Self-service Analytics Explained: Tools and Best Practices in 2023

 

Picture a world where data isn't locked behind IT barriers but is at the fingertips of every professional, ready to be explored. That's the promise of self-service analytics. Join us as we delve into its transformative power, backed by insights from industry titan Gartner, and uncover how AI-driven self-service analytics has propelled it to be the standout trend of 2023.

 

What is self-service analytics?

 

According to Gartner, Self-Service Analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.

 

Self-service analytics often features simple-to-use BI tools with basic analytic capabilities and an underlying data model that has been simplified or scaled down for easy understanding and straightforward data access.

 

Benefits of self-service analytics

 

Here are some of the benefits of self-service analytics:

 

Prevents existing data silos

Self-service analytics makes data consumable for all, which prevents existing data silos.

 

Gain Accurate insights by enabling citizen data scientists

Self-service analytics platforms provide AI-powered insights without the need to hire data scientists, benefiting regular business users. In this situation, we refer to business users who do analytics as citizen data scientists. They create models using advanced analytics, but their main job is not in statistics and analytics.

 

Reduces IT overhead

Self-service analytics tools let business users handle simpler tasks like exploring data, creating visualizations, and generating reports. This reduces IT workload and allows IT personnel to concentrate on more valuable tasks.

 

Increases efficiency

Self-service analytics enables users to access data, find answers, and generate reports without relying on others, boosting efficiency.

 

What's changed about self-service analytics in 2023?

 

Self-service is not only relevant to BI tools. It is a concept that can be applied to various aspects of data analytics and business intelligence.

 

Self-service is a concept that extends beyond BI tools. This includes self-service BI, self-service data analytics, and self-service big data. These tools assist business users in accessing, analyzing, and understanding data. They aim to reduce the need for IT assistance.

 

Best self-service analytics platform and tools in 2023

 

Here are the best self-service analytics platforms based on our research on popular software review websites:

 

Kyligence Zen

 

 

Kyligence Zen is an AI-powered metrics platform. It has a user-friendly platform for easy communication with data and metrics, even for non-technical users.

 

The Kyligence Copilot is an AI assistant that helps with data. It lets users talk to their business numbers, change how they use data, and stay on top of problems.

 

Kyligence Copilot has auto root cause analysis features and easy metric search. It can also be embedded into various applications. This provides a versatile and secure AI experience.

 

Pros:

• AI-powered Chat-based copilot to generate data insights
• Centralized management of metrics.
• Smart Cache feature for quick query responses.

 

Cons:

• It might require training for full utilization.
• Integration with some third-party tools might be challenging.

 

Alteryx

Alteryx is a self-service data analytics software company specializing in data preparation and blending. It allows users to organize, clean, and analyze data in a repeatable workflow. It's especially useful for business analysts connecting to and cleansing data from various sources. The platform can run predictive, statistical, and spatial analytic jobs within a single interface.

 

Pros:

• Comprehensive data preparation and blending capabilities.
• Single interface for various analytic jobs.

 

Cons:

• It might have a steeper learning curve for beginners.
• Licensing can be expensive for small businesses.

 

Domo

 

Domo presents a cloud-centric, mobile-optimized BI platform. It's designed to augment the capabilities of existing data tools, democratizing data access across an organization and facilitating timely decision-making.

 

Pros:

• Cloud-centric, mobile-optimized BI platform.
• Augments capabilities of existing data tools.
• Democratizes data access across an organization.

 

Cons:

• Can be overwhelming for new users.
• Some features might require additional licensing.

 

IBM Cognos Analytics

 

IBM offers a range of BI and analytic capabilities. The Cognos Analytics platform is an integrated self-service solution that allows users to access data to create dashboards and reports. It also offers machine learning-enabled user experiences.

 

Pros:

• Integrated self-service capabilities.
• Strong machine learning and AI integrations.

