Find the best OLAP Tools

Compare Products

Showing 1 - 20 of 190 products


Board is the Intelligent Planning Platform that offers smarter planning, actionable insights and better outcomes for more than 2,000 companies worldwide. Board allows leading enterprises to discover crucial insights which drive bu...Read more about BOARD

Learn More


Tableau is an integrated business intelligence (BI) and analytics solution that helps to analyze key business data and generate meaningful insights. The solution helps businesses to collect data from multiple source points such as...Read more about Tableau

Learn More

SAP BusinessObjects Business Intelligence

SAP BusinessObjects is a business intelligence solution designed for companies of all sizes. It offers ETL (extract, transform, load), predictive dashboard, Crystal reports, OLAP (Online Analytical Processing) and ad-hoc reporting...Read more about SAP BusinessObjects Business Intelligence

Learn More


InsightSquared is the sales intelligence solution. InsightSquared provides sales leaders with the operating system to run their team, produce reliable forecasts, measure rep performance, and maximize revenue growth. Sales leaders ...Read more about InsightSquared

4.62 (51 reviews)

Learn More


TARGIT Decision Suite is a business intelligence and analytics solution that offers visual data discovery tools, self-service business analytics, reporting and dashboards in a single, integrated solution. TARGIT combines the ...Read more about TARGIT

4.47 (34 reviews)

Learn More


SetSight offers a cloud-based business intelligence solution to collect, analyze and report key data associated with sales trends, replenishments, category management and more. It features order management and forecasting, reporti...Read more about SetSight

3.50 (1 reviews)


Panorama Necto is a business intelligence (BI) suite that is designed to work in tandem with any data source—OLAP, spreadsheets, relational and in-memory. Necto suits midsize and enterprise-level companies across all major in...Read more about Necto

4.26 (40 reviews)

Learn More

CCH Tagetik

CCH Tagetik is a unified BI and accounting software that helps to optimize financial and operational planning. The solution also shortens the consolidation and closing process and allows users to analyze results, model, as well as...Read more about CCH Tagetik

4.33 (84 reviews)

Learn More

Profitbase EPM

Profitbase offers user-friendly and flexible business performance management tools that can help optimize financial and operational planning, execute work processes, and analyze business data. It enables users to efficiently colle...Read more about Profitbase EPM

4.00 (1 reviews)

ADS - Medical

Agile Data suite (ADS) is a cloud-based business intelligence solution that provides tools to manage and integrate data. The solution is suitable for industries such as automotive, aviation, medical, consumer products and pharmace...Read more about ADS - Medical

No reviews yet

Infor OS

Infor OS is a web-based networked BI and analytics solution that connects insights from various teams and helps in making informed decisions. The tool enables decentralized users to augment the enterprise data model virtually with...Read more about Infor OS

4.08 (51 reviews)

Learn More


Progress is a business intelligence solution that is designed for different industries such as manufacturing, oil and gas, automotive and aviation. It can be deployed as both on-premise and cloud-based solution. Progress prov...Read more about DataRPM

4.00 (1 reviews)


Sisense goes beyond traditional business intelligence by providing organizations with the ability to infuse analytics everywhere, embedded in both customer and employee applications and workflows. Sisense customers are breaking th...Read more about Sisense

Learn More


Stratum by Silvon is a robust business intelligence solution that was designed to meet the unique needs of business professionals working for manufacturing and distribution companies. Stratum offers a full suite of integrated...Read more about Stratum

4.19 (48 reviews)

Learn More

TIBCO Spotfire

TIBCO Spotfire provides executive dashboards, data analytics, data visualization and KPI push to mobile devices. It complements existing business intelligence and reporting tools, while midsize organizations can use dashboards and...Read more about TIBCO Spotfire

4.36 (57 reviews)

Learn More

Skookum Digital Works

Skookum Digital Works (SDW) provides custom-build business technology assets that help companies to solve business problems and drive their business outcomes. They also provide UI/UX designers, product strategists, and software de...Read more about Skookum Digital Works

No reviews yet


GoodData is a cloud-based business intelligence platform that delivers progress analytics in real-time. With their integrated suite of tools and applications, users across departments will have access to critical metrics, giving v...Read more about GoodData

4.31 (13 reviews)

Service Lifecycle Management

Designed to help companies handle after sales supply chains, DEX Systems’ Service Lifecycle Management solution helps businesses manage their end-to-end operations, from returns and repairs to remarketing and warehouse planning. ...Read more about Service Lifecycle Management

No reviews yet

Analance Business Intelligence Suite

Analance Business Intelligence Suite by Ducen is a hybrid business intelligence (BI) solution that caters to businesses across various industries. Key features include data integration services, application development and mainten...Read more about Analance Business Intelligence Suite

No reviews yet

Phocas Software

Phocas is a team of passionate professionals who are committed to helping people feel good about their data. Our software brings together organizations’ most useful data from an ERP and other business systems and presents it in a ...Read more about Phocas Software

Learn More

Buyers Guide

Last Updated: November 24, 2022

Online analytical processing, or OLAP, is a software capability used to create actionable business intelligence from a company’s available data by empowering analysts to navigate hierarchical relationships between categories and levels of detail in the data (known as dimensions). The power of OLAP is its ability to identify and anticipate trends—goals which are central to most business intelligence initiatives. It’s important that companies shopping for business intelligence (BI) tools be familiar with OLAP. Both end-to-end BI platforms and modern, self-service BI tools offer traditional OLAP or equivalent capabilities for multidimensional analysis.

