Top 05 Self-Service Reporting Tools: An In-depth Review

An Introduction to Self-Service Business Intelligence

With more companies requiring their employees to participate in creating reports and generating insights as well as the need for the insights to be produced in a more time-efficient manner, self-service business intelligence (BI) tools are becoming an integral part of every company’s data strategy. Now you might ask, what is self-service reporting and how does it help companies in report generation.

Self-service BI also known as self-service analytics is a tool that enables non-technical employees of an organization to be involved in the data analysis process without having to seek help from IT specialists or dedicated data analysts. This is because in the past before self-service BI tools came into play, only users with SQL knowledge (database query language) could generate reports but now anyone can generate insights and get the data they need in no time using simplified interfaces without writing a single line of SQL code.

So far we’ve mentioned the word self-service a lot for BI tools and I am sure you are thinking if there are BI tools that are not self-service and the answer is yes. The opposite of self-service BI is traditional BI. Users that work with traditional BI Tools are often IT professionals that have extensive SQL knowledge.

This article aims to highlight the differences between self-service and traditional BI tools as well as provide an in-depth analysis of the top 5 self-service BI tools in the market so you can make an informed decision about which tool is most suitable for your business.

But what exactly is Self-service analytics? - We wrote about it in detail here: Self Service Is A Business State.

Traditional BI VS Self-service BI: Know the Differences

In a broad stroke, if you want to determine whether a BI tool is self-service or not, you need to evaluate the following:

• The effort required to integrate with different data sources:
  Self-service BI tools normally have built-in connectors for major databases. This enables users with sufficient credentials to easily connect the database to the BI platform without  any assistance from the IT department.
  
• The amount SQL scripting required to analyse the data:
  One of the value proposition of self-service analytics tool is to enable users to focus on getting value and insights out of data instead of spending hours to come up with a right SQL script
• If everyone in your organization even those with minimal technical knowledge get the data they need without relying on technical team.

Self-service reporting is supposed to make it possible for everyone in the organization to analyse the data they need normally using a drag and drop feature.

For a more in-depth comparison, let’s compare traditional and self-service BI by looking at the 5 key areas: Infra set-up, agility, data structure, reporting and data governance.

Table 1:Traditional BI VS Self-service BI

Top Common Features of Self-Service BI Tools

There are certain features that are part and parcel of every self-service BI tool regardless of brand. In this section we are going to outline the important ones:

• No SQL required to generate reports:
  One of the most important features of a self-service BI tool is to enable non-technical users (Those who can’t write SQL scripts) to generate reports in a short amount of time. Once the initial integration with database and modelling configuration is done by your developers / engineers, then any stakeholders within the organization can pull the insights they need by combining right dimensions and metrics.
  
• Ability to connect to multiple data sources:
  Self-service analytics tools usually can connect to various databases including MySQL, Google BigQuery. Amazon Redshift, PostgreSQL, etc. This is a crucial feature that allows data from different databases to get unified in one place as sometimes different departments within an organization use different databases to store data
  
• Agility in analytics:
  When referring to agility in self-service BI tools, There are 3 main principles that stand out:

1. Providing the right users the right set of reporting capabilities
2. Creating a collaborative environment where different stakeholders can work together
3. Having a strong data governance to monitor how this collaboration leads to incremental uplifts in your analytics.
   

Now that we’ve covered high-level details about self-service BI tools, let’s dive a bit deeper into individual self-service BI brands that’ve been trending in the market and know them better. We’ll start with Holistics first followed by Metabase, Looker, Tableau and Power BI.

Top 5 Self-Service BI & Reporting Tools

1. Holistics

Holistics is an analytics-focused self-service BI tool that enables non-technical users to explore data and generate actionable insights without having to use SQL.

The Pros

• Budget-friendly - built for Growth: One of the Holistics’s pros that truly stands out is its pricing structure. Unlike many self-service BI tools that have limited pricing options and can cost you tens of thousands of dollars, Holistics provides a variety of pricing options as low as $100 USD / month. The intention is to make it as hassle-free as possible for data teams to try out, validate and grow with the tool.
• Code-based data modeling layer: Data teams are able to centralize business logic in the Holistics modeling layer. Data analysts can also define data models once and reference any piece of it anywhere else to avoid writing the same SQL queries over and over again.
• User-friendly drag-n-drop report builder: Business users can perform self-service analysis and data exploration without knowing SQL or relying on data teams.
• Git version control available: Data teams can write analytics as code, commit it to Git and track every change, perform branching, and code reviews to ensure accuracy, and consistency and maintain codebase quality easier.
• Seamless dbt integration: Perform data modeling and transformation at dbt layer, and push those definitions to Holistics BI layer.
  

