{"id":168232,"date":"2023-05-19T16:37:39","date_gmt":"2023-05-19T15:37:39","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=168232"},"modified":"2023-10-30T19:06:18","modified_gmt":"2023-10-30T18:06:18","slug":"introduction-to-business-intelligence","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/introduction-to-business-intelligence","title":{"rendered":"Business Intelligence: Optimizing performance with a global view"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"168232\" class=\"elementor elementor-168232\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-be1b8f4 e-flex e-con-boxed e-con\" data-id=\"be1b8f4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-345af1f elementor-widget elementor-widget-heading\" data-id=\"345af1f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Business Intelligence (BI) allows all the actors of a company to have a global vision of their activities to help them in their decision-making.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2196881 elementor-widget elementor-widget-text-editor\" data-id=\"2196881\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Thanks to Business Intelligence tools, it is possible to create analyses on historical, current, or even predictive data. With the use of Big Data and data science, BI can handle a very large volume of data, quickly and complexly. The decisions that result from data analysis can be both operational and strategic.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f618bd4 e-flex e-con-boxed e-con\" data-id=\"f618bd4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-48f2fb0 elementor-widget elementor-widget-heading\" data-id=\"48f2fb0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How Business Intelligence could be implemented ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ee21a9b elementor-widget elementor-widget-text-editor\" data-id=\"ee21a9b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">We will detail the four different steps essential to BI, what are they?\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6af67cb elementor-widget elementor-widget-heading\" data-id=\"6af67cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data source\n<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-754ec4f elementor-widget elementor-widget-text-editor\" data-id=\"754ec4f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">A company&#8217;s data is spread over several information systems, <\/span><b>CRM<\/b><span style=\"font-weight: 400;\"> (Customer Relationship Management), <\/span><b>ERP<\/b><span style=\"font-weight: 400;\"> (Enterprise Resource Planning), <\/span><a href=\"https:\/\/datascientest.com\/en\/database-what-is-it\"><span style=\"font-weight: 400;\">databases<\/span><\/a><span style=\"font-weight: 400;\">, flat files, etc. Thanks to BI, it is possible to extract and consolidate this data to render it through dashboards and allow users to perform detailed analyses. To <\/span><b>deepen the analysis<\/b><span style=\"font-weight: 400;\"> even further, it is possible to consolidate both internal and external data such as social networks or Gartner for example.<\/span><\/p><p><span style=\"font-weight: 400;\">Although all this <\/span><b>data is available and accessible<\/b><span style=\"font-weight: 400;\">, it must be retrieved, transformed, cleaned and arranged, and stored. And it is with the help of an<\/span><b> ETL<\/b><span style=\"font-weight: 400;\"> (we will see below how it works) that we can achieve this. All the data could be <\/span><b>recovered and stored<\/b><span style=\"font-weight: 400;\">. Ideally, identifying the data that is essential to the expressed need will reduce costs and improve overall performance.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e7922c9 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"e7922c9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"91\" height=\"302\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/DATA-SOURCE.png\" class=\"attachment-large size-large wp-image-168237\" alt=\"DATA-SOURCE\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/DATA-SOURCE.png 91w, https:\/\/datascientest.com\/en\/files\/2023\/05\/DATA-SOURCE-90x300.png 90w\" sizes=\"(max-width: 91px) 100vw, 91px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e3cf7d4 elementor-widget elementor-widget-heading\" data-id=\"e3cf7d4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">ETL (Extract Transform Load)\n<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b914c2d elementor-widget elementor-widget-text-editor\" data-id=\"b914c2d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The objective of an <\/span><b>ETL for Extract-Transform-Load<\/b><span style=\"font-weight: 400;\"> is to recover data from different sources as input and to have clean data as output, exploitable by<\/span><b> different analytical functions<\/b><span style=\"font-weight: 400;\">. For this, we distinguish three steps.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extraction:<\/b><span style=\"font-weight: 400;\"> the data sources of a company are multiple, it is necessary for the ETL to have many connectors to recover the interesting data. The ETL will connect to the different sources to extract the data or, depending on the system, wait for the data to be sent.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Transformation<\/b><span style=\"font-weight: 400;\">:\u00a0 this step is essential because it allows to clean, sort, organize the incoming data according to the needs. During the transformation, several rules apply such as standardization, deduplication or verification.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Loading<\/b><span style=\"font-weight: 400;\">: the last step in the process, which consists of loading the extracted and transformed data into the data warehouse.<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">This entire process is generally automated. ETL takes into account exception handling.