Best OLAP Tools Shortlist
Here is my roundup of the best OLAP tools available today.
- Adverity - Best centralized data management for marketing teams
- SplashBI - Best all-in-one framework to drive efficiency
- Tableau - Best for advanced management in mission-critical use cases
- Domo - Best for predictive analytics using AI and machine learning
- Sisense - Best for building intelligent analytics into products
- Grow - Best for full-stack business intelligence with no coding
- Reveal - Best for providing pre-trained AI models developed by a team of data scientists
- Toucan - Best for data storytelling for your customers
- icCube - Best embedded analytics solution for SaaS
- Style Intelligence - Best for Mashup-driven dashboards
- Microsoft Power BI - Best for translating insights to impact your entire team
- Minitab - Best for integrating with open-source languages, R, or Python
As business intelligence continues to evolve, predictive analytics can improve your decision-making processes through more precise and dynamic forecasting. Because the technology in this sector is growing in leaps and bounds, there are a lot of AI tools that can help you get ahead of the competition, like OLAP tools.
Learn more about this computing approach and discover which platforms and software options deliver the features and results you need for your organization.
What Are OLAP Tools?
OLAP tools fall under the class of business intelligence (BI), as they take a computing approach to answer multi-dimensional queries faster. These tools also roll data mining, relational databases, and reporting features into one.
OLAP tools have three basic analytical operations, including:
- Drill down: This type of technique is designed to allow you to navigate data details by viewing the retail patterns of individual products.
- Consolidation: Also known as roll-up operation, this analytical technique is about data aggregation in multiple dimensions.
- Slicing and dicing: With this technique, you take a set of data and slice it to see the data from various viewpoints.
Overviews of the 12 Best OLAP Tools
Let’s take a dive into my overview of each OLAP tool to get a snapshot of their inner workings, and an exploration of their standout features, essential integrations, and a few pros and cons that may help you narrow down the best choice.
1. Adverity - Best centralized data management for marketing teams
As an integrated data platform for marketing teams, Adverity specializes in empowering marketers with automated data integration from hundreds of sources, as well as providing a single source of knowledge that reveals the tangible impact of all marketing activities on your business.
Why I picked Adverity: This platform made my list because it was designed for non-technical users, like your marketing team. You can set up automations that enhance and unify your data with no coding required through easy-to-use script templates that automatically transform your data.
Adverity Standout Features and Integrations
Standout features include a data dictionary that’s auto populated to manage metrics and data mapping, as well as built-in data governance to ensure data quality and completeness.
Integrations include Box, Google Ads, Google Analytics, HubSpot Marketing Hub, Instagram, Mailchimp, Microsoft SharePoint, SEMrush, Shopify, and Twitter. The platform offers pre-built API connectors along with customizable universal connectors.
Pricing One-Liner: From $500/month
Free Trial One-Liner: 14-day free trial available
Pros
- Easy to use once training is complete
- Hundreds of integration options
- Frequent updates and improvements
Cons
- Some limitations on automations
- Add-on tools can cost extra
2. SplashBI - Best all-in-one framework to drive efficiency
This comprehensive BI and data visualization platform is built to provide you with instant access to data from multiple systems in the form of visualization, reports, and trends. SplashBI democratizes data with simplified interface tools to visualize data in real time from various sources and share insights with all stakeholders.
Why I picked SplashBI: This tool caught my eye because it stores metadata in MySQL or Oracle database to provide your organization with your preferred architecture and configuration. This repository is accessible both on-premise and in the cloud.
SplashBI Standout Features and Integrations
Standout features include ad-hoc reporting for quick answers to urgent questions and in-depth dashboards through the Data Pipe models for faster go-live.
Integrations include Amazon Redshift, Cornerstone, Excel, Google Ads, Kronos, Marketo Engage, MySQL, Oracle, Salesforce Sales Cloud, and Workday.
