Increasing amounts of data are flooding people in charge of making business decisions that have to deal with business performance improvement, planning or budgeting. To effectively address these challenges, OLAP technology is emerging, an online analytical process that helps improve company forecasts. In this post we will tell you what OLAP is and what it is for.
OLAP stands for Online Analytical Processing. It is a technology that exists behind many business intelligence (BI) applications. It is a very powerful technology for data discovery, including the ability to render unlimited reports, complex analytical calculations, and plan forecast scenarios such as budgets or forecasts.
How is OLAP technology used? (see here)
This process performs multidimensional analysis of business data and enables complex calculations, trending, and data modeling, thereby providing the information and insights needed for better decision making.
It is the foundation of many types of applications for enterprise performance management, planning, budgeting, forecasting, financial reporting, analytics, simulation, knowledge discovery, and data warehouse reporting.
Companies use databases to store all transactions and records (hence their name “online analytics process”). They are usually full of information, which is why OLAP systems were designed to understand and extract this information and optimize performance.
An OLAP cube is the core of most OLAP systems. It is what is known as a multidimensional matrix-based database, which allows you to analyze and process multiple dimensions of data in a much efficient and faster way compared to a traditional relational database.
A relational database table is structured like a spreadsheet and stores individual records in a two-dimensional format, row by column. Each dataset in the database is located at the intersection of two dimensions, row and column, such as region and total sales.
Relational database and SQL reporting tools can query, report, and analyze multidimensional data stored in tables. However, performance slows down as the amount of data increases as reorganizing the results requires a lot of work to focus on different dimensions. This is where OLAP Cube comes in.
In this sense, OLAP Cube extends one table with additional layers. For example, the cube top layer can organize sales by region. From there, additional layers may be added, which can refer to country, province, city, neighborhood and even a specific store. Theoretically, a “cube” can cover an infinite number of layers. (When an OLAP cube represents over three dimensions, it is known as a hypercube.)
Smaller cubes can also exist within layers; for example, each “store” layer might contain cubes that sort sales by vendor and product. In practical terms, data analysts are responsible for creating OLAP cubes that contain only the layers needed for optimal analysis and performance.
OLAP cubes provide four main types of multidimensional data analysis:
- In-depth analysis: A break operation transforms less detailed data into more specific data by moving down the concept hierarchy or adding a new dimension to a cube. For example, with sales data for an organization’s calendar or fiscal quarter, you can break it down to see sales for each month, while simultaneously moving down the hierarchy of concepts from the “time” dimension.
- Roll Up: It is the opposite of the previous function. Roll Up adds data to the “cube” by moving up the hierarchy of concepts or by decreasing the number of dimensions. For example, you can work your way up the hierarchy of “location” measurement concepts by looking at data from each country rather than from each city.
- Cutting operation: This function creates a secondary cube by selecting one dimension from the parent OLAP cube. For example, a reduction can be done by highlighting all data for the first fiscal quarter or the organization’s calendar (which will be a time dimension). The cubes operation isolates a secondary cube by selecting multiple dimensions in the main OLAP cube. For example, a dice operation can be performed by highlighting all data by the calendar or fiscal quarters of an organization (time dimension) and within two different countries (location dimension).
- Rotation: This feature rotates the cube view to display the new data view, allowing you to create multidimensional dynamic views of your data. This is comparable to the pivot table function in spreadsheet software such as Microsoft Excel, but while pivot tables in Excel can be complex, the pivot table function in OLAP is relatively easy to use (requires less experience) and it is faster with a better response time and query performance.