<\/span><\/p><p><span style=\"font-weight: 400;\">This step in <\/span><b>Business Intelligence<\/b><span style=\"font-weight: 400;\"> is of paramount importance. The correct implementation of an ETL allows you to<\/span><b> obtain<\/b> <b>qualitative<\/b><span style=\"font-weight: 400;\">, <\/span><b>exploitable<\/b><span style=\"font-weight: 400;\"> and <\/span><b>analyzable data<\/b><span style=\"font-weight: 400;\">. With the <\/span><b>reporting tools<\/b><span style=\"font-weight: 400;\"> on the market, we will see that this step can be integrated directly into these tools. However, it is important to keep in mind that it is not optimal to work this way. For more robust and efficient processes, the use of an <\/span><b>ETL is highly recommended<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dbe4a10 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"dbe4a10\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start a training in Data Science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ac39e88 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"ac39e88\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"220\" height=\"302\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_2.png\" class=\"attachment-large size-large wp-image-168238\" alt=\"datasource_2\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_2.png 220w, https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_2-219x300.png 219w\" sizes=\"(max-width: 220px) 100vw, 220px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58e2108 elementor-widget elementor-widget-text-editor\" data-id=\"58e2108\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Note: When very large volumes of data are processed using <\/span><b>Big Data solutions<\/b><span style=\"font-weight: 400;\">, instead of an ETL, we use ELT (Extract &#8211; Load &#8211; Transform). In this case, all data is retrieved, stored without processing, usually in a data lake, and then transformed according to the needs.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-001f404 elementor-widget elementor-widget-heading\" data-id=\"001f404\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data Warehouse<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0533d72 elementor-widget elementor-widget-text-editor\" data-id=\"0533d72\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">We have seen what data can be retrieved and how it is done. The <\/span><b>next step is to store this data<\/b><span style=\"font-weight: 400;\">, generally using <\/span><a href=\"https:\/\/datascientest.com\/en\/data-warehouse-2\"><span style=\"font-weight: 400;\">data warehouses<\/span><\/a><span style=\"font-weight: 400;\">. This is a set of databases that contains some or all of the functional data of a company. Thanks to the work done with ETL, the data is<\/span><b> already well-structured and clean<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Data warehouses allow for the storage of important data, but not only. These warehouses are also used for <\/span><b>data historicization<\/b><span style=\"font-weight: 400;\">. This way, no data is deleted or replaced, but historized over time. This makes it possible to build analyses with a follow-up over time of certain indicators. We have seen that <\/span><b>the ETL performs a transformation of the data<\/b><span style=\"font-weight: 400;\"> before loading them. It is also possible to store the raw data in addition to the transformed data. They can be used for <\/span><b>future needs<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">We will focus on the part that allows us to analyze the data. For this, there are good practices and rules to follow to have a <\/span><b>coherent and efficient data structure<\/b><span style=\"font-weight: 400;\"> in a BI context. The most common data model is the star schema. In this schema, we have 2 types of tables:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Fact tables, contain measures such as KPI, price, and quantity;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dimension tables, contain business concepts such as customer, plant, and time.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72a8257 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"72a8257\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"351\" height=\"302\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_3.png\" class=\"attachment-large size-large wp-image-168240\" alt=\"datasource_3\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_3.png 351w, https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_3-300x258.png 300w\" sizes=\"(max-width: 351px) 100vw, 351px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b1a3514 elementor-widget elementor-widget-text-editor\" data-id=\"b1a3514\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Now we have <\/span><b>retrieved the data<\/b><span style=\"font-weight: 400;\"> from different sources, transformed it, and stored it in the Data Warehouse, which has been structured in such a way as to simplify the construction of our analyses. The last step was to create dashboards with the help of reporting tools.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e57955 elementor-widget elementor-widget-heading\" data-id=\"5e57955\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Reporting<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6db7272 elementor-widget elementor-widget-text-editor\" data-id=\"6db7272\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The objective of these tools is to tell a story with the data we have retrieved. A grid with raw data is very <\/span><b>often difficult to analyze and not efficient.<\/b><span style=\"font-weight: 400;\"> This is why the use of graphs, depending on the type of data and the objective, is essential.<\/span><\/p><p><span style=\"font-weight: 400;\">These tools are generally broken down into 4 parts:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Retrieval of data stored<\/b><span style=\"font-weight: 400;\"> in the data warehouse;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Logical modeling<\/b><span style=\"font-weight: 400;\"> of the data;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Construction of <\/span><b>dashboards<\/b><span style=\"font-weight: 400;\">;<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sharing and distribution.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa79c2c elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"fa79c2c\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"253\" src=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_4.png\" class=\"attachment-large size-large wp-image-168239\" alt=\"datasource_4\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_4.png 512w, https:\/\/datascientest.com\/en\/files\/2023\/05\/datasource_4-300x148.