8 Best Self-Service Analytics Tools In 2024

What Exactly Is Self-Service Analytics?

Self-service analytics is either a revolutionary breakthrough, or a disaster waiting to happen—depending on who you ask.

On one side, you have enthusiasts, who touts self-service business intelligence as the “ultimate tool” for “data democratization”, without waiting for IT. It’s the promise of agility and efficiency, where decisions are driven by real-time data, not gut feelings or stale reports. If you’ve been in BI space long enough like me, you might squirm at these verbose.

On the other side, people think that self-service is a myth, a feeling at best, that self-service analytics means 90% of the time the finance, marketing, and compliance teams will find a way to circumvent the data warehouse, query the data at the source, extract it to an Excel file, and build their department-specific aggregations and measures on the fly with no version control or governance.

It’s like Bigfoot - some believe it’s a myth, an illusion sold by vendors and consultants eager to push the latest trend. Yet, others swear by it, claiming they’ve seen it in action, transforming their organizations into data-driven powerhouses.

Between these extremes lies a more nuanced reality.

Realistically speaking, every data team will run into a situation where the data team has to scale to meet the growing business intelligence needs of your company:

1. You continually hire more data analysts to keep up with demand. Every request routes through a data analyst, and the bulk of the analyst’s job is to build dashboards and act as an English-to-SQL translator
2. You equip a small data team to empower the entire organization to get the data they need. Some complex queries continue to be handled by analysts, but the bulk of requests are self-served from some dashboard, report, or drag-and-drop interface.

We think the second option is the more scaleable solution of the two.

This also means a self-service analytics tool (tooling) can’t stand in silos from people (culture) and processes. The truth is that business intelligence problems are socio-technical problems, and you usually have to fix some combination of people (read: culture) and process and tool, all at the same time.

As tool makers, we usually say to ourselves

No data tool can ever help you achieve data literacy in your company. But we should make it easier for you to do so, or at the very least, make sure we don't get in the way

In this article, we’ll go into what makes a good self-service analytics platform, analyze the best tools in the market, and go into a few success case studies to help get a better picture of what success looks like, and how to achieve it with a combination of People, Process and Tool.

8 Features That Self-Service Analytics Tools Should Have

Not all self-service analytics tools are created equal.

Some promise the world but deliver a steep learning curve; others are so user-friendly that you wonder if they’re too simplistic. To truly empower users while maintaining data integrity, a self-service analytics tool needs to strike a delicate balance between functionality and ease of use.

Here’s a closer look at the key features that make a BI tool self-service-able!

• User-friendly interface & Ease of Use: The best self-service analytics tools are designed with the non-technical user in mind. A drag-and-drop interface, intuitive dashboards, and simple data visualization options are essential. These features lower the barrier to entry, allowing users with little to no technical background to explore data and generate insights.
• Customizable dashboards: Every business user has different needs, and a one-size-fits-all dashboard simply won’t cut it. The ability to explore and customize dashboards allows users to focus on the metrics that matter most to them.
• Collaboration features: Insights are only valuable when shared. Collaboration features—the ability to easily share dashboards to Slack and emails, send alerts, share password-protected shareable links, or create embedded analytics dashboards —are essential. This helps cross-department teams work together, align on strategies, and make informed decisions based on the same set of data.
• Single source of truth and centralized logic: One of the biggest challenges in data-driven decision-making is making sure that everyone in the organization is working with the same definitions and metrics. A self-service analytics tool should allow you to centralize data logic and metrics ensuring that reports generated across different departments are accurate and consistent.
• Variety of visualization options: A robust self-service analytics tool should offer a wide range of visualization options, from basic charts and graphs to more complex visualizations like heat maps, scatter plots, and geographical maps.
• Data Governance and Granular Role Permissions: Sensitive data should be tightly protected and users only have access to the data they need. This helps maintain compliance with data protection regulations and ensures that the right people are making the right decisions with the right data.
• Version Control: When more people build metrics and dashboards, the BI reporting system can become this big chunk of spaghetti logic that nobody dares to touch. Dashboards break out of nowhere. Your self-service BI should allow you to define analytics and dashboard as code and govern them with Git version control.
• Usage Monitoring: A lesser-known but equally important feature of self-service BI tools is usage monitoring. This capability allows the data team to identify under-used dashboards, prune redundant reports, discover data champions, support struggling data novices, and share best practices, improving the overall data proficiency across the entire organization.

