Business intelligence architecture is a term used to describe standards and policies for organizing data with the help of computer-based techniques and technologies that create business intelligence systems used for online data visualization , reporting, and analysis. One of the BI architecture components is data warehousing. Organizing, storing, cleaning, and extraction of the data must be carried by a central repository system, namely data warehouse, that is considered as the fundamental component of business intelligence.
But how exactly are they connected? There are various components and layers that business intelligence architecture consists of. Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. We can see in our BI architecture diagram how the process flows through various layers, and now we will focus on each. The first step in creating a stable architecture starts in gathering data from various data sources such as CRM, ERP, databases, files or APIs, depending on the requirements and resources of a company.
Modern BI tools offer a lot of different, fast and easy data connectors to make this process smooth and easy by using smart ETL engines in the background. They enable communication between scattered departments and systems that would otherwise stay disparate.
From a business point of view, this is a crucial element in creating a successful data-driven decision culture that can eliminate errors, increase productivity, and streamline operations. You have to collect data in order to be able to manipulate with it. When data is collected through scattered systems, the next step continues in extracting data and loading it to a data warehouse.
With an increasing amount of data generated today and the overload on IT departments and professionals, ETL as a service comes as a natural answer to solve complex data requests in various industries. Secondly, data is conformed to the demanded standard. In other words, this transform step ensures data is clean and prepared to the final stage: loading into a data warehouse.
Now we approach the data warehousing and business intelligence concepts. While both terms are often used interchangeably, there are certain differences that we will focus on to get a more clear picture on this topic.
The main differences, as we can also see in the visual, between business intelligence and data warehousing are indicated in these main questions:. Business intelligence and data warehousing have different goals. We perform in-depth study of existing Data Warehouse architecture and recommend our strategy and roadmap for significant improvements and opportunities as per your business needs.
We easily integrate the raw information from various data sources and map data elements to develop a Data warehouse. We deliver a complete data solution to park huge amount of data into a well-defined data storage for decision support and data analysis. Right from network quarantines to encryptions, we ensure the data warehouse is equipped with the best-in-class security features to protect your data. These are so mistakenly used that even people who are working in this domain also not sure what to use and when to.
BI is basically a Business Intelligence system which tells you what happened, or is happening right now in your business — it describes the situation to you. Not only that, a good BI platform describes this to you in real time in a granular, accurate and presentable form. But on what basis it is able to do so, what is the source. How can it help me in taking a strategic decision? Data which is accumulated over a large amount of time from several disparate sources. But now a very basic question arises where this data is.
And BI systems make use of Data Warehouse data and lets you apply chosen metrics to potentially huge, unstructured data sets, and covers querying, data mining , online analytical processing OLAP , and reporting as well as business performance monitoring, predictive and prescriptive analytics.
I would like to put more light on this as nowadays for Analytics we are moving towards Big Data Ecosystem to handle a large amount of data, but yes anyway, we are moving towards Enterprise Data Hub with distributed system and Map Reduce processing or in-memory execution engine like Spark.
Now I hope it has made a clear distinction between Business Intelligence and Data Warehouse and let me know your thoughts using the comment section. This has been a guide to Business Intelligence vs Data Warehouse.
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