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Companies use Business Intelligence (BI), Data Science , and Process Mining to leverage data for better decision-making, improve operational efficiency, and gain a competitive edge. Process Mining offers process transparency, compliance insights, and process optimization.
Open-source business intelligence (OSBI) is commonly defined as useful business data that is not traded using traditional software licensing agreements. This is one alternative for businesses that want to aggregate more data from data-mining processes without buying fee-based products.
At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and datamining techniques to uncover meaningful patterns and relationships.
Nevertheless, process mining can be considered a sub-discipline of business intelligence. It is therefore hardly surprising that some process mining tools are actually just a plugin for PowerBI, Tableau or Qlik.
To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.
Data Visualization Tools These tools create visual representations of data, such as graphs and dashboards, making complex data sets easier to understand. DataMining Tools Datamining tools analyse large datasets to discover hidden patterns or relationships within the data.
Diagnostic Analytics Diagnostic analytics goes a step further by explaining why certain events occurred. It uses datamining , correlations, and statistical analyses to investigate the causes behind past outcomes. It analyses patterns to predict trends, customer behaviours, and potential outcomes.
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