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Showing 1 - 20 of 192 products
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Showing 1 - 20 of 192 products

BOARD

Created to combine business intelligence, corporate performance management, and business analytics, BOARD is a full-featured business intelligence system that serves midsize and enterprise-level companies in a variety of different...Read more

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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

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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

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InsightSquared

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

4.65 (50 reviews)

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TARGIT

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

4.47 (34 reviews)

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SetSight

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

0.00 (1 reviews)

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Necto

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

4.26 (40 reviews)

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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

4.31 (82 reviews)

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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

4.00 (1 reviews)

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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

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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

Infor Birst

Birst, an Infor Company, 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 mode...Read more

4.08 (49 reviews)

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DataRPM

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

4.00 (1 reviews)

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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

4.52 (358 reviews)

1 recommendations

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Stratum

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

4.19 (48 reviews)

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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

4.34 (55 reviews)

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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

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GoodData

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

4.31 (13 reviews)

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Service Lifecycle Management

Service Lifecycle Management from DEX Systems offers a comprehensive, integrated suite of SCM applications. They're a great fit for any company who manages high value or volume returns. ...Read more

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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

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Phocas Analytics

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

4.79 (98 reviews)

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Popular OLAP Software Comparisons

Buyers Guide

Last Updated: March 09, 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.