

Sisense and Pyramid Analytics are competitors in the business intelligence tools market. Sisense appears to have the upper hand in integration capabilities and customer service responsiveness, while Pyramid Analytics stands out in advanced analytical features.
Features: Sisense is known for ease of integration with various data sources, rich documentation, and intuitive tools for building dashboards. It efficiently combines disparate data sources, processes queries quickly, and offers strong customer support. Pyramid Analytics is recognized for advanced analytical capabilities, including drill-down reports and DAX query support. It offers robust statistical and predictive features, making it valuable for financial and data analysis.
Room for Improvement: Sisense users have reported performance issues with complex data cubes, limitations in export functions, and advanced filtering capabilities. There is also room to enhance the admin experience with additional visualization options. Pyramid Analytics users find it less user-friendly with a steep learning curve, lack of comprehensive tutorials, and slower data refresh rates for large datasets.
Ease of Deployment and Customer Service: Sisense supports on-premises, public and private cloud deployments and is praised for technical support and responsive customer service. Pyramid Analytics, available in on-premises and hybrid cloud configurations, is criticized for complexity and user-friendliness issues but offers reliable support and flexible deployment tailored to organizational needs.
Pricing and ROI: Sisense offers competitive pricing but might be expensive compared to options like Tableau. However, users note a good ROI, particularly in reducing manual report labor. Pyramid Analytics is cost-effective for mid-sized or larger firms, with pricing increasing with additional modules. Both tools deliver notable ROI, with Sisense users highlighting specific time savings and operational efficiencies.
There is a positive return on the data investment for my clients using Pyramid Analytics.
Because we selected Pyramid Analytics for our product and we are not going to throw out all the work I previously did, we just go with Pyramid Analytics for the product.
It makes it very easy for us to make data-driven decisions and we are able to forecast our future predicament or future plans, hence increasing or boosting productivity in my organization.
Due to the data presented to stakeholders, they are able to make informed decisions that impact the day-to-day operations of the client, giving them more insights into what's happening within their organization.
I have seen a return on investment in that we can manage the Sisense environment with a very small number of users.
The one time we had an issue related to something with the logins, they addressed it that morning.
They always answer quite promptly.
The support was very good.
We typically get responses within 24 hours.
Sisense customer support has been top-notch, great, and very responsive.
Regarding Pyramid Analytics' scalability from what I have seen, with the right people managing it, it can handle growing amounts of data and users well.
The whole process takes time with Pyramid Analytics.
It is scalable to a very large extent and we can integrate any third-party tools.
Sisense's scalability is impressive as it can crunch a lot more data and has consistently better performance.
Sisense works really well for simple to medium use cases and scales well.
Pyramid Analytics is stable in my experience; there have not really been issues with downtime or bugs.
Sisense is very stable and can handle a large amount of data quickly.
Any advanced user wants to implement an idea that they have, and while the whole idea of a platform is not necessarily to give a custom solution, I would not mind if they had more in terms of AutoML or that sort of capability.
Visually, when you want to see the whole model and the connections between tables, the view is not friendly.
I would like to see an improvement in the live data connection, specifically making the process faster.
It could provide more connectors to integrate with emerging different data sources to exponentially increase the amount of data it can handle.
I give Sisense a nine because the integration with some third-party tools is good.
It's not more expensive than all our other BI tools regarding Pyramid Analytics.
My experience with pricing, setup cost, and licensing is that it is cost-effective, but for smaller organizations working under a tight budget, this tool might be a bit expensive for them.
There was no significant difference in pricing between Sisense and ThoughtSpot.
My experience with pricing, setup cost, and licensing shows that pricing is a little bit higher when compared to other applications, but that justifies the use case.
After implementing Pyramid Analytics for my clients, I have seen measurable outcomes and specific improvements, such as having a single platform that gets them from the raw data to the endpoint reporting.
I wish it would be more friendly to a developer, not only just to an end customer.
It offers two ways to access data: by cubing the data or hitting it live.
It allows the user to cater to different use cases and is a very fast aggregator across different data sources, giving you the historical context and helping in operationalizing your data.
Sisense has positively impacted my organization by drastically reducing the time taken to build the data cube, and we can see real-time analytics.
| Product | Mindshare (%) |
|---|---|
| Sisense | 1.6% |
| Pyramid Analytics | 1.1% |
| Other | 97.3% |


| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 3 |
| Large Enterprise | 1 |
| Company Size | Count |
|---|---|
| Small Business | 28 |
| Midsize Enterprise | 7 |
| Large Enterprise | 15 |
Pyramid Analytics provides comprehensive BI reporting, data visualization, and analytics capabilities, integrating with systems like SAP and MSAS for enhanced data-driven decision-making across multiple industries.
Pyramid Analytics is a robust platform offering advanced data model capabilities, drill-down reports, and extensive connectors. With support for DAX queries and a range of AI functionalities, it empowers finance departments to analyze data and provide real-time insights for executives. Despite challenges such as non-intuitive visuals and data management difficulties, it remains a versatile tool for enterprise projects, enabling detailed report development, dashboard creation, and self-service analytics.
What are the key features of Pyramid Analytics?Pyramid Analytics is widely implemented across industries such as finance for detailed data analysis and contact center reporting. It supports enterprise projects by enabling user-friendly data exploration that facilitates comprehensive decision-making. With its integration capabilities, it helps organizations leverage existing systems for better analytics outcomes.
Sisense enables data visualization and analytics with ease of use and fast setup, effectively integrating different data sources for efficient decision-making.
Sisense provides an intuitive platform for companies to handle data visualization and advanced analytics without requiring extensive technical knowledge. Quick deployment, custom dashboard creation, and seamless embedding are supported by an API-first approach. It allows integration of data from multiple databases, enhancing speed through in-chip methodology and real-time updates. Users benefit from its ElastiCube Manager and reduced deployment time, contributing to data-driven decisions. Comprehensive documentation supports users in navigating potential challenges.
What are the key features of Sisense?Sisense is commonly used across industries such as product development, sales, and marketing to deliver tailored analytics and dashboards for diverse departmental needs. Organizations leverage Sisense's capabilities for embedding analytics, optimizing financial models, and improving sales strategies. Its intuitive interface allows non-technical teams to efficiently handle and derive insights from large datasets.
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