real cost that nobody led with: implementation, getting your data clean enough to use, and the hours spent on training people who then don't log in. Tory went through the full onboarding and opened the dashboard maybe four times after that.

If I were starting over I'd start smaller. Pick the cheap one, connect it to something real, see if people actually use it before buying more seats. We bought twelve licenses upfront and I think six got used consistently.

For tracking sales numbers specifically, we ran it alongside Close CRM and that's where the pipeline visibility actually came together in a way the team checked without being asked.

Cloud-based business intelligence software is not something you buy and then have. It's more like something you tend. The organizations I've seen get real use out of it treated it that way from the start. Start with one question you actually need answered and build from there.

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