When we face Business Intelligence , we must carefully study its main discipline, which over time has been integrated into another discipline that has appeared with the arrival of Big Data; We talk about Business Analytics and Data Science (Data Scientist). Index of contents Business Analytics is the fastest growing activity and profession in Business Intelligence since the arrival of Big Data. But first, you have to know what is being discussed here. The definition of Business Analytics provided by Thomas H. Davenport, from Competing on Analytics is: “We understand business analytics as the intensive use of data, statistics and quantitative analysis, predictive and explanatory models, and decision-making based on facts and evidence. BA can be an input for decision-making by people or it can be an engine for automated decision-making.” business_analytics 1. Business Analytics Strategy / Business Analytics To carry out a good Business Analytics strategy, it is important to carry out the following steps.
Design of a data architecture for reporting, analysis, predictive modeling and self-service BI. • Implement a BI Architecture Portfolio. • Solution architecture for data discovery, data visualization, and in-memory BI. • Allow Operational and Analytical BI. • Create master data maintenance program and analytical data governance. • Create shared metadata environments. 2. Activities of a Business Analyst / Data Scientist business_analytics_1 3. Data, information and knowledge number list In Business Analytics, knowledge must constantly iterate with the information, which in turn is executed repeatedly with the data, generating a cycle that results in an enrichment of the first. 3.1. Data By definition, data is the minimum semantic unit and corresponds to primary elements of information that by themselves are irrelevant as support for decision making. They can also be seen as a discrete set of values, which say nothing about the reason for things and are not indicative of action.
he data can come from internal or external sources of the organization, of an objective or subjective nature, or of a qualitative or quantitative type, etc. For example, these can be a person's phone number, their job title, etc. 3.2. Information Information can be defined as a set of processed data that have a meaning (relevance, purpose and context), and that, therefore, are useful for those who must make decisions, by reducing their uncertainty. Data can be transformed into information by adding value to it: • Contextualizing: it is known in what context and for what purpose it was generated. • Categorizing: the units of measurement that help to interpret them are known. • Calculating: the data may have been processed mathematically or statistically. • Correcting: Errors and data inconsistencies have been eliminated. • Condensing: the data have been summarized more concisely (aggregation). Information could be defined with this formula: Information = Data + Context (add value) + Utility (reduce uncertainty). 3.3. Knowledge Knowledge is a mixture of experience, values.