Self-Service Analytics: What, Why, and How?

Self-service analytics, or self-service business intelligence, has long been touted as the answer to your data analytics needs, and while to an extent this may be the case, understanding what it is and how to best leverage it is crucial in it being a help and not a hindrance.

Read on, to understand what self-service BI is, why it could be the right solution for your organization, and how to successfully implement it.

What Is Self-Service Analytics?

We can best define self-service analytics as; a type of business intelligence that allows end-users without a technical background, or in-depth knowledge of data analytics, to access data and create or customize reports and analyses on their own. You can also find Gartner's definition here.

In real terms, this means that employees (or customers) can access business data and gain insights — without in-depth technical knowledge (of SQL for example) or the help of someone in an IT or BI team. Usually, this comes in the form of a self-service reporting tool that allows end users across (or beyond) an organization to analyze and present data without (or with limited) assistance.

Pros and Cons of Self-Service Analytics

There are several benefits of self-service analytics, as well as pitfalls to be aware of. The benefits include:

Data-fueled decision making

One of the main benefits of BI and analytics in general is its ability to enable users across the organization (be it within marketing, finance, sales, etc.) to make more informed decisions based on up-to-date data. With self-service analytics, this is amplified due to the increased ease of access and ease of use that it brings.

Efficiency or Time to Insight

By lowering the dependency on BI and IT teams, efficiency is increased across the board. For the BI and IT teams, this means less time spent manually creating reports and dealing with manual user-associated tasks, and instead focusing on more strategic tasks. For end-users, access to data is faster, meaning time to insight is greatly reduced. This has knock-on effects in terms of quicker decision-making and a more agile, growth oriented organization.

Data Accuracy and Governance

Self-service analytics can enhance data accuracy by allowing users to access real-time, up-to-date information directly from trusted sources. With proper governance frameworks in place, data access and usage can be controlled, ensuring consistency and adherence to standards across the organization. Additionally, self-service platforms often include automated validation checks to prevent errors, promoting higher data integrity and reducing the risks associated with manual processes.

Improved User Experience

Self-service analytics tools empower users to explore data independently, offering intuitive interfaces and customizable dashboards that make complex analysis more accessible. This enables users to quickly gain insights without waiting for IT or data specialists. By providing user-friendly features and interactive capabilities, these tools enhance decision-making speed and foster a more data-driven culture within the organization.

Data Misinterpretation

Users without a strong background in data analysis may struggle to correctly interpret complex datasets, leading to errors. This can result in wrong business decisions based on faulty insights. Misinterpretation is especially common when users lack the context or understanding of how the data was collected. Inaccurate conclusions can undermine business strategies and impact overall performance.

Data Governance

While data governance is a potential upside, when self-service analytics tools are used without a clear governance structure, it can lead to inconsistent data practices across different teams. Without proper oversight, there may be discrepancies in data quality, formats, or interpretations. This lack of control can increase the risk of errors or inconsistencies in reporting. In the absence of defined policies, the integrity and reliability of the data may suffer.

Over-Reliance on Tools

Users may become overly dependent on automated tools for analysis, assuming the results are always accurate and reliable. This could result in the overlooking of insights that require deeper qualitative analysis or critical thinking. Relying solely on these tools can also prevent individuals from recognizing the limits of the software. It's crucial to balance technology with human expertise to ensure well-rounded decision-making.

Self-Service Best Practices

Like any BI or analytics feature, there are some best-practices to consider when rolling out a self-service analytics tool.

• Clarity — Ensure that business questions are clearly defined before diving into the data. This helps prevent analysis paralysis and ensures that the insights generated are actionable.
• Data Quality — Always validate the quality of your data before using it. Inaccurate or incomplete data can lead to misleading insights and poor decision-making.
• Collaboration — Engage with colleagues and subject matter experts to confirm your findings. Collaboration brings diverse perspectives, ensuring the analysis is comprehensive and aligned with business needs.
• Training — Provide proper training for users on the tools and data available. Well-informed users will be able to extract relevant insights while minimizing the risk of misinterpretation.
• Security — Maintain strict data security and access controls. Sensitive data should be protected, and users should have access only to the information necessary for their roles.

What to look for in a self-service analytics platform?

When shopping for a self-service analytics tool, it's important to think about the kinds of features and functionality you require.

There is some obvious minimum set of analytics capabilities for a data platform that will help you achieve an agile self-service environment (besides table-stakes features such as data visualization, pivot tables, or dashboards):

Multitenancy and Scalability

Look for a platform that supports multitenancy, allowing multiple departments or organizations to use the same system while maintaining data isolation. Scalability is also crucial to ensure the platform can handle increasing data volumes and user loads as your organization grows. This ensures that the platform remains responsive and efficient, even with a large number of concurrent users or complex data workloads. A scalable solution can grow alongside your business without requiring frequent upgrades or replacements.

