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Imagine a world where everyone using your product can access data without a data science or engineering background. This is self service analytics (also known as self service business intelligence), and it's a revolution in data democratization. However, this may sound like science fiction if you don't know the underlying mechanisms.
This is why today, we explain what self-service analytics is, how it works, and why you should care, as well as give you some of the best choices for self-service analytics platforms.
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What is self service analytics?
Self-service analytics is a data analytics approach that allows business users without technical expertise to access, analyze, and visualize data on their own without relying on IT or data teams.
It provides intuitive tools like drag-and-drop dashboards, pre-built reports, and natural language queries, allowing users to extract insights quickly.
In short, instead of using complex BI tools, users can easily work on a dataset and do their own data analysis.
These are some of the most common traits of self service analytics tools:
✅ User-friendly interfaces – Users can generate reports with simple clicks without any knowledge of code or data analytics.
✅ Real-time data access – Enables instant insights to data as it happens, without waiting for IT-generated reports.
✅ Customizable dashboards – Users can create, modify, and personalize visualizations, e.g.,, changing chart types to more accurately represent underlying data.
✅ AI-driven insights – Some platforms include AI recommendations and predictive analytics.
✅ Integration with multiple data sources – Connects with databases, spreadsheets, CRMs and cloud apps.
Top benefits of self service analytics
Self service analytics gives your team or end users access to relevant data to make more informed decisions in their day-to-day work. These are some practical benefits of using self service analytics tools.
Faster decision making
Self service analytics reduces the time it takes to go from raw data to meaningful insights. The end users can create custom reports, break down complex data into meaningful bits of information, analyze data and trends on their own, and develop data-driven insights.
The biggest differentiator is that the end user no longer has to rely on a team of data scientists or data analysts to do this work for them. Instead, everyone can be a data scientist. Speaking of which...
Reduced dependency on IT teams
Traditionally, data teams would have to connect the data sources, do data modeling and data transformation, clean up the data, and prepare and then create reports tailored to the person who requested them.
In larger businesses, this creates data silos as data teams have more important work going on most of the time. As a result, a marketing manager can wait for days to get business insights from a report. By that time, the data can be obsolete. Self service analytics solves this problem.
On the other hand, if end users want to access data from your app, they can do so without any technical expertise. With basic tech knowledge, they can do an ad hoc analysis and get insights to improve their business outcomes.
Improved data access
You won't have to invest in a data literacy program or hire an extra dozen data scientists. Self serve analytics allows you to give one data set to different teams or individuals so they can create the kind of dashboards and reports they need to crush your business objectives.
Everyone has access to the same data, and they can generate insights based on their needs. For example, a sales professional will use website analytics data compared to a marketing manager.
Building a data-driven culture
Instead of winging it, everyone in your team is encouraged to make data-based decisions. For example, a customer service team lead can take a deep dive in the most used customer service channels and resolution rates to find the most important areas of focus.
Cost savings
Since your team can create reports and dashboards without technical skills, they won't have to bother your data teams whenever they need insights. This frees up time and increases operational efficiency for everyone involved.
Personalization and customization
You can start with one data set and generate reports for a variety of use cases. For example, you just got the most recent sales data for the quarter. And if you embed a dashboard into your product, you can give insights to your end users.
Marketing can use it to adjust positioning and messaging in the next quarter. The data could show that the ideal customer profile is looking into different feature sets.
Sales teams can use the same data set to create better forecasts going into the next quarter.
The product team can analyze the features most requested by the teams who opted for a competitor.
All of these groups can create their custom reports based on singular data sets and make data-driven decisions for their own use cases. But also, there is...
Improved collaboration
With all the teams and individuals creating their own reports, sharing is a matter of a few clicks. And thanks to the same underlying data set and the user-friendlyuser friendly interface of most self service analytics tools, they can more easily understand the data in the report.
The challenges of self service analytics
In theory, the idea of non-technical users analyzing data sounds great. However, there are a few hurdles that might get in your way.
Data security and governance
With all the users in your team having unrestricted access to data, you can run into compliance risks and security vulnerabilities. Without proper governance, data leaks can happen and people outside of your organization can get access to sensitive information.
Solution: Implement role-based access controls (RBAC) and data governance frameworks to regulate data access and maintain compliance.
Data quality and consistency
Users can pull data from different sources, which can lead to low data quality and inconsistency. More often than not, you may have duplicate entries, missing data fields, or simply outdated data that skews the final result.
Solution: Use automated data cleansing and validation processes to ensure data accuracy and standardization before analysis.
Lack of data literacy
Not all the employees in your team will have the skills to create dashboards, choose the right visualizations, and interpret the results. Even worse, they could do the preparation well and misinterpret the results, which can have huge implications later on.
Solution: Offer data literacy training and intuitive analytics tools that simplify report generation for non-technical users.
Overloaded IT and data teams
In an ideal world, IT teams prepare the data while the rest of the team handles data analysis and interpretation. In reality, it's not often the case as IT needs to provide reliable data, maintain infrastructure, security and access control.
Solution: Implement self-service analytics with automation and create a knowledge base for users to solve common issues independently.
Shadow analytics and data silos
Different teams in your organization can create different versions of a report, leading to inconsistencies. With no centralized oversight, different teams can have multiple interpretations of the same data.
Solution: Enforce a centralized data platform where all teams use the same source of truth for analytics.
Performance and scalability limitations
Certain self service analytics tools can have issues handling large volumes of data. This may not be a huge deal for internal reporting, but in user-facing analytics, it can lead to data loading slowly, which causes poor UX and user frustrations. Poorly optimized queries can halt user adoption.