The Cons

• Limited Data Source Integration: Holistics supports integration with major SQL based data sources, however, compared to other major players in the market such as Tableau or Power BI which supports 50+ data sources, the number of supported data sources supported by Holistics falls short.
  
• 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.

2. Metabase

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

The Pros

• Open-Source: Metabase has an open-source version that is free and hosted on your own company’s server. This is great for companies with limited budgets that want to have a simple and decent BI tool for day to day reporting.
• 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.
• Lack of Automated Data Mapping: Unlike its competitors that automatically do the data mapping between database tables and business logic once the datasource is integrated, You need to do your data mapping manually in Metabase and this leads to less flexibility and lack of customization.
  

Bottom Line

Metabase is an open source BI tool that is most suitable for companies that are looking for a free BI tool to answer their day-to-day analytics questions. However, to answer sophisticated questions, writing SQL queries is a must.

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 visualizes your data.

The Pros

• 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.
  

The Cons

• Eye-Watering Price Point: Looker does not disclose its pricing publicly and companies must request for quotation to receive pricing details. However, a Looker subscription can cost you somewhere between USD $3k to USD $5k for 10 users per month.

(For more details about Looker pricing, check out: Everything About Look Pricing)

• Upfront Work Required to Prepare the Data: You can’t just simply connect your database to Looker and start your visualization. You need to first define a semantic data model that properly defines your logic and metrics that accommodates the requirements of the end users. If the data model is not defined correctly, the business analysts who are responsible to generate insights can’t create the required reports. Learning how to use this semantic data model using LookML requires time and effort and first-time users will spend a fair amount of time studying documents and courses as well as doing trials and errors.
  

Bottom Line

Looker is a great self-service BI tool for enterprises with high budgets who are already using Google ecosystems such as Google Analytics and Google Cloud Platform (GCP) as Looker is easily integrated with these platforms for advanced use cases such as data import or predictions.

But for smaller companies with growing data needs - it might not be the best choice due to high cost barrier.

4. Tableau

Tableau is a BI and analytics platform that empowers users to explore data and discover insights.

The Pros

• Deployment in Multiple Environments: Tableau can be deployed on Cloud, Linux, Mac, Windows, and on-premise. This is a significant advantage as it gives flexibility to companies to deploy Tableau in an environment that is in line with the organization’s tech ecosystem.
• Supporting Integration with Various Data Sources: Tableau supports connection to 60 native data sources including JSON files, BigQuery, PostgreSQL, Google Analytics, etc. This feature is important as it does not limit organizations to a certain type of data sources.
• Tableau Public: Tableau Public is the free version of Tableau where you can explore data visualizations and share it publicly with other users on the platform. The only limitation of Tableau Public is that it can only be connected to Excel or text files.
• Rich Visualization: Tableau users exalt its versatility of visualization - which bring forth great flexibility to design and present data.
  

The Cons

• Difficult to Use 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.
  
• Difficult to Embed to 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.
  

Bottom Line

Tableau is a powerful BI tool that can be deployed in various environments. It is best suited for organizations that have the technical resources (Analysts and developers) to properly set up the platform, model the data and generate the required insights needed for every department.

Related articles:  Best Embedded Analytics Software for Modern Data Stack

05. Lightdash

Lightdash is a relatively new open-source self-service BI solution that can connect to a user's dbt project and allow them to add metrics directly in the data transformation layer, then create and share insights with the whole team.

The Pros

• Open-source: Lightdash can be self-hosted or fully hosted by Lightdash. The fully hosted option is not free. An unlimited number of users can be added to the project, with an unlimited number of charts and dashboards
• dbt integration: Integration with GitHub, Gitlab & dbt, with a strong and supportive community of users.
• Intuitive UI: Lightdash UI is fairly intuitive and approachable for non-technical users - making it easier for them to self-serve.

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.
  

Bottom Line

Lightdash is certainly promising, but it still has a long way to become a full-fledged self-service BI that can meet the needs of both business users and data teams.

Learn more:  How Lightdash Could Be Better

Conclusion

At the end of the day, there are hundreds of 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.