Tools For Agile Development In Business Intelligence Software – Every business is run on data – information that comes from many sources inside and outside your company. And these information acts as a pair of eyes for the management, to give them clarity on what is happening in the business and the market. Accordingly, any misconception, inaccuracy or lack of information can lead to a false picture of the market situation as well as actions in it – followed by bad decisions.
Making data-driven decisions requires a 360° view of all aspects of your business, even areas you may not have considered. But how do you turn unstructured data into something useful? The answer is business savvy.
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In this article, we will discuss the exact steps for bringing business intelligence to your current business development. You will learn how to set up a smart business plan and integrate the tools into the work of your company. What is business intelligence? Business intelligence or BI is a set of methods for collecting, organizing and analyzing raw data to transform it into business intelligence. BI explores methods and tools that transform unstructured data, compiled into easy-to-use information or lists of information. The primary purpose of BI is to support strategic decision making.
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Business Process Analysis: How does BI work? The entire process of business intelligence can be divided into five main areas.
Business intelligence is a technological and strategic approach. Technologies used in BI to transform unstructured or semi-structured information can also be used for data manipulation, as well as tools used for working with big data. Business Intelligence vs Predictive Analytics The definition of business intelligence is often confused when it is related to other areas of knowledge, especially
. With the help of data analysis and analysis – or BI – businesses can analyze the market conditions of their business as well as their internal processes. A summary of historical data to help identify pain points and growth opportunities.
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Based on historical and current historical data. Rather than taking a general view of historical events, forecasting makes predictions about future business practices. It also allows you to test and compare displays. To accomplish this, a professional information science group must be developed.
So we can say that technical information can be considered as the next step of business intelligence. Currently, research studies are the fourth, more advanced type that aims to find solutions to business problems and recommend actions to solve them. Business model: ETL, data warehouse, OLAP and data warehouse
A general idea that can include the organizational part (data management, policies, standards, etc.), but in this article, we will mainly focus on technological developments. Most of the time, it includes
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Now we will look at all aspects of individual development, but if you want to expand your knowledge of information engineering, check out our article or watch the video below.
In the beginning, the main element of any BI architecture is a warehouse. A repository is a database that stores your information in a structured, organized, organized, and error-free manner.
However, if your data has not been processed before, neither your BI tool nor the IT department will be able to query it. For this reason, you cannot connect your data warehouse directly to your data sources. But, you have to use ETL tools. ETL ETL (Extract, Transform, Load) or data integration tools will first process raw data from primary sources and send it to a warehouse in three sequential steps.
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Usually, ETL tools are provided out of the box and BI tools from vendors (we will cover the most popular ones below). Databases After collecting data from the selected sources, you must create a repository. In business knowledge, warehouses are specific types of data that usually store historical information in chronological order. Data warehouses are connected to data processing and ETL processes on one end and reporting tools or dashboard connections on the other. This makes it possible to provide information from different systems through a single application.
However, a warehouse often contains a lot of data (100GB+), which is understandable for the delay in responding to queries. In some cases, the data can be stored unstructured or semi-structured, which leads to a high level of error when analyzing the data to create a report. Analysts may require some form of data to be integrated into a repository for ease of use. That’s why businesses are using additional technologies to provide quick access to smaller, more basic information.
Tip: If you don’t have a lot of data, using a simple SQL database is enough. Additional building blocks such as sales information will be very expensive without providing any value. Data Warehouse + OLAP Cubes The data stored in a warehouse is two-dimensional, because it is usually represented in the format of the distribution (trees and rows). The way a data warehouse stores data is also called a
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. There can be thousands of types of data in a single database, so finding a warehouse requires a lot of time. To meet the needs of researchers to quickly access data, search from different angles and combine whenever needed, using OLAP cubes.
OLAP or Online Analytical Processing is a technology that analyzes and compares data from multiple sources simultaneously. Organizing your data in OLAP cubes helps overcome the limitations of a warehouse.
The OLAP cube is a data structure that is ideal for quick analysis of data from SQL databases (warehouses). Cubes retrieve information from a data warehouse as a small representation. However, the data structure is considered to be more than 2 dimensions (row and column). Measurements are the key elements that make up the report, eg. for the sales department may be
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Cubes create a highly organized database of data that can be sorted to be sorted in a variety of ways and to generate reports quickly. A warehouse and OLAP are used together, as containers to store a small amount of data and services for ease of operation.
Recommendation: The data warehouse + OLAP cubes can be used by companies of all sizes that require highly specialized data analysis. If you don’t want to bombard your warehouse with queries, consider an OLAP modeling approach. Data warehouse technology + data mart The warehouse is the first and most important element of business intelligence. A small example of a data warehouse is a database that collects information specific to a specific field. With the help of data marts, different departments can get required information.
Recommendation: Data warehouse + data marts is the second most popular architecture style. It allows the creation of continuous reports or easy access to information, without giving permission to the end user. Business integration may require multiple options for data management. Data marts and cubes are different technologies, but both are used to represent small data from the warehouse. Data encryption represents part of a particular problem of a data warehouse, but can be implemented differently. The choice of implementation includes relational data (warehouse or any other SQL database) and multidimensional, which are usually OLAP cubes. So you can use two technologies to manage your information and distribute it to the departments of the organization.
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Tip: You can use two technologies as they support the same concept but have different purposes. Data acquisition can be performed as part of a data warehouse for security, data collection, or usage. Or you can use the database as a multidimensional representation of the OLAP cube. But keep in mind that sales data and OLAP cubes will require different databases.
Now that we’ve covered what goes into a BI infrastructure, let’s talk about how to implement it in your organization. Use business intelligence
The BI adoption process can be broken down by incorporating business intelligence as a concept into your company’s workforce and the actual integration of tools and applications. Let’s examine the main points.
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Step 1: Introduce business intelligence to your employees and partners To start using business intelligence in your organization, first explain the concept of BI to all your stakeholders. How you do it will depend on the size of your organization. Understanding is important here because employees of different departments will be involved in the data processing. So make sure everyone is on the same page and don’t confuse business intelligence with technical analysis.
Another purpose of this section is to introduce the concept of BI to the main people involved in the management of information. You need to define the real problem you want to work on and recruit the necessary experts to start your smart business.
It is important to mention that in this step, in particular, you will make some comments about the information and models that are designed to control the flow. the data. You will be able to prove your ideas and secure your work data in later stages. That’s why you should be ready to change your information supply channels and team structure. Step 2: Defining Goals, KPIs and Requirements The biggest step after using the vision is defining the problem.
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