{"id":8563,"date":"2022-04-05T06:29:44","date_gmt":"2022-04-05T04:29:44","guid":{"rendered":"https:\/\/www.sqorus.com\/big-data-and-bi-from-predictive-to-prescriptive-analysis\/"},"modified":"2024-08-21T11:14:06","modified_gmt":"2024-08-21T09:14:06","slug":"big-data-and-bi-from-predictive-to-prescriptive-analysis","status":"publish","type":"post","link":"https:\/\/www.sqorus.com\/en\/big-data-and-bi-from-predictive-to-prescriptive-analysis\/","title":{"rendered":"Big data and BI: from predictive to prescriptive analysis"},"content":{"rendered":"

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Predictive, prescriptive and machine learning are now considered important aspects of business intelligence (BI). Those who succeed in developing these technologies first will have a decisive competitive advantage in their own market, especially thanks to a large volume of data analyzed in real time. This article provides an overview of the different forms of analysis in the era of big data and social networks so that you can take into consideration their challenges. From predictive to prescriptive analysis, let’s take a closer look at these developments. You will find this theme in our technological innovation file.<\/p>\n

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Data mining: from data lake to big data<\/h2>\n

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One of the biggest problems facing any business is the increasing amount of information being stored. Data is everywhere! And they multiply at the speed of light. This is where organization and knowledge management come in. Before any form of analysis can be undertaken, data sets must be retrieved from all systems.<\/p>\n

This raw information is initially stored in a data lake<\/a><\/strong>. Whether they are structured or not, they are not organized for further analysis. Companies can retrieve the data whenever they want. Only then do they organize them and create a structure that facilitates analysis.<\/p>\n

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From big data to data analysis for decision support<\/h2>\n

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There are many technologies that take the form of algorithms and offer the opportunity to manage large amounts of data and create models that allow for predictive analysis<\/strong>. In other words, once the information has been collected, it can be visualized from dashboards<\/strong> and processed.<\/p>\n

It is possible to produce reports, graphs and analyses or to use all the information to find the answer to strategic questions. More generally, these aspects are referred to as data science<\/strong><\/a> “. The data scientist can discover why employees tend to leave the company, for example. Obtaining an answer to the problem helps to anticipate future departures and avoid them. The prescriptive analysis <\/strong>corresponds to the analysis and understanding of the reasons and causes that led to the occurrence of certain events. It also aims to recommend actions for the benefit of the forecast.<\/p>\n

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The different forms of analysis and the current difficulties<\/h2>\n

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If we were to compare the different forms of analysis and talk about a pyramid of uses, we could consider that descriptive analysis allows us to know what is happening in practice. Analytics aims to answer the question “why?”, while predictive and prescriptive analyses offer the opportunity to know what’s going to happen and what actions can be taken.<\/p>\n

Finally, companies currently wishing to exploit prescriptive analytics<\/strong> face certain legal constraints, such as the General Data Protection Regulation (GDPR<\/a><\/strong>). It is therefore important to find the best solution in terms of privacy policy to stay within the legislation, for example by anonymizing user information.<\/p>\n