Best OLAP Tools
Buyer's Guide
OLAP is a computing approach to multidimensional data analysis. OLAP tools are in demand as companies seek to delve deeper into multidimensional metrics without compromising performance. Many are analytics and BI tools with OLAP engines embedded into them.
Whether you plan to buy a new software solution or add an OLAP engine to your existing one, this buyer’s guide will take you through the steps necessary to choose one that fits. It informs you how to evaluate and compare OLAP systems based on their features and functionality. Additionally, we give you handy tips and resources to aid your software search.
Executive Summary
- OLAP tools enable drilling into multidimensional business metrics to reveal performance insight, including previously unknown patterns.
- OLAP cubes improve query performance by reducing computing overheads on warehouses through in-memory processing.
- MOLAP and ROLAP are the most common OLAP servers in use.
- Read about the most popular OLAP tools in the market to know which features to add to your requirements checklist.
- Spell out your business needs clearly by preparing questions before approaching vendors.
What Are OLAP Tools?
OLAP platforms are software solutions for the interactive analysis of multidimensional information stored in databases and data warehouses. OLAP supports faster ad hoc queries and complex analytical calculations for business intelligence, reporting, predictive analytics and decision support.
While OLAP focuses on analysis, online transaction processing (OLTP) involves executing transactions concurrently in real time, like online banking, order processing, payment workflows and sending messages.
OLTP use cases include eCommerce software, credit card payment processing, record-keeping tools, ATMs and online booking and reservation systems. OLAP enables business reporting for sales, marketing, employee performance management (EPM), budgeting and forecasting, including financial reporting.
Traditional querying methods fall short in the case of multidimensional metrics, as increasing volumes can slow down performance. Additionally, reorganizing the results to highlight different dimensions is resource-draining.
OLAP databases use a multidimensional model for storing information and support complex ad hoc and analytics queries with a fast runtime. OLAP systems are mostly read-only to keep computing time to a minimum.
The OLAP cube is at the center of an OLAP system and abstracts multidimensional data analysis from warehouses and databases through in-memory computing.
Cubes, Dimensions, Measures
An OLAP cube is to OLAP what a schema is to relational data. It’s a multidimensional format created by reorganizing information from multiple relational data sets.
The actual values or facts you analyze, like product quantity and sales figures, are called measures. Dimensions are the various views for a measure in an OLAP cube, for instance, employee, date, customer, product and area fields for the same sales numbers.
Metadata is an essential component of the OLAP cube and contains key information about measure groups and their relationship to dimensions with their storage details. It includes another business-critical element — Multidimensional Expressions (MDX) scripts to add code for data processing. Additionally, you can define user actions to open and view reports and drill into the metrics for a closer look.
OLAP helps you analyze your data through consolidation, drill-down and pivoting.
OLAP Operations
These tools perform four basic operations:
- Drill-down involves moving down the dimension hierarchy in the OLAP cube. For instance, you could expand the sales figures for a region to view the numbers for individual products.
- Consolidation (Roll-up) is the reverse of drill-down and involves gaining a high-level overview by moving up the hierarchy or reducing the number of dimensions. To view the overall projections, you can aggregate individual store data and show it against the consolidated sales dimension.
- Slicing involves isolating a single dimension from the central OLAP cube. Viewing the revenue numbers for the last six months is an example of slicing the year (time dimension).
- Dicing involves drilling down into more than one dimension simultaneously. You can dice years (time dimension) into months and countries (location dimension) into cities simultaneously, creating smaller cubes from the central OLAP cube.
- The pivot feature is similar to the pivot table feature in MS Excel. Fast OLAP querying enables rotating the cube view for disparate data views.
MOLAP, ROLAP and HOLAP
These are terminologies associated with data warehouses representing logical data models.
- Multidimensional OLAP or MOLAP is the concept of directly processing data in OLAP cubes.
- Relational OLAP or ROLAP involves analyzing multidimensional data from relational tables in warehouses. But, performance issues due to complex queries can slow down processing. Additionally, data views in ROLAP are static, making pivoting impossible.
- The hybrid OLAP (HOLAP) approach provides the benefits of ROLAP and MOLAP — access to larger information volumes and fast processing. But, performance issues can slow down your analytics pipeline due to its complex architecture.
Primary Benefits
OLAP answers questions standard tables can’t solve, driving rapid analysis and proactive action to help complete projects on time.
Reduce Time to Market
OLAP systems speed up product development and delivery cycles, helping establish your brand’s market reputation as a serious player. OLAP engines save time by pre-aggregating variables and conducting lightning-speed business calculations. Near real-time insight gives relevant results, giving you a chance to take timely action.
Sharable data cubes save time by promoting collaboration. Additionally, they boost warehouse performance by eliminating the burden of repeated queries.
Gain Data Insight
God is indeed in the details, and so is valuable business insight. Roll-up, slice-and-dice and pivot actions help zoom in and out of granular views in OLAP cubes, revealing correlations you didn’t know existed.
Uncovering data relationships can reveal previously unknown market trends, gaps and opportunities.
For instance, you can slice the location dimension into states, regions and cities for detailed views of performance metrics. Drilling down helps highlight gaps not apparent otherwise and successes you didn’t notice in the high-level view.
Build Comprehensive Reports
Many OLAP tools provide interactive visualizations for ad hoc analysis and reporting. With quick data access, manipulating datasets directly within the data cubes speeds up the analytics process. Despite being fast, OLAP servers perform accurate calculations, ensuring the correct information feeds your reports.
You can build predictive models by moving the datasets around for what-if analysis. Configuring individual permissions at the report level ensures your users see only the pertinent metrics.