Pricing One-Liner: From $9.99/month
Free Trial One-Liner: 14-day trial available
Pros
- Easy to use
- User-friendly with minimal technical experience
- Best tool for integrating with Salesforce
Cons
- Advanced tools require more technical knowledge
- Limits how many applications can run in a single workspace
3. Tableau - Best for advanced management in mission-critical use cases
Designed to provide you more depth and serve the needs of the largest global enterprises, Tableau can fit in with any data strategy and workflow. Instead of dictating your performance management, it adapts to your environment with more flexibility in no-code queries, interactive data stories, and metadata management.
Why I picked Tableau: I like this OLAP system because it gives you more control over metadata management. With Tableau, you can modify number formats, create data subsets with selected groups of points, combine fields into groups, rename fields, and build new hierarchies.
Tableau Standout Features and Integrations
Standout features include cloud or self-hosted server options, enhanced security with Customer-Managed Encryption Keys (CMEK), and a content migration tool to define and schedule migration plans.
Integrations include Asana, ClickUp, Google Analytics, MATLAB, OptiSigns, Procore, Salesforce Sales Cloud, Smartsheet, SurveyMonkey, and Yodeck.
Pricing One-Liner: From $15/user/month
Free Trial One-Liner: Free trial available
Pros
- Intuitive and easy to use
- A lot of customization options
- No-code functionality, like drag-and-drop
Cons
- Some data syncing issues
- Lags when interfacing with Big Data
4. Domo - Best for predictive analytics using AI and machine learning
Domo offers end users a cloud-native data platform that’s designed to go beyond traditional business intelligence analysis with user-friendly dashboards, actionable insights, and data visualizations that anyone can understand. Domo can connect with existing legacy and cloud systems.
Why I picked Domo: This platform made my list because it can incorporate ML and AI into your workflows. With these technologies, you can infuse your existing data with additional insights and ask questions in natural language queries for live analysis that helps you keep up with trends.
Domo Standout Features and Integrations
Standout features include ModelOps to reduce manual work and streamline deployment, as well as business-facing dashboards to share business data with non-technical users.
Integrations include Basecamp, DocuSign, DropBox Business, GitHub, Google Drive, Jira, Mailchimp, Microsoft Excel, SurveyMonkey, and Trello.
Pricing One-Liner: From $83/month
Free Trial One-Liner: Free plans available
Pros
- Drag-and-drop ETL capabilities
- Over 1,000 pre-built data connectors
- Android and iOS native apps available
Cons
- No navigation across related objects to retrieve fields in different data sets
- Customer service reps are mainly non-technical
5. Sisense - Best for building intelligent analytics into products
As a data and analytics platform, Sisense strives to make it easier for your team to mashup data from across your entire landscape and transform it into actionable analytics that you can embed anywhere, including reports, web pages, and presentations. Sisense offers functionality in the cloud or on-premise, as well as a mix of both to provide more versatility.
Why I picked Sisense: I chose this platform for my list because they have a Notebook feature that allows you to leverage a combination of cached or live data models for optimal performance and resource management. Whether you do ad hoc analysis or build something more advanced, you can prep, cleanse, and perform sophisticated data transformations through SQL queries, R-code, and Python.
Sisense Standout Features and Integrations
Standout features include Git-based workflows to visualize and augment business analytics and scalable data processing for all skill levels, including no code, low code, and full-code.
Integrations include Dropbox Business, Gmail, Google Drive, Microsoft Excel, PayPal, QuickBooks Desktop Enterprise, Mailchimp, Salesforce Sales Cloud, Slack, and SurveyMonkey.
Pricing One-Liner: Pricing available upon request
Free Trial One-Liner: Free demo available
Pros
- Very integratable with common databases
- Highly customizable dashboards and reporting
- Attractive user-interface design
Cons
- Customization can be tedious
- Installation is long and complex
6. Grow - Best for full-stack business intelligence with no coding
This no-code BI platform is meant to empower your entire organization to explore data for better decision-making for every team. Grow combines data warehousing, ETL, and visualizations with an OLAP server to help you connect and explore data and surface new insights.