The Best Self-Service Analytics Tools (Updating)

1. Holistics

Holistics is a self-service BI platform that strongly focuses on data governance and centralization to keep metrics consistent across teams.

It offers a wide variety of features designed for self-service analytics, governance, and customization.

Key features:

• Centralized data modeling: With a code-based semantic layer approach, Holistics allows the data team to define data logic and metrics centrally, ensuring all teams use the same well-defined metrics and dimensions.
• Easy data exploration: Non-technical users can easily filter, sort, and drill down into data within an intuitive drag-and-drop interface. Common analytics functions like Percent of Total or Period-over-Period comparison are all 1-click functions, natively built-in.
• Robust variety of visualizations: Offers a wide range of visualization options, from basic charts to more advanced geographical maps.
• Dashboards as code: Data teams can build customized dashboards using code and turn them into reusable templates. This also allows analysts to turn rigid dashboards into living documents with interactivity, business context, and storytelling.
• Embedded analytics and external analytics: Users can easily send reports to Slack and email, share data securely with password-protected sharable links, and embed dashboards into their own documents/applications.
  

The Cons

• No predictive analytics feature: At the moment, Holistics does not support predictive modeling. For instance, you can’t deploy machine learning models to forecast propensity to buy based on your data in Holistics.
• User experience (UX): Holistics might seem rough around the edges for some users, with the interface can feel less intuitive compared to other BI tools.

Pricing: Starts at $150/month

2. Metabase

Metabase is an open-source business intelligence tool that helps users answer their data questions using different visualizations.

Key Features

• Ease of use: Metabase’s point-and-click interface is designed for non-technical users, making data exploration straightforward without needing SQL.
• Simple query builder: For those with SQL skills, Metabase offers a simple query builder, allowing for deeper data analysis and custom queries.
• Open-source flexibility: As an open-source tool, Metabase offers significant flexibility for customization and integration, tailored to your organization’s specific needs.
• Basic visualizations: Metabase covers the essentials with bar charts, line graphs, and more—sufficient for most business needs.
• Question feature: Metabase has this question feature that lets you answer your simple and daily data questions. In “Simple question” mode, you can filter, summarize, and visualize data. If you have a more complex question, you may choose “Custom questions” which gives you a powerful notebook-style editor to create more complex questions that require joins, multiple stages of filtering and aggregating, or custom columns.

The Cons

• Heavily dependent on MySQL for complex analysis: If your query is too complex for the question feature, you need to write your own MySQL script to get your desired results. This is not user-friendly for people with limited SQL knowledge.
• Performance at scale: It works well for smaller datasets but can struggle with performance when scaling up, particularly with complex queries.
• Security and governance: Being open-source, Metabase may require additional setup for enterprise-level security and governance, which could be a hurdle for some organizations.
• Lack of automated data mapping: Unlike its competitors that automatically do the data mapping between database tables and business logic once the data source is integrated, You need to do your data mapping manually in Metabase and this leads to less flexibility and lack of customization.
  

Pricing: Starts at $85/month, with $5/month per user

3. Looker

Looker is an enterprise cloud-based self-service BI tool owned by Google that sits on top of your SQL database and helps you model and visualize your data.

Key Features

• Strong data modelling capabilities: Looker has its own data modeling language called LookML. With LookML you can define your dimension, metrics, calculations, and data relationships in A SQL database.
• Predictive analytics: Looker offers various data tools that can help you get the most out of your analysis including ML models that can be deployed in your dataset. For instance, There are BigQuery ML models available within the Looker Marketplace including classification, regression and time series forecasting models.
• Flexible data exploration: Users can create custom reports and dashboards with a drag-and-drop interface, and drill down into data for detailed insights.
• Robust data integration: Looker connects directly to a wide range of databases and cloud warehouses, ensuring real-time data access without the need for extraction. It integrates well with tools like Slack and Google Sheets and other Google Products, making collaboration easier.

