In the modern world today, competition between companies is very common, especially when they are offering similar products. With the field of Data Analytics and BI(Business Intelligence) becoming popular day by day, companies that offer innovative and unique products & services get the majority of customer share in the market and ultimately higher revenue. When it comes to the field of Business Intelligence, the choice of Sisense vs Tableau is a relatively tough one.
Sisense is a popular AI-driven, BI software company that focuses on providing analytical solutions for all its customers, helping them go beyond the traditional dashboard and visualize their data in an efficient manner. Tableau is a BI and Data Visualization Platform that helps visualize data from almost any data source into your desired format so that you can gather valuable insights from your business data.
This article provides you with a comprehensive analysis of both BI tools and highlights the major differences between them to help you make the Sisense vs Tableau decision with ease. It also provides you a brief overview of both BI tools along with their features. Finally, it highlights a few challenges you might face when you use these BI tools. Read along to find out how you can choose the right BI tool for your organization.
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Factors that Drive the Sisense vs Tableau Decision
Now that you have a basic idea of both technologies, let us attempt to answer the Sisense vs Tableau question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Sisense vs Tableau comparison:
1) Sisense vs Tableau: Data Governance
Data Governance is the process of managing the availability, usability, integrity, and security of data in an enterprise environment.
Sisense incorporates Data Governance by monitoring the actions of 5 main users: Administrator, Data Administrator, Designer, Data Designer, and Viewer. Each of these roles comes with default settings and access configurations. By using the Sisense API, administrators have granular control over the permissions of each role.
Tableau offers Data Governance through multiple models. Some of those models are Centralized, Delegated,, and Self-Regulating models. Companies can choose each model depending on the needs of IT and business groups, the skill level of individuals, and the access share settings on data groups. As each of these models is flexible, they enable companies to adapt their governance models with their growth and sophistication of their needs.
2) Sisense vs Tableau: Embedded & Advanced Analytics
Image Source
Embedded Analytics is the technology that makes Data Analysis and Business Intelligence more accessible for any application or user. As both BI tools offer Embedded Analytics, it is important to understand what features each tool offers towards this.
Sisense offers Embedded Analytics and a platform to develop applications. You can use Embedded Analytics in Sisense and link them to existing applications via iframes, JavaScript APIs, or Plugins. You can also build your own applications in Sisense by using BIoX, a visual template tool that supports customization capabilities via CSS and JSON. Sisense further houses support for integration with Amazon Alexa and many other IoT (Internet of Things) bots and tools.
Tableau also offers Embedded Analytics via iframes or JavaScript APIs depending on the company’s needs. Data Administrators connect directly with data sources without running any SQL queries. Tableau also provides row-level granular security, multi-tenancy, and other security measures for its applications.
3) Sisense vs Tableau: Power & Performance
Processing Power determines how effectively a tool can process data. The processing power is an important factor when choosing a BI tool as it measures the efficiency of the tool to extract valuable insights from multiple data sources.
Sisense uses in-chip technology to process data. This means that it leverages the computer’s CPU to its full extent and this processor-based computing works much faster than tasks that run on the RAM and the disk memory. In-chip technology divides all queries into blocks so that the in-chip processor can access and reuse them in future queries. The more queries you make, the more blocks you have, and the faster Sisense can access the data in subsequent queries. This improves concurrent processing and also supports parallelism in tasks.
Tableau uses a proprietary technology called Hyper to increase parallel queries and improve processing speed. Hyper is Tableau’s new and improved in-memory data engine technology that is designed for extracting, transforming and visualizing analytical queries on large or complex data sets. Its technology is based on morsels, small working units, that can be allotted to multiple cores so that they function efficiently. It increases the query speed almost by 5 times and triples the extraction speed. With Hyper, companies can accelerate their processes and queries accroding to their requirements seamlessly.
To learn more about Hyper, click this link.
4) Sisense vs Tableau: Additional Features
Both Sisense and Tableau offer Additional Features based on Natural Language that produce narratives to explain your user’s data.
Sisense leverages Sisense Narratives License to add Natural Language to its application. By using this feature, Sisense will add English descriptions to individual widgets that provide context to those widgets. Sisense also offers Boto, a natural language bot that companies can use in third-party apps like Slack, Skype, or Facebook. Users can ask Boto a question, and Boto will analyze data and return a Natural Language answer.