Why I picked Grow: This platform has a few unique features, including automated reporting that keeps your data pipelines fresh with sync schedules that refresh your dashboards and other reports as the information changes. Customize your KPIs and metrics to see the things you truly care about.
Grow Standout Features and Integrations
Standout features include an OLAP database where you can access reusable data tables from multiple data sources and permissions-based control to authorize and assign raw stored tables.
Integrations include Dropbox, Facebook Ads, Google Ads, Google Analytics, Google Sheets, HubSpot, Instagram Business, MySQL, PostgreSQL, and Stripe.
Pricing One-Liner: From $1,500/month
Free Trial One-Liner: Free trial available
Pros
- Unlimited user license pricing model for better budgeting
- Dashboards are easy to read
- Knowledgeable customer support team
Cons
- Doesn’t offer all BI capabilities
- Limited export capabilities
7. Reveal - Best for providing pre-trained AI models developed by a team of data scientists
As a web-based eDiscovery software, Reveal can help your organization find, sort, and store electronic data to gain actionable insights from unstructured or structured datasets with a focus on AI and machine learning technology. This platform focuses on streamlining litigation, configuring workflows, and managing investment processes.
Why I picked Reveal: One standout feature from this platform is the pre-generated AI models that are designed by the in-house data scientist team. You can access out-of-the-box solutions, including data cleanup, toxic work environment detection, privileged document detection, and sexual harassment detection.
Reveal Standout Features and Integrations
Standout features include large language models for predictive content generation and legal hold management for discrete data preservation.
Integrations include NexLP.
Pricing One-Liner: Custom pricing available
Free Trial One-Liner: Free trial available
Pros
- Can be used in a cloud environment
- Intuitive navigation and interface
- Lots of built-in features
Cons
- Pre-defined reports could be better
- Can’t print directly from the platform
8. Toucan - Best for data storytelling for your customers
Instead of revealing data about your internal operations, Toucan is designed as a customer-facing analytics platform to ensure you’re providing the best end-user experience. Toucan provides guided data visualization and an app gallery you can use as a starting point for dashboard customization.
Why I picked Toucan: The thing that I found really cool about this platform was its focus on making data more interesting. With the Storytelling Framework and Guided Design features, you can create professional, engaging reports without needing to code or design.
Toucan Standout Features and Integrations
Standout features include Zero-Click Mobility that allows you to create an application once for access on any device and live data connections with any API-ready cloud data source.
Integrations include Aircall, Amazon Web Services, Azure, Microsoft SQL Server, MicroStrategy, MongoDB, Onedrive, SAP HANA, Snowflake, and Soap.
Pricing One-Liner: Custom pricing available
Free Trial One-Liner: Free demo available
Pros
- Versatile software can integrate with any workflow
- It supports many reporting visualization styles
- Customer support is friendly and knowledgeable
Cons
- Can be daunting to learn initially
- High learning curve that requires customer support
9. icCube - Best embedded analytics solution for SaaS
As a B2B SaaS solution, icCube is designed to deeply embed analytic capabilities inside your applications through white-labeled dashboards, multidimensional OLAP analytics, and fine-grained security features. icCube is built to shorten your time to market and continually respond to changing market expectations and customer demands.
Why I picked icCube: This platform made my list due to its robust architecture. This platform offers a single Java process that runs on any OS as a result of modular architecture and respect for well-accepted language standards, including J2EE, HTTP, and JCR. You can set it up as a high availability solution with great reliability or as a redundant solution to maintain consistency.
icCube Standout Features and Integrations
Standout features include a self service dashboard editor with real-time data in a browser-based solution and branded design across all reports using the out-of-the-box wizard or Javascript.
Integrations include GitHub, Java data sources, Microsoft Excel, OLAP4J, Python data sources, and XMLA. Available REST API.
