We’ve talked to hundreds of B2B SaaS startups about their data challenges. A recurring theme stood out: the need for self-service BI (business intelligence).

"Self-service BI is the holy grail," one founder remarked. But what exactly is self-service BI, and how does it help companies grow faster? Let’s take a closer look.‍‍‍

What is self-service BI?

An executive told us, “We want everyone in the company to be able to use data without special skills.” The answer? Self-service BI.

In simple terms, self-service BI allows anyone in the company to access, analyze, visualize, and understand data without special technical skills or relying on data professionals.

These tools are built to be intuitive and user-friendly, with features like natural language queries, and interactive dashboards. This democratization of data lets both technical and non-technical users create reports, generate insights, and explore data independently, speeding up decision-making and encouraging a data-driven culture within the startup.

In the hyper competitive landscape of B2B SaaS where agility and rapid responses to market changes are key, self-service BI empowers teams to gain insights and act on them without waiting for the data team to generate reports.

Traditional vs. self-service BI

Let’s take a step back and look at traditional BI for a moment.

Traditional BI relies on a centralized data team to handle everything, from data extraction and transformation to report creation and complex queries. While this can help ensure consistency, it almost universally also creates bottlenecks, as teams must wait for the data team to produce the reports they need. The process can be slow, impacting decision-making and overall business agility. The end result isn't just wasted time – it's people not using data.

Self-service BI, on the other hand, puts the power of data directly into the hands of users across the organization. It allows non-technical users to explore, analyze, and visualize data on their own, without having to rely on the data team.

The power of self-service BI is often best seen when using data to test hypotheses, or to explore ad-hoc questions. Many of the best questions don't have a pre-built report ready and waiting, and self-service BI removes the friction between just having a thought and finding the facts you need to guide your decisions.

This shift from traditional BI to self-service BI significantly reduces delays in report generation and data analysis. It frees up the data team to focus on more complex, high-value analyses and strategic initiatives, enhancing overall efficiency. Moreover, self-service BI fosters a data-driven culture, empowering all team members to make informed decisions based on real-time insights.

Why self-service BI hasn’t worked until now?

The reality is that most BI tools have called themselves “self-service” for many years now. In practice, though, only a select few have been able to go past just clicking around pre-built dashboards.

Part of the reason is that most BI tools were built a really long time ago. Back then, databases needed to be designed ahead-of-time for specific analytical queries you would be running – and this made perfect sense for static dashboards. It was something the data team could do, and that felt like the best that could be done.

Today, companies have incredibly performant cloud data warehouses which are able to scale up compute resources in mere seconds to answer even the most complex ad-hoc questions – questions that the databases at hand weren’t explicitly designed for.

While historically, most companies would just have their own products’ databases to play with, it’s now become near-trivial to bring all those third-party products that modern SaaS teams use into one tool. ETL (Extract, Transform, Load) tools are plenty and integrations are a solved problem.

Recent unlocks in cloud data warehouses and AI have allowed a few companies to reimagine what business intelligence would look like if it truly were self-service. Supersimple, for example, is doing so by combining an unique set of no-code steps, a self-explaining AI copilot and a powerful semantic data model.

Do you still need a data team with self-service BI?
Yes, you do. But with self-service BI, product owners, marketers, and others can handle their daily data needs independently. This means the data team can actually focus on more complex and strategically important work, rather than constant fire-fighting.

For data teams, this shift means:

Fewer ad-hoc requests
More focus on high-value projects
Improved job satisfaction

Now that you have a better understanding of self-service BI, let’s talk about the benefits it offers to B2B SaaS startups.

Learning and adapting quickly using data

Successful startups grow quickly because they learn and adapt quickly. They blend intuition with data to predict customer needs, refine strategies, and scale beyond product-market fit.

Self-service BI plays a crucial role in giving teams easy access to data and insights.

For a SaaS startup, implementing self-service BI offers several key benefits:

Faster decision-making: Teams can quickly access and analyze data without waiting for reports from the data team, leading to quicker, more informed decisions.
Better customer insights: In the fast-paced world of SaaS, the ability to respond rapidly to market changes and customer needs is critical. Self-service BI allows teams to explore data and gain insights in real time.
Operational efficiency: By reducing the need for data specialists, teams save time and can focus on their core responsibilities.
A Data-driven culture: Making data accessible to everyone in the organization helps streamline internal operations and foster a culture of data-driven decision-making.

These benefits clearly illustrate how self-service BI can transform your startup's approach to data. Now, let's look at what features you should consider when selecting a business intelligence tool.

What to look for in a self-service BI tool?