Solution: Invest in scalable cloud-based analytics solutions and optimize data queries for performance efficiency.
Change management and user adoption
Employees and end-users accustomed to traditional reporting may push back against self service analytics. With a lack of proper training, your team can underutilize the self-service analytics platform.
Solution: Develop a structured onboarding process, provide ongoing support, and highlight the business benefits of self-service analytics.
The most important features to look for in a self service analytics platform
The self service data analytics industry is growing eachgrowing with each year. Besides traditional big players like Tableau, Looker, Power BI and others, new business intelligence tools are entering the market. When comparing these self service BI tools, watch out for these features.
- Easy data connectivity: Connects to multiple data sources like databases, data warehouses, CRMs, cloud storage, and APIs, enabling seamless data access.
- Intuitive user interface (UI): Drag-and-drop functionality and no-code/low-code tools make it easy for non-technical users to create reports, run what if scenarios and more.
- Data preparation & cleansing: Automates data cleaning, removes duplicates, corrects errors, and transforms datasets for accuracy.
- Interactive data visualization: Provides charts, graphs, heatmaps, and dashboards for better data exploration and trend analysis.
- Natural language query (NLQ) support: Lets users ask questions in plain English (e.g., "Show revenue growth for Q1 2024") to generate insights.
- AI & predictive analytics: Uses machine learning to detect trends, provide recommendations, and forecast outcomes based on historical data.
- Self-service reporting & dashboard customization: Enables users to create, modify, and share reports without IT involvement, with personalized views.
- Collaboration & sharing: Supports role-based permissions, team collaboration, and multiple report export formats like PDF and Excel.
- Embedded analytics capabilities: Allows analytics to be integrated directly into business applications with branding customization.
- Strong security & governance: Provides role-based access control (RBAC), encryption, and compliance with GDPR, SOC 2, and HIPAA.
- Scalability & performance optimization: Cloud-based or hybrid deployment ensures fast query performance and the ability to handle large datasets.
Top self service analytics tools for business users
Choosing the right self service analytics platform will depend on a variety of factors. Your budget, team size, the number of end users, whether you already use a business intelligence tool internally and others. These are some of the best embedded analytics tools that you can choose to get actionable insights from your data.
Embeddable
Embeddable is a developer-first tool designed to integrate lightning-fast, customer-facing dashboards seamlessly into applications. It uses a headless architecture, offers complete control over the user experience, and provides a performant data service to ensure rapid load times.
Embeddable supports unlimited chart customization, allowing developers to tailor visualizations to specific needs. Its flat-rate pricing model facilitates scalability without escalating costs, making it suitable for both startups and large enterprises.
Best for: Organizations seeking highly customizable, native-feeling embedded analytics with exceptional performance and scalability.
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Microsoft Power BI
Power BI Embedded is a Microsoft business intelligence product that enables adding analytics to internal or external applications. It supports seamless integration with various data sources, including SQL and NoSQL databases.
The platform allows for preloading data in embedded dashboards to enhance user experience, though this may affect data freshness. Embedding is facilitated through iframes, simplifying the integration process but potentially impacting the native feel and performance.
The pricing model is usage- and user-based, starting at $735.91 per month for an A1 node with 3GB of RAM capacity and one virtual core.
Best for: Organizations already utilizing Microsoft products seeking to integrate analytics into their applications, focusing on security and data source compatibility.
Tableau
Tableau for embedded analytics is a BI platform built primarily for internal use, offering embedding capabilities through iframes or web components. However, the web component option lacks row-level security, limiting its applicability for sensitive data.
Customization options are restricted, primarily to colors and fonts, making it challenging to achieve a fully native look and feel within applications. Users have reported performance issues, particularly with large datasets, and a steep learning curve for non-technical users.
Best for: Internal reporting where extensive customization and native integration are not critical requirements.
Looker
Looker Embedded is a business intelligence platform that offers embedded analytics capabilities, allowing organizations to integrate data insights directly into their applications. It provides a semantic modeling layer, enabling consistent data definitions across the organization.
Looker supports integration with various data sources and offers customization APIs. However, embedding is typically achieved through iframes, which may not provide a seamless native user experience. Customization of visualizations is also limited compared to more developer-centric tools such as Embeddable.
Best for: Organizations seeking a robust BI platform with embedding capabilities, where data consistency and integration with existing data infrastructure are prioritized over extensive customization.
Sisense
Sisense is a mature embedded analytics tool offering a wide range of features suitable for complex data analysis needs. It utilizes iframe embedding, which may limit the native feel within applications and restrict customization options.
Sisense provides both self-hosted and cloud-hosted deployment options, catering to diverse security and compliance requirements. The pricing model is user- and usage-based, which could lead to higher costs as the number of users or data volume increases.
Best for: Organizations requiring a comprehensive embedded analytics solution with a rich feature set, where customization and native integration are less critical, and budget allows for user-based pricing.
Conclusion
When done right, self-serviceself service analytics can improve data accuracy in your team, streamline the way you work, improve data driven decision making, and ultimately, save time and money. For businesses offering end-user analytics, these tools can increase product adoption, help you upsell your existing customers, branch out into new markets and unlock expansion revenue.
All it takes is a flexible platform that both your developers and users will love. In other words, all it takes is Embeddable. Our flexible, developer-friendly embedded analytics platform let you create stunning dashboards and reports that your dev team can easily embed in your website or product.
Get access to Embeddable today!