Business Intelligence Architecture- What it Is & Why You Need It
Companies and organizations are often confronted with difficult business decisions. Owners can base their decisions on instinct, or they can utilize reliable data that provides a concrete foundation for their decision-making.
The manner in which data is organized and stored can affect the way in which business insights are formed, and how decisions are made. Proper data storage and management allows owners and stakeholders to access valuable information and share it with other decision-makers. Business intelligence architecture is a data management and analytics tool that is frequently utilized by organizations for this purpose. Here is an in-depth look into understanding BI architecture, as well as why growing businesses need it.
What is Business Intelligence Architecture?
Business intelligence refers to the strategies and technologies that organizations use in order to analyze and manage data. In relation, the architecture is the framework that organizes that data and technology that is utilized in business intelligence systems. Sustainable architecture depends on 3 components, which include-
Data Collection Streams
This refers to the methods of collecting data within an organization. Sustainable architecture recognizes which data is important for each company department, and where the data originated. This is important because the quality is how reliable insights are made.
This includes the integration of data and how it is maintained within the business intelligence architecture. Sustainable architecture can hold and manage multiple data sources. Users should also be able to quickly extrapolate data so insights can be made.
Owners and department managers utilize data to see key performance indicators and other various trends within the organization. This allows decision-makers to make better choices based on their own insight into these trends.
Why Do We Need Business Intelligence Architecture?
There are a variety of reasons why an organization might need business intelligence architecture. Some indicators are-
If an IT department is overwhelmed by report requests from various users within the organization, then a business intelligence system might be necessary. BI systems can help users resolve many issues on their own, so the IT department isn't solely responsible for resolving each task at hand.
Poor IT Systems
Complex IT systems that include silos of data and various data formats make it difficult to fulfill report requests. BI architecture can help alleviate this by consolidating the data into one system that is uniformly formatted.
It can be expensive to maintain all of the necessary IT resources for multiple information systems, or silos. When each department has a different management system, there can be a lack of synergy and efficiency when completing a project. Contrasting systems can lead to a bottleneck, or too much work for each department to handle. This can increase company expenses and waste valuable time and resources.
Components of Business Intelligence Architecture
BI architecture has several key components that make it functional. Some of these include-
Data should first be organized within a source system or an operational system. These systems are utilized in order to process day-to-day transactions within the company. While the majority of data comes from operational systems, some can come from other sources, such as benchmarking data or market data. If data is not captured in the operational system, it cannot be analyzed later.
After the data is organized within the operational systems, it is extracted and put into the data warehouse. This mechanism is referred to as Extract Transform Load, or ETL. Some ETL technologies include IBM Websphere Data Stage, Oracle Data Integrator, or SQL Server Integration Services.
Data modeling is a process that extracts necessary data from the data sources, chooses the format it will be in, and then manages its relation to other data within the operating system. Extracting all of the data from a source system can be expensive because data has to be duplicated and saved in case the backup system fails. Data modeling only extracts relevant data and organizes it, which minimizes the cost of data replication and storage.
Enterprise Information Management
Enterprise information management systems, or EIM, encompass many types of data management tools. Some of these include data profiling, or collecting statistics on data within the data source, and metadata management, or managing the way in which data relates to other data. Data modeling is also an enterprise information management tool.
Business Intelligence Hardware
Companies should make careful decisions pertaining to the BI hardware that houses the architecture. Reliable data warehouse appliances combine the server, database, and storage into one, secure system. Owners should research the durability and performance capability of different systems before making any purchasing decisions.
How Can I Use Business Intelligence Architecture?
Organizations should first carefully map out their data collection and analytics strategies before utilizing business intelligence architecture. Some elements to consider include what the data sources are, what type of operational system is utilized, and how much codified data to expect from each department.
Organizations should also consider what they achieve from data analysis within each department. For example, sales data can tell the sales manager how employees are performing on a day-to-day basis. Analyzing the sales data over a few months' time span can provide a framework for understanding how each employee is contributing to the organization's overall sales goals.
Business intelligence architecture should then encompass the data sources and components that will allow for reliable sales data analysis. After the data sources are chosen, organizations can determine which data is the most valuable and how to securely store it. Poorly stored and sorted data can affect the quality of the overallbusiness intelligence architecture because the system will be unable to determine which data is necessary to extract. Finally, organizations should choose the proper data analytics tools, or tools that show insight into the extracted data. High performing data analytics tools allow organizations to quickly understand data in order to gain valuable insights.
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