Data Warehouse vs. OLAP Cube

How to store your data is an important facet of Business Intelligence analytics.  This article will highlight the differences between Data Warehouses and OLAP Cubes.

Data becomes an increasingly buzz-y trending topic in the business world as the amount of data that a company logs, stores, analyzes, and utilizes continues to grow.  Storing and accessing relevant data is imperative for reporting company performance and planning for growth and development in the future.  However, outside of having an IT or programming background, comprehending the technology options for data storage can be a challenge.  The options are few, but their functionalities vary, and depending on the various Business Intelligence (BI) requirements that need to be met, the product may require a certain data storage solution.  This article is going to focus on discussing and comparing the two most common options: Data Warehouse versus an OLAP Cube.

First things first: defining the two options.  A data warehouse is simply a database that houses information to support decision-making, managed separately from a company’s operational database.  It supports the processing of organizational information by offering a stable platform of consolidated, transactional, organized data.  On the other hand, OLAP stands for online analytical processing and cube is another word for a multi-dimensional set of data, so an OLAP cube is a staging space for analysis of information.  Basically, a cube is a mechanism used to query data in organized, dimensional structures for analysis.  These two options have different IT requirements.

Data Warehouses historically have been a development project that can be pretty pricey just to build.  However, data warehouses are now also being offered as a product – fully built, configurable, and able to house multiple types of data.  Some data warehouse solutions can be managed by the business user.  An OLAP cube is not an open SQL server data warehouse, so it requires someone with OLAP technical skills and experience to manage the server.  This translates to specific personnel requirements, but as OLAP cubes are being used all over the business sector, there is logically enough people in the workforce with the skill set required to manage the cube – if the budget allows for the expense of this position.  While cost is an important factor to consider, there are several characteristics to think about.

Understanding OLAP cubes and data warehouses better is extremely beneficial for making choices about implementation of BI tools.  For some, since company data is needed for such regular analysis, data storage could very well drive the route an organization would take for acquiring BI solutions.  For others, the investment in a vault of transactional data might be a secondary purchase, in response to BI processing needs.  Either way, the differences are important when making a data storage decision.

A Data Warehouse is organized with business user accessibility at the center of design.  It is subject-structured, meaning that it is organized around topics like product, sales, and customer.  Because data must be replicated from an Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or any other data organization system, data warehouses are not used for live analysis.  Instead, the information housed within data warehouses can be used for periodical reporting; planning, forecasting, and modeling; and in dashboards or graphical scorecards where trends and trajectories of company data can be visually analyzed.  Moreover, a warehouse can house a wide variety of data types.

A data warehouse is crafted in such a way that it can integrate several, disparate data sources to create a consolidated database.  This is done through data cleaning and data integration techniques that are “smart” processes innate to the data warehouse.  Therefore, a company can store personnel data, financial transactions, and any other organizational information all in one place – going beyond numbers and cash flow.  It is a very accessible storage unit where data is replicated and transformed from the operational environment.  There are really only two operations when accessing the data: the initial loading of the information and the access itself.  Data warehouses do not require any recovery, transaction processing or concurrency control mechanisms – they stand simply and securely on their own.  Data warehouses are so efficient in terms of business user accessibility because of their SQL server framework.  However, they can be bought directly already built, or the price tag associated with developing a home grown data warehouse over time by programmers or other IT professionals can be overwhelming.  Pricing Data Warehouses and OLAP projects could warrant its own blog article.

An OLAP Cube basically takes a spreadsheet and three-dimensionalizes the experiences of analysis.  Breaking it down, OLAP means analytical data as opposed to transactional, and the cube part of the nomenclature refers to the storage aspect.  OLAP cubes are basically multi-dimensional databases.  They store data for analysis, and a lot of BI products rely on OLAP cubes for access to company information for reports, budgets, or dashboards.  For example, a CFO might want to report on company financial data by location, by month, or by product – these elements would make up the dimensions of this cube.  However, OLAP cubes are not SQL server relational databases, like data warehouses are.

OLAP cubes are not an open SQL server data warehouse, so they require someone with the know-how and the experience to maintain it, whereas a SQL server data warehouse can be maintained by most IT people that have regular database training.  This aspect accordingly has a price tag attached to it.  Whether allocating time and energy from a current employee to focus on management of the OLAP cube or seeking a new, perhaps full-time employee to join payroll for this role.  Additionally, OLAP cubes tend to be more rigid and limited when it comes to designing reports because of their table-like functionality.  Aesthetics and capabilities could and arguably should be important to a company who is building their portfolio of BI solutions.

The take-home of this article could be that, in looking for BI tools, consider what format of data storage the product utilizes for analysis.  Understanding OLAP cubes and the pros and cons of data warehouses should result in a preference – and the storage of company data, exponentially bigger amounts by the year, is arguably a foundation for successful analysis.  Solver offers a fully built, configurable data warehouse stand-alone and as part of the comprehensive suite of BI modules and would be happy to answer questions and generally review BI360’s easy-to-use Data Warehouse solution for collaborative, streamlined decision-making capabilities.