Organizations today require quick access to more information for better decision-making to improve efficiency and increase productivity, resulting in reduced costs and improved revenue. This information must be secure, real-time, shared across different departments, and presented in different ways to support the various needs of management. Keep reading to learn how online analytical processing can offer stronger insights and better business intelligence opportunities to support growth and profitability.
What is Business Intelligence?
Business intelligence (BI) is the process of extracting accumulated raw data through various software applications, allowing a company to analyze and learn important information to help improve processes and performance.
BI systems provide historical, current, and predictive views of business operations with information gathered in a data warehouse or a data mart (occasionally working from operational data). Applications tackle sales, production, financial, and many other sources of business data for purposes that include business performance management.
For real insights, it is important to extract accurate information from the data. Your information must be clean and complete to guide vital business decisions. In today’s fast-paced digital world, this information is crucial to improve processes and workflows, better serve clients, and gain a competitive advantage.
Acquiring good business intelligence takes time and effort – but the results can have long-term and lasting benefits. To get BI data, you need to use established systems for gathering and processing information.
The Difference Between OLTP and OLAP
BI systems operate largely on data acquired automatically through a front-end “source” system, which could be a standalone application, web application, cloud-based portal, IT product, or mobile application.
The source system feeds real-time data into an OLTP (Online Transaction Processing) Database. Separately, the OLAP (Online Analytical Processing) Component is a computing method that enables users to easily and selectively extract and query data in order to analyze it from different points of view.
Migrate Your OLTP Data to Online Analytical Processing (OLAP)
OLTP data continually grows as more users access source systems, which means you need a separate system for data analysis. Typically, it’s not best practice to build OLAP systems on top of a real-time database. Trying to run both systems in the same space increases the risk of degrading the performance of critical real-time applications.
ETL (Extract, Transform and Load) algorithms provide processes for data warehousing/replication and are responsible for pulling data out of the source systems and placing it into a data warehouse. ETL systems are utilized to migrate your OLTP data for online analytical processing — which is the first step for converting data into useful information.
Usually, data migration happens outside of business hours or when minimum user impact will occur. Incremental data load approaches are used to migrate the data from the OLTP system to OLAP.
Benefits of a Separate Online Analytical Processing System
Parallel and distributed processing platforms provide a higher computational and storage capacity. Companies can ingest real-time streaming data from multiple sources and easily combine it with business process data. A combination of online transaction processing (OLTP) and online analytical processing (OLAP) is a must for managing the streaming data in real time
Reports can be built in real-time over OLTP system by calculating the data against any dimension. But this decreases the performance of the system because the user who is extracting data must wait until calculations are complete. These calculations impact the end user accessing the data negatively.
Providing a separate system for online analytical processing (OLAP) gives major advantages to your business. The measures are pre-calculated and stored in the cube, so any information can be quickly extracted at any time without impacting the real-time system.
Other Key Aspects of BI
Data is the key for success for any business. Data accumulates rapidly day by day and is always “raw.” For it to be useful information, data must be furbished, extracted, and converted in a strategic manner.
Graphical Reporting and Dashboard
The BI system also supports graphical tools and graphical representation of the data. Visual illustrations of data helps management quickly pinpoint trends and needs.
To achieve visual reports, you can represent data with pie charts, bar graphs, histograms, comparisons, and more. For example, represent sales figures for the last 10 with a bar diagram or as a growth line to compare seasonal shifts and look at long-term growth trajectory. Or, use a comparison report for sales over profit to identify overhead expenses.
Furthermore, make your data engaging. Integrate your visual data with dashboard tools that provide interactive click and drill-down features.
Database Facts and Dimensions
Facts and dimensions in a database form the core of any business intelligence effort. These tables contain the basic data used to conduct detailed analyses and derive business value.
Dimensions are core items containing data calculations. For example, territory-related dimensions include information on Country, State, and City, while time-related dimensions include Name, Day, Month, Year, and Time Zone. Similarly, Business Product, Client Segment, Sales Segment, and Zones are business-specific dimensions.
Facts are the calculations made against any dimension. This includes Total Sales, Total Customers, and Total Profit.
A business intelligence system extrapolates your fact against one or multiple dimensions, such as, “Sales against Year”, “Customer against City.” The system also allows multiple dimensions to be used for calculations, such as “Yearly Sales against Country, State, City.” In this case, Year and State both are dimensions, so, you can compare yearly sales as well as by region.
Enterprise Business Intelligence
Siloed data can cause problems for enterprise businesses. Data and content aren’t separate aspects of information management, and they require an integrated approach for large companies.
Enterprise information management brings business intelligence and enterprise content management together.
Traditionally, business intelligence vendors only targeted top the pyramid. However, a focus on self-service BI means a paradigm shift that makes BI accessible at the bottom of the pyramid. Today’s organizations are pushing operational business intelligence — a market under-served and largely uncontested by vendors.
Improve BI and Online Analytical Processing With Xperity
Business intelligence systems give extensive, multi-dimensional support for executives, business managers and other operational workers to make critical business decisions. BI provides this information across all enterprise levels with online analytical processing. Utilizing BI makes it possible to improve operational visibility and business management.
Ready to improve business intelligence?
Tap in the industry’s most complete software development and IT bench of experts. Xperity provides an arsenal of analytical, design and implementation skill sets to support BI development.
Reach out to learn more about how our team can help you establish OLAP for better business insights.
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