{"id":1173,"date":"2022-06-28T14:45:12","date_gmt":"2022-06-28T14:45:12","guid":{"rendered":"https:\/\/ignos.blog\/application-of-artificial-intelligence-in-bi"},"modified":"2023-04-03T11:11:28","modified_gmt":"2023-04-03T11:11:28","slug":"application-of-artificial-intelligence-in-bi","status":"publish","type":"post","link":"https:\/\/ignos.blog\/en\/application-of-artificial-intelligence-in-bi","title":{"rendered":"Application of Artificial Intelligence in Business Intelligence (BI)"},"content":{"rendered":"\n

Artificial Intelligence is transforming the processes of an increasing number of organizations at a rapid pace. Not too long ago, these tools and the necessary infrastructure to utilize them were only accessible to a few, primarily large corporations. As AI becomes more democratized, these tools are starting to become available to a larger number of users, including medium and small-sized businesses.<\/p>\n\n

The reduction in costs related to both processing<\/a> and storage capabilities allows for the exploitation of these tools at reasonable costs. A proper implementation of an AI solution can provide benefits that far outweigh the expenses required for its deployment and operation, especially in the field of Business Intelligence.<\/p>\n\n

AI Techniques<\/h2>\n\n

There are multiple techniques to implement AI solutions, and one relevant example is machine learning<\/a><\/strong>. This branch of Artificial Intelligence allows computer systems to autonomously learn from historical data, giving them the ability to make predictions. Other Artificial Intelligence systems enable the generation of content based on the data on which they have been trained. These different systems, as will be seen, can bring great value to existing BI platforms.<\/p>\n

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Branches of Artificial Intelligence<\/figcaption><\/figure><\/div>\n

The most commonly used models today are neural networks, of which there are different models depending on the type of prediction desired. For example, recurrent neural networks<\/strong> are used for the prediction of time series. Transformers<\/strong>, which have recently emerged, are used in very large natural language processing models such as GPT-3 or LaMDA<\/strong>, or in systems for generating images from text descriptions such as DALL-E<\/strong> or Google Image<\/a>.<\/p>\n\n

Machine learning includes supervised learning strategies that require data to be labeled beforehand, unsupervised learning strategies that do not require such labeling, or mixed models. Machine learning also includes reinforcement learning, in which the prediction capability is improved through continuous simulation of the system itself. Finally, deep learning uses networks with a high number of layers, allowing for interpretation of very complex data.<\/p>\n\n

The uses or applications of AI are currently present in numerous sectors, and their growth is exponential. They cover sectors such as commerce, industry, or health, for example. In any case, its integration into Data Warehouse systems is becoming increasingly present due to the enormous benefits it brings.<\/p>\n\n

Applications of Artificial Intelligence.<\/h2>\n\n

Within the business environment, many AI systems rely on the huge amount of data managed by corporations, establishing a Big Data – AI relationship with numerous applications. These systems allow the discovery of structures, patterns, trends, or non-obvious anomalies to the human eye, and ultimately a meaningful understanding of the data.<\/p>\n\n

AI systems are helping with customer segmentation and retention, payment fraud detection, advertising effectiveness by predicting campaign popularity, conducting predictive marketing, detection of consumption patterns and demand patterns.<\/a>, stock forecasting or the recommendation of relevant products or advertisements for users, among others. <\/p>\n\n

Real examples<\/h3>\n\n

Both these and other specific AI systems allow for decision-making support. <\/p>\n\n