Data Warehouse and OLAP Cube-based Reporting for Microsoft Dynamics

How you access your data can determine the speed and efficacy you can achieve in your financial reporting processes.  This article will discuss data warehouse and OLAP cube based reporting.

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.

Let’s begin by defining your options.  OLAP is an acronym for online analytical processing and cube is a term for a multi-dimensional data set, so an OLAP cube is a staging space for information analytics.  More specifically, a cube is a Business Intelligence (BI) data store utilized in querying data in ordered digital spaces for analysis.  On the other hand, a data warehouse is basically a database that stores data to inform decision-making, typically managed separately from an organization’s operational database.  It offers a stable platform of aggregated, transactional, organized information.  These two routes have distinct IT requirements.
An OLAP cube is more specialized, in terms of what it takes to manage the technology.  OLAP cubes are not open SQL server based devices, so they require someone with OLAP-specific technical skills and experience to manage the server.  This means they come with particular personnel requisites, but there are a number of people in the business culture with the know-how needed to oversee the cube – if your budget allows for this expense.  Data warehouses have traditionally been a development project that can be lengthy and expensive to build.  But today, data warehouses are now also being more affordably offered as a fully built product – configurable, with the ability to store diverse data types.  The premier data warehouses can be managed by the business end user.  Cost is an important consideration when it comes to accessing your data for financial reporting, but there are plenty of aspects to research.
Knowing the differences between OLAP cubes and data warehouses is only going to help you in making decisions about implementing financial report writers.  For some, investing in a BI store might be more secondary in their BI roadmap.  For others, analytics are so regular that storing company data dictates the way an organization would decide to acquire BI tools.  Regardless of how you fall on this spectrum, the distinctions between OLAP cubes and data warehouses are a big deal when making a BI data store decision in the context of financial reporting.
An OLAP Cube is structurally a three-dimensional version of a spreadsheet, in terms of how it organizes your data.  More specifically, OLAP translates to analytical data instead of transactional, and the cube is the storage element of the technology.  OLAP cubes store data in multiple dimension databases.  Plenty financial reporting options depend on OLAP cubes for data access and analysis in report form.  One example: an accountant at your company might like to run a report on financial data by product, by month, or by location – the dimensions of an OLAP cube are comprised of these kind of data elements.  However, OLAP cubes are not as easy-to-use as data warehouses for one main reason.
Because an OLAP cube is not an open SQL server data warehouse, they require a professional with the skill set and experience to manage the database, while SQL server data warehouses can be managed by most professionals with regular database training.  This criteria has a cost associated with it, whether you’re hiring a new employee, maybe full-time, to join payroll in order to help you run your reports, or re-allocating time and energy from a current team member to manage your OLAP cube.  Furthermore, OLAP cubes are typically more rigid and restricted in terms of report design due to their table-like functionality.  Reporting aesthetics and abilities can and arguably should be important for any company who is building their BI toolbox.
On the other hand, Data Warehouses are built with business end user accessibility at the core.  They are organized by subject, which means that they are structured around topics like customer, product, and sales.  Much like an OLAP cube, the data stored in a data warehouse can be utilized for routine, periodical reporting.  And your reporting will be richer and more robust because a data warehouse can host an expansive variety of data types.
Data warehouses are designed so that you can integrate multiple, distinct data sources for a consolidated database, no matter the size of your company.  This is achieved through data cleaning and other integration techniques that are “smart” in nature and are built into the data warehouse technology.  In practice, this means that your company can house personnel information, financial transactions, and any of your other organizational data in one space – moving beyond just numbers and cash flow – for richer analytics in your financial reporting.
Modern data warehouses are accessible and business user friendly.  There are really only two tasks when accessing your data – initially loading or replicating your data to the platform and then, actually accessing or querying that information.  Because data warehouses can stand securely and simply on their own, they do not require any concurrency control mechanisms, recovery, or transaction processing.  And again: their SQL server infrastructure and data warehouse management software make them efficient and easy-to-use.  Data warehouses can be built internally for a significant price tag, related to programming and development time, or you can find commercial, configurable options on the market for a more reasonable cost.  The costs associated with OLAP cubes and data warehouses could fill up its own article.
In conclusion, when looking at financial report writing solutions, pay attention to what data storage option the technology relies on for analyses.  Getting a head start by learning the strengths and weaknesses associated with OLAP cubes and data warehouses can help you figure out your preference, which will narrow your financial reporting tool shopping list down and help you focus on the most effective solution for your company needs.  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 financial reporting and stronger decision-making.