Limitations:

• Complexity of LookML: LookML is powerful but has a steep learning curve, requiring technical expertise which may increase reliance on data engineers.
• High Cost: Positioned as a premium product, Looker’s pricing may be a barrier for smaller organizations. Looker pricing is reported to start at $35K/year.

Pricing: Starting at $35K/year.

4. Thoughtspot

ThoughtSpot is well-known for its focus on AI-powered, search-driven analytics, making data exploration easy even for non-technical users. Here’s why it stands out as a self-service analytics platform

Key features:

• Search-driven analytics: Type in a question in plain English, and ThoughtSpot delivers instant insights. This makes it super accessible, even for those without a tech background.
• AI-powered insights: The platform uses AI to automatically highlight trends, anomalies, and hidden opportunities you might not have noticed.
• In-memory computing: This feature speeds up query processing, making it great for organizations that need fast decision-making.

Limitations:

• Complex setup and maintenance: ThoughtSpot is easy for users but can be tricky to set up and maintain, especially in larger, complex environments.
• High cost: It’s a premium product with pricing to match, which might be a barrier for smaller businesses.
• Limited advanced customization: ThoughtSpot may not offer the deep customization options for reports and dashboards that other BI tools do.

Pricing: Starts at $1250/month

5. Tableau

Tableau is a powerhouse in BI, known for its stellar data visualization and user-friendly design.

Key Features

• Excellent data visualization: Tableau excels at creating a wide range of visualizations, from simple charts to complex interactive dashboards, all through an easy drag-and-drop interface.
• Real-Time analytics: Tableau supports live data connections, ensuring your insights are always up-to-date, which is critical for agile decision-making.
• Collaboration features: Share your insights easily with Tableau Server or Tableau Online, fostering a data-driven culture across teams.
  

The Cons

• High learning curve for non-technical users: Unlike other BI tools such as Holistics or Looker that allow non-technical users to explore data and generate insights, the majority of Tableau users are experienced analysts or developers as setting up data models and generating insights sometimes need programming knowledge such as SQL, R, and Python. Business users can self-serve with Tableau, but it often involves a lot more training.
• Lacking built-in self-service BI features: Tableau may not have built-in support for certain features that are needed for self-service exploration, such as fiscal calendars or relative date range defaults on the date slider.
• Difficult to embed into organization’s products: You can embed Tableau into external applications such as internal knowledge bases, CRMs, and blog posts. However, It can be a real challenge for an organization from both financial and technical perspectives to seamlessly integrate Tableau.
  

Related articles:  Best Embedded Analytics Software for Modern Data Stack

06. Lightdash

Lightdash is a fresh player in the BI scene, built specifically for teams using dbt (data build tool) for data transformations.

It’s a developer-friendly platform that turns your existing dbt models into customizable dashboards and reports. Here’s what makes Lightdash tick as a self-service BI:

Key Features:

• Seamless dbt integration: Built with dbt users in mind, Lightdash integrates directly with your dbt models, avoiding duplicate efforts and keeping your data consistent.
• Developer-centric: Ideal for teams comfortable with SQL and coding, Lightdash lets developers craft and tweak dashboards straight from dbt models.
• Open-Source flexibility: As an open-source tool, Lightdash gives you plenty of room to customize, integrate, and even contribute to its development.

The Cons

• Product immaturity: Since Lightdash is new to the market and still in early development, its visualization options are quite limited in comparison to other BI tools.
• Steep learning curve: Geared towards technical users, non-developers may struggle without extra training.
• Performance Issues: Still evolving, Lightdash may face performance hiccups with large datasets or complex visualizations.

Learn more:  How Lightdash Could Be Better

07. Sigma Computing

Sigma Computing is designed to make cloud data warehouses accessible to everyone, offering a spreadsheet-like interface that’s intuitive for users without SQL skills.

Key Features:

• Spreadsheet-Like Interface: Feels familiar to Excel users, making it easy to create reports and analyze data without needing to learn SQL.
• Collaboration and Governance: Multiple users can work on the same datasets, with robust data governance features to keep everything secure and compliant.
• Scalability: Built to grow with your data, Sigma handles increasing data volumes and complexity without a hitch.

