Decoding the Cost of Business Intelligence: Aligning Pricing Models with Value

In this post, we'll take a look at the most common pricing models that business intelligence tools have out there and break them down for you.

Pragati Basu

June 13, 2023

Pricing strategy of BI tools explained

Imagine you’re tasked with finding the best BI solution for your team. Your mind is likely full of questions, one of them being how to understand the total cost of ownership for your chosen tool. The key to successful decision-making lies in aligning the pricing of the tool with the value it provides. In this post, we’ll take a look at the most common pricing models out there and break them down for you.

Unpacking the Value of BI Tools

Before we do that, let’s remember that when it comes to the value of BI tools, the magic isn’t in the price tag. Instead, it’s about the transformative power these tools bestow upon your team. Think of it as equipping your team with a superpower – the ability to make informed, fact-based decisions, quickly and efficiently.

BI tools act as a conduit, funneling vast amounts of data into digestible, actionable insights. Your team no longer needs to spend countless hours sifting through raw data or in meetings with your data analysts. Instead, they can harness the power of a BI tool on their own to understand patterns, trends, and correlations that would otherwise remain hidden in the data labyrinth. 

And more importantly, everyone can make informed decisions, not simply top management.

The result: You’re enabling faster reactions to market changes, a better understanding of customer behavior, and greater operational efficiency. In the technical realm, BI tools contribute to smoother data management, improved data quality, and decreased reliance on IT for data queries.

In essence, investing in a BI tool is like investing in a valuable team member. It’s not about the cost but the efficiency and decision-making prowess it brings to your business. With a BI tool in your arsenal, your team is set to unlock new levels of performance, ushering in an era of unrivaled business efficiency.

Striving for Zero Cost in Information Access

Now let’s talk about ROI and cost.

Imagine a world where access to vital business information is as affordable and ubiquitous as clean water. This is the golden objective many forward-thinking businesses are aiming for – reducing the cost of accessing information to virtually zero. Why? Because, the cheaper the access to information, the more it will be used to make decisions. 

In this vision, the factors that traditionally weigh heavy on the cost of accessing information are – investments in data infrastructure, data extraction tools, labor, and BI tools – skillfully managed and optimized. The path to zero isn’t about eliminating all tools, because information inaccessibility incurs an infinitely higher cost.

Instead, these businesses are strategically investing in scalable, efficient BI tools and infrastructure, democratizing data access throughout their organization. For them, each question answered is a step closer to their goal. 

These seemingly innocuous activities are transforming business intelligence and setting the stage for an era of affordable, omnipresent data access.

Young tech employee thinking

Exploring Pricing Models for BI Tools

As you wade deeper into your quest for the perfect BI tool, you’ll come across various pricing models. These pricing models are critical, and can significantly influence the cost of accessing information. Here’s a more detailed look at the three main pricing models: flat fee, price per user, and price per query.

The flat fee: On the surface, this seems an appealing option. With a one-time payment, your team has free reign to use the tool as much as they want. The more you use it, the more value you extract from your initial investment, thereby reducing the cost for information. The main issue with the flat fee is the upfront risk taken. You’re investing a hefty sum upfront without any guarantee of how much your team will use the tool. In other words, the initial cost is usually high, making it a bigger risk.

Price-Per-Query: On the other end of the granularity spectrum is the price-per-query model. Here, you only pay for the questions your team asks. It seems risk-free since you’re only shelling out money for actual use. However, there’s a significant drawback: the cost to access information doesn’t decrease the more you use it. This model could even discourage your team from using the tool, as they may start to see each question as an additional expense. This model fails to achieve the goal of reducing the cost to access information, which is the exact opposite of what the BI tools of tomorrow are trying to achieve.

Price-Per-User: Hovering between these two extremes is the price-per-user model. Think of it as a flat fee applied at the granularity of a user. This model offers you more control over the cost, mitigating the risk associated with the flat fee model. At the same time, it allows users to extract more value the more they use the tool. The price-per-user is the simplest model that strikes the right balance. It’s mostly aligned with the objective of reducing the cost to access information.

In the complex world of BI tool pricing models, there’s no one-size-fits-all solution. You might encounter other pricing models that are variations or combinations of these three main ones. While these might seem attractive, it’s often more important to keep things simple.

So, when choosing a BI tool, consider not just the upfront cost but the potential value for your team. Ensure you select a pricing model that aligns with your company’s goal of reducing the cost of accessing information.

Beyond Pricing: The Importance of Automation and Scalability

Understanding the pricing model of your BI tool is crucial, but it’s only one part of the equation. A large part of the total cost of ownership also includes aspects like the effort and resources needed for maintenance and scaling.

Let’s dive deeper into this aspect. A great BI tool isn’t just about providing answers—it’s about doing so in a way that is efficient, scalable, and requires minimal manual effort. Automation and scalability in a BI tool could mean the difference between a small team handling data analysis for a global company versus needing a full-fledged department.

Consider, for instance, how Amazon leverages automation in its operations. By automating data gathering and analysis, Amazon can understand customer behavior at scale, enabling them to create personalized experiences for millions of customers globally.

There’s also an increasing interest in AI-powered solutions and LLMs like GPT, which make data exploration as easy as a Google search, thereby scaling access to insights. Our very own AI Analytics Assistant is worth checking out – especially as you can harness the power of it for yourself within your organization as well as within your product ( eg. CRM, ERPs etc.)

These platforms and technologies leverage artificial intelligence algorithms to automatically answer questions, reducing the need for manual dashboard creation and ongoing maintenance. In other words, scalability is front and center here.  Why? Because these solutions not only empower the business user to make better decisions but also reduces the workload on data teams.

It’s important to remember that the automation and scalability of your BI tool can enhance its overall value, irrespective of its pricing model. This is the future of BI—scalable, automated, and highly efficient solutions that turn data into a truly democratized resource.

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