When choosing a BI tool, there are a few key things to consider:

Self-service usability: The tool should be accessible to anyone on the team, allowing them to get answers to complex data questions in minutes. Additionally, natural language processing are key features that make data exploration simple.
Data modelling layer: It’s crucial to get reliable answers to your data questions, and the tool itself should ensure data accuracy and consistency. A robust data modelling layer can be helpful here.
How easy it is to understand what existing reports mean: An under-appreciated part of the problem of self-service BI is the explainability of reports or data explorations. It's critical for users on your team to be able to understand how saved reports and charts work and what the data they see means, in order for them to trust it and take action.
Security and governance: The tool should offer robust security measures and data governance features to protect sensitive information and ensure compliance with regulations.
True setup costs: Many tools are easy to plug into your existing databases, but properly getting started with an usable setup can often infuriatingly be much more difficult.
Support and training: The world of data is complex, and you're bound to occasionally run into complex problems that do need an external helping hand. This is where having an included support plan with fast response times can be a lifesaver. Many vendors require you to separately buy a support plan and only offer "community support" by default, while others include it in the base offering.
Pricing model: Different tools use different structures for their pricing models. In general it's advisable to give (almost) everyone in the company access to the BI tool of choice. Some tools may make this prohibitively expensive, especially when usage ramp-up across the entire company will be a gradual process, as the data culture matures.
How much you'll need to build from scratch: Some tools will make it easier for you to model out or implement certain metrics commonly found among many similar companies. For example, Supersimple can help with a lot of commonly used B2B SaaS metrics in particular.

Challenges of implementing self-service BI

Implementing self-service BI can present several challenges, such as:

Maintaining data quality and consistency: As users generate their own reports and insights, it’s crucial to ensure that the data models and definitions are accurate, consistent, and up-to-date to avoid misinterpretations. Tools like a semantic layer (or data model) can help a lot here.
User adoption: Encouraging users to adopt the new tool and integrate it into their daily workflows can be challenging. While self-service BI tools are designed for non-technical users, some may still require training. BI tools that make it easy to collaborate on data explorations can be helpful here.
Integration with existing systems: Integrating the BI tool with your existing systems can be complex, particularly when dealing with security and compliance issues, diverse data sources, and compatibility challenges. In general, it's easiest to connect a single central data warehouse with your BI tool of choice.

Getting started with self-service BI

Despite the challenges, implementing self-service BI in SaaS startups can be effective with a strategic approach. One of the first steps is to define your goals clearly. Start by identifying specific objectives, whether it’s gaining a high-level overview of the business or addressing a detailed problem like marketing attribution. This focus helps set both short-term and long-term goals, ensuring you achieve quick wins that build momentum.

Next, evaluate your current data infrastructure. Assess your existing data sources, including production databases, CRM systems, and marketing tools, to determine what data you have and where gaps may exist. Establishing a data warehouse that meets your needs—whether it’s BigQuery, Snowflake, or another solution—is essential for consolidating data from different sources.

Choosing the right BI tool is another critical step. Look for tools that meet your requirements in terms of ease of use, scalability, and security. Establishing a semantic layer within your BI tool helps create a unified data model, ensuring consistent and trustworthy data usage across the company.

Creating a small number of core dashboards that are high-quality and relevant can help team members start using data to drive their decisions. It’s better to have a few reliable dashboards than many outdated ones. For product adoption, promoting a data-driven culture from the top down is essential.

How B2B SaaS teams are using self-service BI?

As mentioned earlier, self-service tools empower non-technical users to access, explore and analyze data without having to rely on data teams. In addition, modern self-service BI tools (like Supersimple!) let users go deeper by getting answers to complex, ad-hoc questions within minutes, accompanied by clear and relevant graphs and charts.

To give you a clearer picture, here are some examples of how different teams are using self-service BI tools, and some popular questions they ask Supersimple to get more nuanced insights:

Sales

Sales teams often use self-service BI to analyze performance metrics, track sales targets, and identify trends. This helps them make data-driven decisions to improve sales strategies and drive stable revenue growth.

Some of the most frequently asked questions by sales teams in Supersimple:

Which features were most frequently used by our most profitable customers in January?
How does the deal closure rate vary by acquisition channel?
What’s the average length of the sales cycle across enterprise deals in Q2?

‍

Marketing

Marketing teams use self-service BI to find common lead characteristics and patterns, enabling them to adjust tactics and focus on the most promising prospects. This results in more efficient resource allocation and higher conversion rates.

Some of the most frequently asked questions by marketing teams in Supersimple:

Which lead source has the highest conversion rate in the last five weeks?
Which marketing campaigns are generating the highest quality leads?
Which segment, by job title, converts the fastest?

Product

Product teams use self-service BI to gain a deeper understanding of product usage, helping them guide users to reach the “aha” moment faster.

Some of the most frequently asked questions by product teams in Supersimple:

What in-app behaviour best correlates with churn?
How does time-to-value affect retention?
What features are the most popular among enterprise customers?

Customer success

Customer success teams use self-service BI to explore support ticket data, identifying common issues, measuring response times, and assessing customer satisfaction.

Some of the most frequently asked questions by customer success teams in Supersimple:

What's the latest status of X customer?
Which customer segments have the highest NPS?
What is the churn rate among customers with high support ticket volumes, and what are their common issues?

C-suite

Executives use self-service BI to gain a holistic view of the business based on real-time data and key metrics. This enables them to make better strategic decisions, prioritise and set informed business objectives.

Some of the most frequently asked questions by C-suite:

How's our revenue growing month-over-month?
What was the impact of the latest feature launch on customer engagement?
What is the average revenue generated per user? How has it changed over the past two years?

Final words

Self-service BI is changing the way B2B SaaS startups work with data. By making data accessible and empowering non-technical users, self-service BI tools enable faster, more informed decision-making across your entire organization, leading to greater growth, retention and bigger wins overall.

Self-service BI 101 for B2B SaaS teams