Tableau employs Natural Language generation by establishing connections to tools like Wordsmith, Narrative Science, and Yseop. These tools help Tableau configure its dashboards and give detailed explanations about the data being analyzed. Users can use the Ask Data tool in Tableau to ask questions about their data. This allows users to make decisions about their data without any programming experience or interacting with the Tableau dashboard.
5) Sisense vs Tableau: Connectors
Both BI tools offer a wide range of BI platform implementations that help process, store and visualize data from multiple data sources and connections. As each tool offers different data sources, proper procedures must be put in place to import the data.
Sisense offers database connections both On-Premise and on the Cloud. The import process is relatively simple and can be established with few IT resources/infrastructure as well. However, users must have a strong knowledge of SQL to import tables, spreadsheets, and data formatting techniques. Connections are established using Elasticube tools that import data directly into Sisense. Connections can also be made using third-party applications but cannot be saved on Sisense.
Tableau establishes its connections through its Prep and Prep Conductor preparation licenses. Tableau Prep allows users to cleanse and visualize the original data before they import it into Tableau whereas Prep uses 3 views: a visual combiner, a row-level combiner, and a columnar view to import data. Tableau also supports multiple types of connectors. They have File type connectors for CSV, JSON, PDF, etc. They also have Database connectors for popular SaaS platforms like SalesForce, MongoDB, Oracle, Redshift, Snowflake, and many more.In case the source is not mentioned in the list, they support ODBC and JDBC connectors too.Companies need to purchase either license in order to import data into Tableau.
6) Sisense vs Tableau: Pricing Models & Deployment
Both Sisense and Tableau offer flexible deployment and pricing models. Both keep their customer needs in mind and are available On-Premise and on the Cloud.
Sisense has a hybrid deployment method and is available both on the Cloud and on-premise. Pricing follows a pay-as-you-go model and additional features such as embedded analytics and natural language narratives increase the default amount.
The pricing model of Sisense follows a transparent and flexible approach with a ‘No-Surprise’ workflow. Companies that want to use Sisense need to have a meeting with Sisense’s employees. The pricing model of Sisense is shown below.
Image Source: Self
Tableau also follows a hybrid deployment model and is available on a premise server, a public cloud server, or as a fully-hosted private online solution. Recently, it can be deployed on mobile devices as well. Additional features such as Embedded Analytics can easily be integrated too.
The pricing model of Tableau follows a very systematic approach. Prices are divided among individuals and teams. Tableau offers $70 for an individual to use their software. The pricing model for teams is shown below. Additional features can also be added, but they will incur additional charges.
Image Source: Self
Challenges of Sisense
Now that you have a good idea about Sisense, it is now important to understand some of the challenges you might encounter while working with Sisense. The challenges of Sisense are:
- Users need to have a strong technical understanding of BI in order to understand the Sisense dashboard.
- There are no advanced graphics available to create detailed visualizations or a plain default format to create simple visualizations in Sisense.
- The Elasticube functionalities are time-consuming and prone to errors.
Challenges of Tableau
Now that you have a good idea about Tableau, it is now important to understand some of its challenges. The challenges of Tableau are:
- The cost of Tableau is expensive for the budget of many small-middle range companies. It also has inflexible pricing on its products.
- Tableau does not have the best IT support team and suffers from multiple security issues.
- It does not regularly backup its software and has poor after-sales support too.
Conclusion
This article gave a comprehensive analysis of the 2 popular BI tools in the market today: Sisense and Tableau. It talks about both the BI tools and their features and limitations. It also gave the parameters to judge each of the BI tools. Overall, the Sisense vs Tableau choice solely depends on the goal of the company and the resources it has.
Sisense will be a good choice if your team has prioritized the building of analytical applications and has multiple tools integrated into those applications. Tableau is a good choice when it comes to granularity and variety in visualizations. Companies that require high granularity in their tasks over data permissions for internal dashboards and Embedded Analytics will find it much easier to reach their analytical goals using Tableau.
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Share your experience of learning about Sisense vs Tableau in the comments section below.
Aakash is a research enthusiast who was involved with multiple teaming bootcamps including Web Application Pen Testing, Network and OS Forensics, Threat Intelligence, Cyber Range and Malware Analysis/Reverse Engineering. His passion to the field drives him to create in-depth technical articles related to data industry.