Let’s do it – let’s talk about OLAP cubes. With the amount of data only growing, exponentially for some, Business Intelligence (BI) data stores are becoming more and more prevalent – and are sensible ways for modern organizations, companies, and corporations to access, store, and organize company data for financial reports, budgets, and dashboards, as well as financial consolidations. Whether you’re relying on a data mart, data warehouse, or an OLAP cube, your data queries won’t slow down the Enterprise Resource Planning (ERP) system, and you can grab multiple types of data to enrich and broaden your analyses. But this article will specifically zoom in on OLAP cubes. Who manages them? What are they? When do OLAP cubes come into play? Where are they staged? Why would you choose an OLAP cube over another BI data store?
Data is driving decision-making at all levels because data continues to grow in size and significance. Logging, storing, and evaluating data has become a big part of the business culture today. In order to produce rich, helpful financial reports for an analysis of a company’s opportunities and challenges, you will have to routinely store, access, and manage your data. Because of this reality, you might feel overwhelmed your technology options for storing your data. You have a few options, but they vary in functionality, and one might be better for your company than another, depending on what analysis goals you are trying to achieve. This article will explore data warehouse and OLAP cube based reporting.
Data integrations can be confusing, and they make a difference when picking the right BI tool. This article will discuss the difference between live reporting for Microsoft Dynamics versus data warehouse or OLAP cube integrations.
Data, data, data. These days, companies are swimming in it. Transactional and operational information is a required aspect of conducting business these days – for many reasons, but perhaps, especially when it comes to making important decisions about the future of the company. Business Intelligence (BI) involves analysis of enterprise data for understanding of trends and trajectories, successes and failures, as well as related planning or forecasting for the fiscal year.
Whether a company is using an OLAP cube or some sort of data warehouse, important company facts and figures, such as transactions, personnel information, and inventory, are used in Enterprise Resource Planning (ERP) systems and BI tools like financial report writers, budgeting solutions, and dashboards. When selecting which BI tool to use, an important question arises pretty early in the selection process: is it more advantageous to integrate live to and from ERP systems like Microsoft Dynamics, Sage, Acumatica, or SAP Business One, or to integrate to a BI data storage option?
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.