In this guide we discuss:

What is OLAP?
What Sets OLAP Apart
Common Functionality of OLAP Tools
Benefits of OLAP Software

What is OLAP?

As we’ve already suggested, the primary characteristic of OLAP is that it’s multidimensional. Dimensions of data include geographic categories (country, city, state), temporal categories (year, month, day), levels of aggregation (total sales, sales by store, sales by dept), etc.

You may have noticed that these dimensions can generally be grouped into conceptual hierarchies, and OLAP allows analysts to easily navigate between levels in these hierarchies to understand business problems. For instance, to understand why total sales plummeted in a given quarter, it may be necessary to drill down to a more detailed level: sales by store, and then compare that category with data on the types of products sold.

Generally, OLAP tools are used for historical analysis aimed at deriving insights about trends affecting the business, problems, opportunities for growth etc. In most contexts, a human user guides the analysis. This is opposed to operational analytics, or analytics aimed at processing data used in the business’s operations in real time or near real time.

Let’s take a closer look now at what distinguishes OLAP from operational analytics.

What Sets OLAP Apart

OLAP is frequently compared to OLTP, or online transactional processing. While OLTP handles the processing of data created in a business’s typical day-to-day operations, OLAP seeks to identify trends and help companies better prepare for the future.

Other differences between the two are highlighted in the chart below:

Function Monitors and records ongoing business transactions, such as purchases and sales. Finds patterns that can help explain issues. Used to guide future plans and strategies. OLAP servers are commonly used in data mining and data warehousing operations.
Query types Simple, standardized queries, such as: “How many units did Store 26 order last month?” Complex, multidimensional queries, such as: “When Store 26 places a larger than average order, which other stores place larger than average orders the following month?”
Data source Core business processes Data gathered through OLTP
Database format Relational; often presented in tabular form Non-relational; comprised of data “cubes”
Read/write Dataset is read/write and updated frequently. Dataset is typically read-only to ensure it doesn’t get changed while analysis is underway.

An OLTP database can be represented as a simple table or spreadsheet. This is easy to do because OLTP databases have a limited number of variables and the variables are directly related to one another.

This simple table shows average sales per day for each of a department store’s nine salespeople (A-I):

OLTP Table With Two Variables

OLTP Table With Two Variables


In the table below, we’ve added information from a third variable. It includes the same information as above: which employee sold how many products—but it now also shows which brands’ products were sold:

OLTP Table With Three Variables

OLTP Table With Three Variables

Both tables above represent the straightforward and limited nature of the types of datasets used in OLTP. They provide simple, clear transactional information—and little else. They may not be fancy, but few businesses operate without them.

Now, imagine the department store from the example has six branches. Each branch maintains its own sales records in a separate OLTP database, similar to those shown above.

The department store’s head office then “stacks” the individual transactional databases from the six branches into one single dataset. This creates a data “cube”—the format used in OLAP systems—also called multidimensional cubes or hypercubes.

OLAP Data Cube

OLAP Data Cube

We’ll continue with this example as we discuss the common functionality of OLAP.

Common Functionality of OLAP Tools

The data cube above looks impressive, but astute readers may have noticed: despite the added dimension, it can still be read like an ordinary table or spreadsheet. Arranging datasets into cubes only makes it possible to begin the analysis.

The actual processing is where the value of OLAP lies. It relies on three common functionalities, all made possible by the flexibility of the non-relational database.

    • Drill-down is used to present more granular detail about a given variable. The company in the example above may wish to focus in on sales of a particular brand’s products. Using a drill-down function, they could de-aggregate the sales-per-brand totals above to learn which items from an individual brand have sold in what quantities.
    • Slice-and-dice lets users look at the business’s datasets from different angles and perspectives. In the above example, the department store might want to correlate sales to a variable other than the individual salesperson or their selection of brands. For example: They might want to know how the number of salespeople working during a single shift across all branches affects sales of one particular brand. The slice-and-dice functionality of OLAP tools makes that possible.
    • Roll-up is the opposite of drill-down, and the two are often used in conjunction. Roll-up combines data into broader categories, decreasing the level of detail. In the slice-and-dice example above, the store might roll-up the per-branch sales information before further analysis, given that they’re not concerned with that variable for this analysis.

Benefits of OLAP

Due to the unique way OLAP arranges data, it offers benefits that other methods—those using relational databases—simply cannot. The wide variety of variables allowed and the ability to slice and dice them any which way gives companies new opportunities to find value in their existing company data.

Many use OLAP systems for predictive analytics, often for the purposes of forecasting and problem solving:

Predictive analytics tool from Halo showing forecast

Predictive analytics tool from Halo showing forecast

The department store from the example might use predictive analytics to determine the ideal number of salespeople per branch for each day of the week. The OLAP tool tries several options and then “predicts” how the number of salespeople might impact sales. The store can then weigh this information against the cost of staffing and determine the ideal number of employees for per day.

Another benefit of OLAP is that it can uncover patterns and relationships that have not been previously considered. This is useful for problem solving. Let’s say the department store above is having a problem with sales of a particular brand’s products, but only in two of its six stores. OLAP analysis could reveal the root cause as an inexperienced manager who works in both of the problem stores.