Business Intelligence Tools Database – All businesses operate with data – information generated from various sources internal and external to your company. And these data channels serve as a pair of eyes for executives, providing them with analytical information about what’s happening with the business and markets. Therefore, any misunderstanding, inaccuracy or lack of information can lead to a distorted view of the market situation as well as internal operations – followed by poor decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even those you might not have thought of. But how do you turn unstructured pieces of data into something useful? The answer is business intelligence.
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In this article, we’ll cover the actual steps in bringing business intelligence into your company’s existing infrastructure. You’ll learn how to set up a business intelligence strategy and integrate tools into your company’s workflow. What is business intelligence? Business intelligence or BI is a set of practices of collecting, structuring, and analyzing raw data to turn it into actionable business insights. BI considers methods and tools that transform unstructured data sets, organizing them into easy-to-understand information reports or dashboards. The main goal of BI is to support data-driven decision making.
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Business intelligence processes: How does BI work? The entire business intelligence process can be divided into five main stages.
Business intelligence is a technology-driven process and relies heavily on input. The technologies used in BI to transform unstructured or semi-structured data can also be used for data mining, as well as being front-end tools for working with big data. Business intelligence vs predictive analytics The definition of business intelligence is often confusing because it intersects with other areas of knowledge, esp
. With the help of descriptive and diagnostic analytics – or BI – businesses can study the market conditions of their industry, as well as their internal processes. An overview of historical data helps find pain points and development opportunities.
Based on data processing of past and present events. Instead of producing an overview of historical events, predictive analytics makes predictions about future business trends. It also allows simulation and comparison of scenarios. To make this happen, complex data architectures involving advanced ML techniques must be created by professional data science teams.
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So we can say that predictive analytics can be considered as the next stage of business intelligence. Meanwhile, prescriptive analysis is the fourth most sophisticated type which aims to find solutions to business problems and suggest actions to solve them. Business intelligence architecture: ETL, data warehouse, OLAP and data marts
Is a broad concept that can include organizational aspects (data governance, policies, standards, etc.), but in this article, we will primarily focus on technology infrastructure. Most often, it includes
Now we will examine all the infrastructure elements one by one, but if you want to expand your knowledge of data engineering, check out our article or watch the video below.
First of all, the core element of any BI architecture is the data warehouse. A repository is a database that stores your information in a predefined format, usually structured, classified and cleared of errors.
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However, if your data is not preprocessed, your BI tool or IT department will not be able to query it. For this reason, you cannot connect your data warehouse directly to your information sources. Instead, you should use an ETL tool. ETL ETL (Extract, Transform, Load) or data integration tools will process the raw data from the initial source and send it to the warehouse in three consecutive steps.
Typically, ETL tools are provided together with BI tools from vendors (we will discuss the most popular ones further). Data warehouse Once you have configured data transmission from the selected sources, you must set up the warehouse. In business intelligence, a data warehouse is a specific type of database that typically stores historical information in tabular format. The warehouse is connected to data sources and ETL systems on one side and reporting tools or dashboard interfaces on the other. This makes it possible to present data from multiple systems through a single interface.
However, warehouses typically contain large amounts of information (100GB+), making them slow to respond to queries. In some cases, data can be stored unstructured or semi-structured, leading to high error rates when parsing the data to generate reports. Analytics may require certain types of data to be grouped in one storage space for ease of use. That’s why businesses use additional technology to provide faster access to smaller, more thematic pieces of information.
Recommendation: If you don’t have large amounts of data, using a simple SQL warehouse is sufficient. Additional structural elements such as data marts will cost a lot of money without providing any value. Data warehouse + OLAP cube Data stored in a warehouse has two dimensions, as it is usually depicted in spreadsheet format (tables and rows). The way a warehouse stores data is also called a
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. This may include thousands of data types in a single database, so querying the data warehouse takes a lot of time. To meet analysts’ needs of quickly accessing data, analyzing it from multiple dimensions, and grouping it whenever they need, OLAP cubes are used.
OLAP or online analytical processing is a technology that analyzes and represents data from multiple dimensions simultaneously. Structuring your data in OLAP cubes helps overcome the limitations of data warehouses.
OLAP cube is a data structure optimized for fast analysis of data from SQL databases (warehouses). Cube source data from the data warehouse into a smaller representation. However, the data structure assumes there are more than 2 dimensions (row and column format in a spreadsheet). Dimensions are important elements that make up reports, for example for sales departments
Cubes form a multidimensional database of information that can be customized to group it in different ways and create reports more quickly. Warehouses and OLAP are used together, because cubes store relatively small amounts of data and serve for convenience of processing.
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Recommendation: Data warehouse + OLAP cube architecture can be used by companies of all sizes that require complex multidimensional information analysis. If you don’t want to bombard your warehouse with questions, consider an OLAP architecture approach. Data warehouse technology + data marts The warehouse is the first and largest element of the business intelligence architecture. A smaller representation of a warehouse dataset is a data mart that collects information dedicated to a specific subject area. With the help of data marts, separate departments can access the required data.
Recommendation: Data warehouse + data mart is the second most popular architectural style. This allows constant reporting or easy access to information, without granting permission to the end user. Hybrid architecture Enterprise businesses may require multiple options for data management. Data marts and cubes are different technologies, but both are used to represent smaller pieces of information from a warehouse. Data marts represent a subset of data warehouses that have specific problems, but can be implemented differently. Implementation options include relational databases (warehouse or other SQL databases), and multidimensional, which are essentially OLAP cubes. So, you can use both technologies to manage data and distribute it across organizational departments.
Recommendation: You can take advantage of both technologies because they support the same ideas, but have different goals. Data marts can be implemented as part of a data warehouse for security, data aggregation, or accessibility. Or you can use a data mart as a multiple-dimensional representation of an OLAP cube. But keep in mind that data marts and OLAP cubes will require separate database setup.
Now that we’ve discussed what BI infrastructure includes, let’s discuss how to implement it in your organization. Implementation of business intelligence
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The BI adoption process can be broken down into the introduction of business intelligence as a concept for your company’s employees and the actual integration of tools and applications. Let’s explore the main stages.
Step 1: Introduce business intelligence to your employees and stakeholders To start leveraging business intelligence in your organization, first explain the meaning of BI to all your stakeholders. How you do this will depend on the size of your organization. Mutual understanding is very important here because employees from different departments will be involved in data processing. So, make sure everyone is on the same page and don’t confuse business intelligence with predictive analytics.
Another goal of this phase is to introduce BI concepts to key people involved in data management. You must determine the exact problem you want to address and organize the necessary specialists to launch your business intelligence initiative.
It is important to mention that at this stage, technically, you will be making assumptions about the data sources and the standards set to control the data flow. You will be able to verify your assumptions and define your data workflow at a later stage. That’s why you need to be ready to change your data source channels and team makeup. Step 2: Define goals, KPIs, and requirements The big step after aligning on the vision is defining the problem