AI Assistance

AI-powered features can greatly enhance self-service analytics by automating insights and suggesting trends or anomalies based on data patterns. These capabilities help users quickly identify valuable insights without needing advanced analytical skills. AI assistance can also streamline tasks like data cleaning, model creation, and predictive analysis, improving overall efficiency. This reduces the learning curve for users and ensures more accurate and relevant insights are derived.

Metrics (Semantic) Layer and Data Governance

A semantic layer simplifies complex data models by providing users with business-friendly definitions of metrics and dimensions. This ensures consistent understanding across all users and departments. In addition, strong data governance features are essential to control data access, monitor usage, and ensure compliance with regulations. Proper governance ensures that data remains accurate, consistent, and secure, preventing misuse and enhancing reliability.

Analytics as Code

A platform that offers "analytics as code" allows for advanced customization and automation by using code-based approaches to build complex analytics workflows. This is particularly useful for teams with data engineers or developers who want more control over data processing and analysis. Analytics as code enables a high level of flexibility, allowing users to create tailored solutions for their specific business needs. It also makes it easier to version control, collaborate, and maintain analytics pipelines over time.

Embedding and Distribution

The ability to embed analytics into applications, websites, or other business tools is a key feature to look for. This makes it easier to share insights and reports across different teams or external partners without requiring them to access the platform directly. Embedding allows users to seamlessly integrate data visualizations, dashboards, and reports into their workflows. Distribution features ensure that reports can be automatically sent to relevant stakeholders or scheduled for regular updates, increasing overall adoption and use.

Self-Service Data Analytics Tools to Consider

When it comes to choosing a specific self-service analytics tool, there a number to choose from, each with its own strengths and weaknesses. Here’s a short list of analytics platforms to consider.

GoodData

GoodData is an end-to-end cloud-based analytics platform designed for businesses to provide self-service reporting and insights through customizable dashboards. It emphasizes data governance and scalability for enterprise-level applications.

Pros: Strong data governance features, highly scalable, and customizable reporting options.

Cons: May require technical expertise for advanced functionality.

Tableau

Tableau is a powerful data visualization tool that enables users to create interactive and shareable dashboards. It supports self-service analytics with a simple drag-and-drop interface for non-technical users.

Pros: User-friendly, highly customizable visualizations, extensive community support.

Cons: Can be expensive for larger organizations, steep learning curve for advanced features, performance issues with large datasets.

See how GoodData and Tableau stack up in our head-to-head comparison paper.

Qlik

Qlik Sense is a flexible, self-service BI platform that allows users to explore and visualize data. It offers associative data models, enabling users to discover insights through data exploration.

Pros: Robust data discovery features, in-memory processing for fast analysis, highly interactive visualizations.

Cons: Steep learning curve, pricing may not be cost-effective for small businesses, complex setup for advanced features.

See how GoodData and Qlik stack up in our head-to-head comparison paper.

Sisense

Description: Sisense is a BI platform that allows for both self-service and enterprise-level reporting. It features a simple drag-and-drop interface and allows users to embed dashboards into applications.

Pros: Easy-to-use, scalable for large datasets, good at embedding analytics into applications.

Cons: Limited customization in some visualization options, high cost for small businesses, can be challenging to set up for non-technical users.

See how GoodData and Sisense stack up in our head-to-head comparison paper.

Luzmo

Luzmo is an analytics and business intelligence tool designed for small to mid-sized companies. It offers self-service dashboards and reporting capabilities with an easy-to-use interface aimed at empowering business users.

Pros: Simple and intuitive interface, ideal for small businesses, affordable pricing model.

Cons: Lacks some advanced features for larger enterprises, limited integration options, may not scale well for large datasets.

Sigma

Sigma is a cloud-based BI platform that allows users to analyze data directly in cloud data warehouses (such as Snowflake). It offers a spreadsheet-like interface, making it intuitive for non-technical users to create reports and dashboards.

Pros: Easy-to-use, integrates with cloud data warehouses, strong collaborative features for teams.

Cons: Can be slow with large datasets, limited advanced analytics features compared to some competitors, pricing can be steep for smaller organizations.

Compare some of the the best self-service BI tools

Read our comparison guide to find more about different tools and features.

Learn more

Self-Service Analytics With GoodData

A solid self-service analytics solution requires a combination of a strong self-service analytics tool alongside a robust set of data analytics governance practices as well as several other factors as mentioned above. And while no tool alone will get you there, GoodData will certainly provide a great foundation for a successful self-service analytics and BI experience.

To learn more, request a demo and let our experts take you on a guided tour of the GoodData platform. They’ll help you discover its rich feature set and ease of implementation as well as answer your questions.