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3336cd2 e-flex e-con-boxed e-con\" data-id=\"3336cd2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0860b8e elementor-widget elementor-widget-heading\" data-id=\"0860b8e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Uses of Business Intelligence<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2cfd9d elementor-widget elementor-widget-text-editor\" data-id=\"d2cfd9d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Many different industries have embraced BI earlier than usual, including healthcare, information technology, and education. All organizations can use data to <\/span><b>transform their operations<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Financial services companies are already using BI to get a complete view of all their branches to measure performance and identify opportunities. Access to a <\/span><b>central business intelligence platform<\/b><span style=\"font-weight: 400;\"> has enabled some of them to bring all of their branch data together in a single dashboard.<\/span><\/p><p><span style=\"font-weight: 400;\">Decision-makers at these companies can now <\/span><b>identify customers<\/b><span style=\"font-weight: 400;\"> whose investment needs might change. They can also see whether a region is performing above or below average and which branches are driving performance in that region. The result is <\/span><b>more optimization opportunities<\/b><span style=\"font-weight: 400;\"> and better service for customers.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7467945 e-flex e-con-boxed e-con\" data-id=\"7467945\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1362cdd elementor-widget elementor-widget-heading\" data-id=\"1362cdd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The Future of Business Intelligence<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae3b759 elementor-widget elementor-widget-text-editor\" data-id=\"ae3b759\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The intelligence industry is <\/span><b>constantly evolving<\/b><span style=\"font-weight: 400;\"> in response to business needs and technology. It is also clear that<\/span><a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-definition\"><span style=\"font-weight: 400;\"> artificial intelligence<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><span style=\"font-weight: 400;\">machine learning<\/span><\/a><span style=\"font-weight: 400;\"> will continue to grow and that companies can incorporate AI insights into their BI strategy. As they strive to become more <\/span><b>Big Data oriented<\/b><span style=\"font-weight: 400;\">, there will be more data sharing and collaboration. <\/span><a href=\"https:\/\/datascientest.com\/en\/dataviz-definition-objectives-and-uses\"><span style=\"font-weight: 400;\">Data visualization<\/span><\/a><span style=\"font-weight: 400;\"> will be even more essential for working together in teams and across departments.<\/span><\/p><p><span style=\"font-weight: 400;\">BI offers near <\/span><b>real-time sales tracking<\/b><span style=\"font-weight: 400;\"> capabilities and allows companies to discover insights into customer behavior, make revenue forecasts, etc. <\/span><b>Various industries<\/b><span style=\"font-weight: 400;\"> such as retail, insurance, and oil have adopted BI. More and more are joining every year. BI platforms are adapting to <\/span><b>new technologies<\/b><span style=\"font-weight: 400;\"> and <\/span><b>user innovation<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">We have seen all the key steps to build a successful process that respects the <\/span><b>fundamental principles of Business Intelligence<\/b><span style=\"font-weight: 400;\">. From data sources to sharing and disseminating dashboards, we have covered the entire BI process.<\/span><\/p><p><span style=\"font-weight: 400;\">BI is a cross-functional domain that links data to the business. By relying on numerous technologies related to <\/span><b>Data Science<\/b><span style=\"font-weight: 400;\">, <\/span><b>Data Engineering<\/b><span style=\"font-weight: 400;\">, and <\/span><b>Data Analysis<\/b><span style=\"font-weight: 400;\">, it is possible to bring a lot of information to all the businesses of a company. If you want to know more about Business Intelligence, we have developed a <\/span><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\"><span style=\"font-weight: 400;\">Data Analyst training<\/span><\/a><span style=\"font-weight: 400;\"> on this subject.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-348140a elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"348140a\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our Data Analyst training<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3681135 e-flex e-con-boxed e-con\" data-id=\"3681135\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-92bc1fb elementor-widget elementor-widget-html\" data-id=\"92bc1fb\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Uses of Business Intelligence\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Many different industries have embraced BI earlier than usual, including healthcare, information technology, and education. 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With the use of Big Data and data science, BI can handle a [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":168233,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2420],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Business Intelligence: Optimizing performance with a global view<\/title>\n<meta name=\"description\" content=\"Business Intelligence (BI) allows all the actors of a company to have a global vision of their activities to help them in their decision-making.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/introduction-to-business-intelligence\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Business Intelligence: Optimizing performance with a global view\" \/>\n<meta property=\"og:description\" content=\"Business Intelligence (BI) allows all the actors of a company to have a global vision of their activities to help them in their decision-making. 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