Pricing One-Liner: From $24,000/year
Free Trial One-Liner: Free trial available
Pros
- Very easy to use the platform
- Has a lot of flexibility
- Designed for quick embedding into other tools
Cons
- High pricing compared to competitors
- Restricted extensibility and expansion
10. Style Intelligence - Best for Mashup-driven dashboards
This platform from InetSoft Technology is a data mashup and BI application for large organizations that need advanced tools to help them connect data from a variety of sources to generate valuable, actionable insights. Style Intelligence offers a versatile analytics engine that can operate on a standalone server, as well as large big data clusters based on Hadoop or Apache.
Why I picked Style Intelligence: This tool made my list because it offers iterative Mashup and visualization for reporting and dashboards. You can profile data and verify manipulations quickly through data visualization. You can also build visualization dashboards and add data transformations for even more efficient visual output.
Style Intelligence Standout Features and Integrations
Standout features include in-cloud and on-premise data with diverse formats and structures and deliverable document reports that can be accessed online or sent while offline.
Integrations include Adobe Analytics, AirTable, Buffer, Campaign Monitor, Chargebee, Flat files, FreshBooks, Freshdesk, SAP ERP, and SAP HANA.
Pricing One-Liner: From $165/month
Free Trial One-Liner: Free plan available
Pros
- High quality support from account managers
- Easy to learn and use
- Dashboards and visualizations always load quickly
Cons
- Intricate data block management can be tough
- White Label could be a little more streamlined
11. Microsoft Power BI - Best for translating insights to impact your entire team
This suite of analytics tools is customizable with pre-built dashboards that help you unify important metrics and show real-time updates on every device. Microsoft Power BI delivers a centralized source of knowledge that connects organizational data while Power BI for Windows delivers report authoring features and local data mashup data. You can then query the data using natural language.
Why I picked Microsoft Power BI: One unique feature from this platform is the Copilot feature, which helps you uncover insights faster. You simply describe what you need, including calculations, reports, or narrative summaries, and ask a question about your data. Copilot will do all the work, pulling, analyzing, and visualizing the right information.
Microsoft Power BI Standout Features and Integrations
Standout features include semantic models that empower you to combine self-service and enterprise analytics, as well as industry-leading AI that helps non-data scientists prep data and find insights.
Integrations include ActivTrak, MATLAB, Microsoft Suite, OptiSign, QuickBooks Online, Reach, Smartsheet, UpKeep, Xero, and Yodek.
Pricing One-Liner: From $12.80/user/month
Free Trial One-Liner: Free trial available
Pros
- Easy to learn how to make custom reports
- Robust collaboration tools for sharing
- Easy to integrate data sources
Cons
- Steep learning curve for non-BI users
- No data cleansing solution
12. Minitab - Best for integrating with open-source languages, R, or Python
Minitab is a statistical software that integrates with a variety of open-source languages, as well as boosting your capabilities with machine learning algorithms, including TreeNet, Random Forests, and CART. You can also visualize your data in the Graph Builder, exploring any number of graph and chart configurations without re-running your analysis.
Why I picked Minitab: I put this platform on my list because it prioritizes better outcomes for products and processes through key statistical tests. With Minitab, you can run one and two proportions, t tests, chi-square, normality tests, and equivalence tests through their integrated approach.
Minitab Standout Features and Integrations
Standout features include measurement system analysis with gage studies and attribute agreement analysis, variable, time weighted control charts, and design of experiments with screen designs, full factorial, and fractional factoria.
Integrations include Amazon Aurora, Amazon Redshift, Box, CircleBack, Constant Contact, JMP, Microsoft PowerPoint, Microsoft Word, SIMUL8, and WinSPC.