Democratize Data
Modern OLAP tools have an intuitive interface with easy user actions to perform analysis without technical assistance. You can analyze data independently by connecting to the desired sources and interacting with the datasets to show the necessary metrics.
Select-and-click and drag-and-drop actions help you perform complex calculations across multiple dimensions — all the SQL querying happens at the backend. You can calculate the revenue earned from stores in Florida as a percentage of the total revenue for the current year and view its progression in a time-series analysis.
OLAP cubes are scalable, limiting data consumption by incorporating only the required datasets.
Key Features & Functionality
Ease of use, accessibility and support for complex calculations and standard dimensions are business-critical features in an OLAP software solution.
| Transparency | The OLAP tool should provide complete transparency into your homogeneous and heterogeneous information sources. Full visibility into the OLAP computing architecture and the underlying databases is essential for monitoring and troubleshooting. |
|---|---|
| Accessibility | OLAP servers seamlessly connect to client systems and source databases. An OLAP tool presents a single source of truth by mapping its logical schema to available data stores, aggregating the information and performing the necessary transformations. Consistent performance is business-critical, irrespective of the increase in the number of dimensions or the database size. |
| Intuitive Data Manipulation | OLAP operations — drill-down, pivot and slice-and-dice — enable a deep-dive analysis of multidimensional information. Intuitive user actions like select-and-click and drag-and-drop are essential to ensure quick turnaround and proactive decision-making. |
| Complex Calculations | Calculations help you extend the cube’s capabilities, flexibility and robustness in business intelligence applications. A calculation is an MDX expression or script used to define a value at run time in a cube. Calculations let you add objects the cube data doesn’t define, and they can reference other parts of the cube, other cubes or values outside the underlying database. The OLAP system should provide powerful calculation methods for sales forecasting, such as moving averages and percentage growth. |
| Location and Time Intelligence | Location and time are standard dimensions for OLAP analysis. Period-over-period comparisons enable reviewing past performance and planning business strategy using what-if scenarios. |
Software Comparison Strategy
Our lean selection methodology breaks down the process for you, whether you plan to buy a new software solution or add an OLAP module to your existing one.
Create a requirements checklist to assess your internal organizational needs. We can help you hit the ground running with our pre-populated, customizable requirements template.
Finalize and freeze your key requirements in consultation with top management and other stakeholders, including teams that will use the software. Convert the requirements into questions and distribute them to potential vendors with a request for proposal (RFP).
Review vendor proposals and rank the products on a scale of 1 to 100, depending on how well they address your business needs. Refer to our Jumpstart platform for access to comparative analysis and scores of leading OLAP tools based on our analyst research.
Cost & Pricing Considerations
Budget constraints can weigh you down when seeking new software solutions or upgrades. It’s best to check the pricing tiers from the vendor’s website or contact them directly. Refer to our pricing guide to learn how much an OLAP solution will likely cost you.
Pricing will vary across products, depending on the deployment option, user licenses and additional modules you choose. Often, the product package includes basic support, though you must opt for a paid plan for personalized assistance. Attention to these details can help you put a number to the total cost of ownership (TCO).
The Most Popular OLAP Tools
We conducted an in-depth analysis of the leading OLAP tools based on how well they deliver enterprise requirements. Here are the three most popular products.
Power BI
Microsoft supports MDX and Data Analysis Expressions (DAX) with live connections to Microsoft SQL Server Analysis Services (SSAS) models. Most Power BI visuals benefit from improved query performance using SuperDAX —- enhancements for consolidating multiple operations within a standard DAX query. Microsoft’s latest innovation is Horizontal Fusion, combining smaller queries into a larger one to reduce processing demands.
Horizontal Fusion in Power BI improves query speed and scalability.
Oracle Analytics Cloud (OAC)
Oracle OLAP is an analytic engine embedded in Oracle Database 12c, enabling centralized management of business information and rules in a scalable, secure platform. It’s a robust enterprise solution, allowing you to create custom functions by combining hundreds of analytics functions. Lightning-speed SQL queries let you deploy OLAP cubes as materialized views.
Defining a materialized view in OAC.
MicroStrategy
MicroStrategy’s versions 9.0 and later support Intelligent Cubes, which the system generates automatically on running new reports against the data warehouse. The platform’s logic automatically matches user requests with a suitable intelligent cube without manual intervention.
MicroStrategy incorporates third-party data mining and modeling tools for predictive analysis.
Multidimensional data analysis and reporting in MicroStrategy.
Questions To Ask
Pose questions to team members and senior stakeholders to understand your teams’ expectations. You can fine tune your requirements list by matching product features to your business needs.
- What’s not working with the present system? Do your company’s needs extend beyond multidimensional data analysis?
- Will add-on OLAP modules work, or will you need a standalone solution?
- What’s your budget?
- Is predictive modeling a deal-breaker?
- Which analytics functions will you need for complex calculations?
Meeting up with vendors is an excellent way to narrow down your shortlist. Preparing questions beforehand saves time and helps you clarify your requirements and expectations during vendor discussions.
- Are the OLAP techniques easy to use?
- Are self-service visualization and dashboard reporting available?
- Does the vendor provide training?
- How often do they release updates and patch fixes?
- What is the average support response time?
In Conclusion
Software selection demands due diligence and extensive research and can be time-consuming. It’s natural to feel frustrated at the slow pace as you sift through seemingly overwhelming information about the various OLAP tools in the market. But it’s worth it.
Staying tuned to your business needs while defining requirements, matching product features and holding vendor discussions can help you make the right choice.
Once you shortlist eligible vendors, contact the top one for final discussions, else move down the list. If all goes well, finalize the purchase and set up a deployment plan with clear timelines on when to go live.
Reach out to our Managed Services team to assist you through the process.
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