Limitations:

• Premium Pricing: Sigma is a premium product, which might be pricey for smaller businesses or those on a budget.
• Customization Limits: May not offer as much customization for specialized reports or niche visualizations as other tools.

Pricing:

• Sigma operates on a subscription model, with pricing scaling based on users and features. It’s generally on the higher end, suited for mid-sized to large enterprises.

Self-Service Analytics Best Practice: A Case Study

In the classic 1989 movie Field of Dreams, Kevin Costner’s character, Ray, hears a voice whispering, "If you build it, they will come."

So despite taunts of lunacy, he builds a baseball diamond in his cornfield, and sure enough, the ghosts of legendary players show up to play ball. The phrase became iconic, suggesting that if you create something valuable, people will naturally gravitate toward it.

But when it comes to self-service analytics, it’s not that simple.

You can’t just buy a self-service BI tool and expect everyone to start using it right away. You need training, education, and a cultural shift to get people to embrace data thinking.

The data teams at Spenmo (Fintech, $500M valuation) and ZOE (Nutritional Science, $250M valuation) show how focusing on people and processes can lead to successful self-service BI adoption.

Here’s how they did it.

1. Understanding Different Data Needs (People)

Success starts with empathy.

Spenmo’s data team knew that different departments needed different things. The strategy team needed ad-hoc analysis, which required more self-service enablement, while the operations team required consistent dashboards. Instead of a one-size-fits-all approach, they tailored their solutions:

• Operations: Created evergreen dashboards that required minimal updates, allowing the team to monitor key metrics without constant data team involvement.
• Strategy: Educated the team on how to ask the right questions and use Holistics to find their own answers, empowering them to conduct independent, data-driven analysis.

Spenmo’s data team puts their users first and constantly asks themselves: “how can I help them succeed?”.

The answer to this question is the variety of thoughtfully designed datasets that can cater to different analytical needs and maximize the number of use cases to be served by each data table.

2. Education: Implementing Data Clinics (People)

Spenmo introduced data clinics—open sessions where users could learn, ask questions, and get guidance on using BI tools. These clinics helped build a culture of self-sufficiency while protecting the data team’s time from being consumed by “quick questions.”

The success of data clinics is well-documented.

For example, in a presentation shared with the dbt Slack community, Jacob from Montreal Analytics reported that the number of self-serve business users on their BI tool increased tenfold after implementing data clinics. This kind of structured support helps bridge the gap between data teams and business users, moving the organization closer to full-fledged self-service capabilities.

We wrote a detailed guide on how to set up data clinic and promote self-service culture here. Check it out!

3. Setting Clear Expectations (Process)

Processes matter. At Spenmo, the data team was upfront about their limitations.

When users requested data, they were told it could take up to eight weeks. But they didn’t leave them hanging—instead, they offered to train users to find answers themselves using their self-service BI tool, often with just a one-hour session.

This approach not only encouraged self-sufficiency but also made users feel supported and confident in their data skills.

4. Creating Comprehensive Documentation (Process)

Thorough documentation was another cornerstone of Spenmo’s success. The data team developed detailed documentation for every data table in Holistics.

This documentation ensured that business users were fully aware of:

• Which data was available for exploration?
• Which datasets were most appropriate for their specific analyses?
• What data to expect in each column?
• The source tables were used to populate the data, which was crucial for validation purposes.

By making this information readily available, the data team enabled business users to navigate the data landscape more confidently and independently. Comprehensive documentation is a cornerstone of effective self-service, reducing the need for constant guidance and helping users make the most of the BI tools at their disposal.

Conclusion

At the end of the day, there are a lot of self-service BI tools in the market and each offers various features and capabilities.

What is important is that you do your homework by reading different articles, forums, user reviews, etc before choosing a BI tool for your organization. Most importantly, If you can get your hands on a free trial version, go for it and try out some of your business use cases with the tool to evaluate if it can fulfill the requirements. This is crucial to help you make an informed decision.