Pricing One-Liner: Single-user annual license starts at $1,780
Free Trial One-Liner: 30-day free trial available
Pros
- Very feature-rich platform
- Easy to use and navigate
- Large and knowledgeable support community
Cons
- No visual or low-code options
- Interface feels a little bit outdated
Other Options
My list of the top OLAP tools doesn’t have every single option out there, so here are a few more that might be the right fit for your organization:
- Qlik Sense - Best for transforming your organization through the democratization of BI
- Looker - Best for self-service, governed, and embedded business intelligence tools
- OriginPro - Best for graphing data for science and engineering needs
- Databox - Best for building custom dashboards and reports with thousands of metrics
- Integrate.io - Best for designing a no-code data pipeline for data teams
- IBM Cognos - Best for data accessibility through a natural language AI assistant
- Pentaho BI - Best for full-solution functionality with optimization, integration, and analytics
- Apache Kylin - Best analytical data warehouse for big data
Selection Criteria For OLAP Tools
Here’s a short summary of the main selection and evaluation criteria I used to develop my list of the best OLAP Tools for this article:
Core Functionality
OLAP software is an incredibly powerful way to better understand the data you’re getting, but there are some core functionalities that every software on my list prioritizes, including:
- Being easy to read and understand
- Customization and flexibility
- Uncover new patterns and relationships
- Monitoring and recording ongoing business transactions
Key Features
The OLAP tools on my list offer plenty of features to ensure the functionality above, but some of the most important things to look for include:
- Front-end flexibility: Your data should be democratized to ensure many people throughout your organization can use and understand these tools to get as many perspectives and insights as possible. With a non-relational database, your team can utilize drill-down, slice-and-dice, and roll-up functionality to begin data analysis.
- Strong metadata layer: Metadata in this case refers to the structure of the OLAP Cube and its various multi-dimensional elements. These elements may include the characteristics of objects that you are using for database architecture and makes it easier to search, manage, and link the contents of the database.
- Security features: You’ll see a variety of security options, including enterprise-grade security with SSO or AD Synchronization. You may also want to make sure you can control access with permissions by roles and responsibilities to ensure sensitive information is safe.
- Custom reports: Data visualization is an incredibly important aspect of understanding your data and gaining the insights you need to make or explain various business decisions. Make sure you can automate, template, and customize your reports to make them easier to read.
- Data and monitoring: To get the most accurate analytics, your system needs as much data as possible, so many of these tools offer access to at least 15 different sources, and some even have unlimited integrations with syncing at regular intervals.
Usability
OLAP tools are by nature a little more complex and difficult to learn how to use. However, that doesn’t mean that they should only be accessible to researchers, scientists, and data teams. I chose tools for my list that offer pre-built dashboards and reporting that can deliver insights to various teams within your organization. I also focused on intuitive navigation, user-friendly interfaces, and a robust support team.
Software Integrations
Because data sources are a big part of OLAP analysis services, integrations are critical to ensuring you have all the information you need at your fingertips. You’ll see which tools have tons of options in their breakdown where I list the most popular integrations, as well as whether or not they require Zapier.
Pricing
Pricing for OLAP tools can be a little confusing because it’s often based on functionality. How many people need access to the tools? How many unique data rows do you require each month? You can expect pricing to be subscription-based and customized to your needs for the most part. Tools can cost as little as $10/user/month or up to $2,000 a month.
People Also Ask
Still need more information to get the full picture on OLAP tools? Check out the answers to some of the most frequently asked questions here.
What are the benefits of OLAP tools?
Who uses OLAP tools?
What are the 3 types of OLAP?
Other Revenue Operations Software
Are you looking for other BI tools to round out your tech stack? Take a look at these overviews to find the top SaaS options to fit your needs:
- Business Intelligence Software For RevOps
- Business Development Software
- Revenue Intelligence Platforms
- Sales Intelligence Software
- Predictive Analytics Software
- Sales Forecasting Software
Next Steps
As you review these OLAP options, consider the predictive analytics strategies you already have in place and what systems you’re currently using. You might be able to supplement or exchange your current digital tools with something from my list. Explore some free trials and demos to see which ones would integrate best with your workflow before deciding.
But The RevOps Team is here to provide so much more when it comes to enhancing revenue operations and improving your business model. If you’re interested in even more insights from the experts, sign up for the newsletter to get up-to-the-minute advice delivered to your inbox.