OLAP Tools

Compare All OLAP Tools

Filters

Apply Filters:

X

Your Industry

Your Company Size

Price

Sort by

Recommendations: Sorts listings by the number of recommendations our advisors have made over the past 30 days. Our advisors assess buyers’ needs for free and only recommend products that meet buyers’ needs. Vendors pay Software Advice for these referrals.
Reviews: Sorts listings by the number of user reviews we have published, greatest to least.
Sponsored: Sorts listings by software vendors running active bidding campaigns, from the highest to lowest bid. Vendors who have paid for placement have a ‘Visit Website’ button, whereas unpaid vendors have a ‘Learn More’ button.
Avg Rating: Sorts listings by overall star rating based on user reviews, highest to lowest.
A to Z: Sorts listings by product name from A to Z.
Showing 1 - 20 of 193 products
Filters
Showing 1 - 20 of 193 products

Domo

Domo is a cloud-based business intelligence suite and collaboration platform that provides real-time visualizations of company and project-specific data across multiple business units. ...Read more

4.23 (193 reviews)

4 recommendations

ClicData

ClicData is a business intelligence (BI) dashboard solution designed for use primarily by small and midsized businesses. The tool enables end users to create reports and dashboards. A drag-and-drop interface designed for ease...Read more

4.60 (123 reviews)

3 recommendations

PriceDemo

Dundas BI

Dundas BI, from Dundas Data Visualization, is a browser-based business intelligence and data visualization platform that includes integrated dashboards, reporting tools, and data analytics. It provides end users the ability to cre...Read more

4.52 (121 reviews)

3 recommendations

Style Intelligence

Style Intelligence from InetSoft is a business intelligence for midsize to global organizations. It offers users customizable dashboards and a data mashup engine that generates reports and visual analyses from real-time data....Read more

4.55 (42 reviews)

2 recommendations

Sisense

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

4.53 (357 reviews)

1 recommendations

Sigma Computing

Sigma is the business intelligence and analytics solution that allows everyone in your organization, not just analysts, to ask questions and find answers using data. Instant, guided access to the cloud data warehouse enables teams...Read more

4.23 (77 reviews)

1 recommendations

PriceDemo

Logi Analytics

Logi Analytics is a business intelligence (BI) platform that provides self-service analytics tools for businesses. It can be embedded directly into the applications that employees use every day. Key features include a dashboard, d...Read more

4.20 (40 reviews)

1 recommendations

Toucan

With a focus on reducing the complexity of insights from data for business users, even complex tasks are made simple with the Toucan guided analytics platform: Data visualization is guided, allowing the user to focus on the story...Read more

No reviews yet

1 recommendations

PriceDemo

BullseyeEngagement Business Intelligence Dashboards

BullseyeEngagement's Business Intelligence Dashboards synthesize data from various sources and make complex information easily understandable. These highly customizable dashboards were built to give busy business leaders real-time...Read more

No reviews yet

1 recommendations

PriceDemo

Tableau

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

Software pricing tips

Read our OLAP Software Buyers Guide

Subscription models

  • Per employee/per month: This model allows you to pay a monthly fee for each of your employees.
  • Per user/per month: Users pay a monthly fee for users—normally administrative users—rather than all employees.

Perpetual license

  • This involves paying an upfront sum for the license to own the software and use it indefinitely.
  • This is the more traditional model and is most common with on-premise applications and with larger businesses.

Rated best value for money

Google Cloud Platform

Featuring G-Suite and GCP, Google Cloud is a platform that provides a reliable and easy-to-use set of solutions that can be used to tackle the toughest challenges in any type of industry. It provides secure storage options, integr...Read more

PriceDemo

Microsoft Power BI

Microsoft Power BI is a web-based business analytics and data visualization platform that is suitable for businesses of all sizes. It monitors important organizational data and also from all apps used by organizations. Microsoft P...Read more

Google Charts

Google Charts is a cloud-based business intelligence solution designed to help teams visualize data on their websites in the form of pictographs, pie charts, histograms and more. Key features include content management, custom das...Read more

PriceDemo

Operations Hub

Operations Hub allows you to easily sync customer data and automate business processes. It supercharges your HubSpot CRM by synchronizing contacts, leads, and company data with other applications. Operations Hub works two ways a...Read more

Minitab

Minitab is a cloud-based statistical tool designed to help small to large organizations across various verticals such as manufacturing, healthcare, energy, automotive or non-profit discover market trends, predict patterns and visu...Read more

PriceDemo

Grow

Grow is a cloud-based, business analytics and reporting solution suitable for small to midsize organizations. The solution allows users to create customizable dashboards for monitoring business workflows and key activities. Grow...Read more

PriceDemo

Zoho Analytics

Zoho Analytics is a cloud-based reporting and business intelligence solution that offers several applications within its suite, including dashboards, analysis, reporting, data warehousing and more. This solution is used in multipl...Read more

Qlik Sense

Qlik Sense is a business intelligence (BI) and visual analytics platform that supports a range of analytic use cases. Built on Qlik’s unique Associative Engine, it supports a full range of users and use-cases across the life-cycle...Read more

Adaptive Planning

Workday Adaptive Planning, founded in 2003, provides a web-based system for budgeting, forecasting and reporting. The solution is suitable for a wide variety of company sizes. Delivered over the Web in a software-as-a-service (Saa...Read more

TapClicks

TapClicks is a smart marketing cloud-based set of automated marketing solutions designed to work in unison, powered by your data. We make the complex world of marketing easy with everything a business needs to differentiate itself...Read more

PriceDemo

Popular OLAP Software Comparisons

Buyers Guide

Last Updated: January 27, 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:

  OLTP OLAP
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.