Business Intelligence Task Market – All business operates with data – information generated from multiple sources internal and external to your company. And these data channels serve as a pair of eyes for managers, providing analytical information about what is happening with the business and the market. Accordingly, any misunderstanding, inaccuracy, or lack of information may lead to a distorted view of the market situation as well as internal affairs – followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even the ones you don’t think about. 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 will discuss the actual steps of bringing business intelligence into your existing enterprise infrastructure. You will learn how to establish a business intelligence strategy and integrate the tools into your company’s operations. What is business intelligence? Business Intelligence or BI is a set of practices for collecting, structuring and analyzing raw data to transform it into actionable business insights. BI considers methods and tools that transform unstructured data sets, compiling them into easy-to-understand reports or information tables. The main purpose of BI is to support data-driven decision making.
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Business intelligence process: How does BI work? The entire process of business intelligence can be divided into five main stages.
Business agility is a technological process that is heavily input dependent. The techniques used in BI to transform unstructured or semi-structured data can also be used for data mining, and also become the first tools to work with big data. Business Intelligence vs. Predictive Analytics The definition of business intelligence is often complex as it relates to other areas of knowledge, especially
. 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. A look at historical data helps identify pain points and opportunities for improvement.
Based on data exchange of past and present events. Rather than generating historical events, predictive analytics makes predictions about future business trends. It also simulates and compares scenarios. For that to be possible, a complex data architecture based on advanced ML techniques must be created by a professional data science team.
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So we can say that predictive analytics can be considered the next stage of business intelligence. Meanwhile, prescriptive analytics is the fourth, most advanced type that aims to find solutions to business problems and recommend actions to solve them. Business intelligence architecture: ETL, data warehouses, OLAP, and data marts
Is a broad concept that can include an organizational aspect (data management, policies, standards, etc.), but in this article, we will focus mainly on the technological infrastructure. Most of the time, it is included
We will now explore all the infrastructure elements individually, but if you want to expand your knowledge about data engineering, check out our article or watch the video below.
To begin with, the core element of the BI architecture is a data warehouse. A warehouse is a database that stores your information in a pre-defined, generally organized, classified and error-free manner.
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However, if your data is not pre-processed, your BI tool or your IT department will not be able to query it. Therefore, you cannot directly connect your data warehouse to your information sources. Instead, you should use ETL tools. ETL ETL (Extract, Transform, Load) or data integration tools will process the raw data from the initial sources and send it to a warehouse in three consecutive steps.
Typically, ETL tools are provided out of the box with BI tools for vendors (we’ll cover the most popular ones next). Data repository Once you have configured the data transfer from the selected sources, you must set up a repository. In business intelligence, data warehouses are special types of databases that typically store historical information in tabular formats. Warehouses are connected to data sources and ETL systems on the one hand and communication tools or dashboard interfaces on the other. This allows data from different systems to be presented in a single interface.
But a repository usually contains a lot of information (100 GB+), which makes it understandably slow to respond to queries. In some cases, the data can be stored unstructured or semi-structured, which leads to a large error rate when parsing the data to generate a report. Analytics may require some type of data to be aggregated into a single storage location for ease of use. That’s why businesses use additional technologies to quickly access smaller, thematic details of information.
Recommendation: If you don’t have large volumes, using a simple SQL repository is sufficient. Other building blocks like data marts will cost you too much without providing any value. Data warehouse + OLAP cubes The data stored in a warehouse has two dimensions, since it is usually displayed in tabular form (tables and rows). The method of data storage is also called a
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. It may contain thousands of types of data in a single database, so querying a database takes a significant amount of time. OLAP cubes are used to satisfy the needs of analysts to quickly access data, analyze it from different dimensions, and group it when they need it.
OLAP or online analytical processing is a technology that analyzes and represents data from multiple dimensions simultaneously. Structuring your data in OLAP cubes helps you overcome the limitations of a data warehouse.
An OLAP cube is a data structure optimized for fast analysis of data from SQL databases (warehouses). Cubes retrieve data from a data warehouse that is a smaller representation of it. However, the data structure assumes that there are more than 2 dimensions (row and column format of spread sheets). Dimensions are important elements that make up the report, for example, for the sales department it might be
Cubes create a multidimensional database of information that can be adapted to group in different ways and generate reports faster. A warehouse and OLAP are used together, because the cube stores a smaller amount of data and serves for processing convenience.
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Recommendation: The data warehouse + OLAP cube architecture can be used by companies of any size that require a complex multidimensional analysis of information. If you don’t want to bombard your database with queries, consider an OLAP architecture approach. Data warehouse + data mart technologies The warehouse is the first and largest component of the business intelligence architecture. A smaller representation of database transactions is a database that collects information dedicated to a specific subject area. With the help of data marts, different departments can access the necessary data.
Recommendation: Data warehouse + data marts is the second most popular architectural style. It allows the establishment of continuous reporting or easy access to information, without providing permission to end users. Hybrid Architecture Enterprise businesses may require multiple options for data management. Data marts and cubes are different technologies, but they are both used to represent smaller chunks of information from a warehouse. Transactional data represent a problem-specific part of a data warehouse, but they can be implemented differently. The implementation option includes relational databases (repository or other SQL database) and multidimensional, which are basically OLAP cubes. So you can use both technologies to manage your data and distribute it across the organization’s departments.
Suggestion: You can use both technologies because they support the same idea, but serve different purposes. Data marts can be implemented as part of a data warehouse for security, data collection, or access. Or you can use data marts to represent multiple dimensions of an OLAP cube. But remember that both data marts and OLAP cubes will require different database setups.
Now that we’ve covered what BI infrastructure consists of, let’s finally talk about how to implement it in your organization. Implementation of business intelligence
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The BI adoption process can be divided into introducing 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 using business intelligence in your organization, first of all explain what BI means to all your stakeholders. How you proceed will depend on the size of your organization. Mutual understanding is important here as 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 communicate the concept of BI to key people involved in data management. You have to define the exact problem you want to work on and organize the necessary expertise to launch your business intelligence initiative.
It is important to mention that at this stage, you will, technically, make assumptions about the data sources and the standards set to control the data flow. You will be able to correct your assumptions and define your data processing in later stages. So you must be willing to change your data source channels and your team lineup. Step 2: Define your goals, KPIs, and needs.
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