 

Cons:

• Might be complex for smaller businesses.
• Licensing and pricing can be on the higher side.

 

Infor Birst

 

Infor Birst is a cloud-based analytics solution that connects organizations using a network of virtualized BI instances. It offers an adaptive user experience and a completely virtualized data ecosystem.

 

Pros:

• Cloud-based solution.
• Virtualized BI instances for flexibility.

 

Cons:

• Requires training for full utilization.
• Integration with some third-party tools might be challenging.

 

Tableau

 

Tableau provides an extensive visual Business Intelligence (BI) and analytics platform. Recognized as a leading player in the industry, it offers versions such as Tableau Desktop, Server, and Online. It facilitates connections to numerous data sources and promotes collaborative data sharing.

 

Pros:

• Extensive visual BI and analytics platform.
• Facilitates connections to numerous data sources.
• Promotes collaborative data sharing.

 

Cons:

• Licensing can be expensive for some businesses.
• Might have a learning curve for beginners.

 

Power BI

 

Power BI, a Microsoft product, operates predominantly in the cloud. It empowers users to engage with data without extensive technical expertise. A distinguishing feature is its integrated approach to data preparation and visualization. Additionally, it seamlessly integrates with tools like Excel.

 

Pros:

• Operates predominantly in the cloud.
• Integrated approach to data preparation and visualization.
• Seamless integration with tools like Excel.

 

Cons:

• Limited customization options.
• Might not be suitable for very large datasets.

 

Best practices for implementing self-service analytics

 

The best practices for implementing self-service analytics recommended by Gartner include the following:

 

Making data easily accessible

 

Self-service analytics requires that data is easily accessible to all users. This means that data should be organized, cleaned, and stored in a way that is easy to access and understand.

 

Ensuring a user-friendly interface

 

A user-friendly interface is essential for self-service analytics. The interface should be easy to navigate, and intuitive, and provide users with the ability to customize their experience.

 

Encouraging a quick test-and-learn culture

 

A quick test-and-learn culture is important for self-service analytics. This means that users should be encouraged to experiment with data and explore different insights to find the best solutions.

 

Providing data teams with advanced analytics tools

 

Data teams should be provided with advanced analytics tools to help them analyze data more effectively. These tools should be easy to use and provide users with the ability to create custom reports and visualizations.

 

Establishing a governance committee

 

A governance committee should be established to oversee the self-service analytics initiative. This committee should be responsible for setting policies, standards, and procedures for data access, security, and quality.

 

Choosing the right vendor and solution

 

Choosing the right vendor and solution is critical for the success of self-service analytics. The vendor should provide a solution that is scalable, flexible, and customizable to meet the specific needs of the organization.

 

These best practices aim to democratize access to data across an organization, empowering the average person to participate and have a greater impact on the analytics process so they can improve business outcomes and use data regularly for informed decision-making.

 

Transform your business with self-service analytics

 

The journey through the world of self-service analytics has been nothing short of transformative, with AI steering the ship into uncharted territories. As we conclude, one thing is clear: 2023 isn't just another year; it's the dawn of a new data revolution. Embrace it, and let your business soar to new analytical heights.

 

Kyligence Zen is an AI-powered metrics platform that puts the power of 10 data analytics to your business users. Interested in getting started with Kyligence Zen? Start your free trial of Kyligence Zen today!

 

Frequently asked questions for self-service analytics

 

What does Gartner have to do with self-service analytics?

 

Renowned advisory firm Gartner has spotlighted the transformative potential of self-service analytics. Emphasizing its role in democratizing data access, Gartner underscores how these tools empower professionals across sectors to derive insights independently, minimizing IT dependencies. Their research further reveals that self-service analytics significantly enhances job efficiency, with many finance leaders attributing improved team performance to these tools. In essence, Gartner's findings validate the growing importance of self-service analytics in today's data-driven business landscape.

 

Ready to boost data-driven decisions? Register now to try Kyligence Zen for free.