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Business Intelligence and the Human Brain - Coggle Diagram

- - - - Characteristics
        • Simulation of Organizational changes (Commercial Structure, Product Mix, Divisions, Markets).
        • Unlimited versions for scenario simulation.
        • Cascade models with generic variables (Interest Rate, Exchange Rate, etc…) and/or specific ones (Prices, Quantities…).
        • Simulation on Drivers: Impact of the Cost of Raw Material, Simulation of Prices, Margins and %, etc.
        • Sensitivity Analysis before variations in exchange rates (eg Manufacture or Buy and where to do it).
        
        Projections
        The realization of projections on the results of the Company and the variables that will influence the optimization of these, are key factors for the achievement of objectives and above all to know the possible growth scenarios to know where to direct the Company and what adjustments along the way, distinguishing where it is necessary to improve and where and how much it is necessary to invest.
        
        Predictive Models
        The application of complex statistical algorithms, to business models with large volumes of information, by business users, in minimum response times, has meant an important advance so that Companies can make predictions about the future or even about events. not known.
- - - - What dangers does AI have?
        There are three fundamental risks: accidents, misuse and arms races. Artificial intelligence systems sometimes malfunction. For now, the damage they can cause is limited, although there have already been fatal accidents involving autonomous cars.
        Greater control in processes: artificial intelligence is capable of controlling and being much more efficient in production processes and production lines. · Greater productivity: in addition, thanks to automation and rapid decision-making, AI favors the increase and quality of productivity.
- - - - Likewise, intelligence, creativity, communication and relationship between living beings have reached their maximum capacity and refinement in humans, and this is mainly due to the remarkable development and evolution of the brain.
- - - - Data analytics is the process of exploring, transforming, and examining data to identify trends and patterns that reveal important insights and increase efficiencies to support decision making. A modern data analytics strategy enables systems and organizations to work from automated analytics in real time, ensuring immediate, high-impact results.
        
        The data analysis process
        The data analysis process is based on several steps and phases. Findings from later phases may require rework at an earlier phase, implying a cyclical rather than a linear process. Most importantly, the success of data analysis processes depends on the repeatability and automation of each of these steps.
        
        The analysis process is best divided into the following steps and phases:
        Data Entry: Determines the requirements and collects the data. This involves some investigative work, such as talking to stakeholders, finding out who is responsible for the data, and gaining access to the data.
        
        data input
        Data Preparation: This is the strategy and tactic of preparing data for your primary goal of producing analytics insights. This includes cleaning and consolidating raw data into data that is well-structured and ready for analysis.
        data preparation
        Data exploration: Data exploration, or exploratory data analysis, is the process of studying and investigating a large data set through sampling, statistical analysis, pattern identification, visual profiling, and more.
        data exploration
        Data Enrichment: Data is enriched and augmented with inputs and additional data sets to improve analysis.
        data enrichment
        Data science: It is about applying more advanced methods of data extraction to obtain deeper and more difficult-to-extract meanings and insights, which are largely unattainable through more rudimentary modalities of data processing.
        data science
        Business intelligence: Business results can be achieved through an organization's combination of data, software, infrastructure, business processes, and human intuition.
        business intelligence
        Report Builder: The results of data analysis must be shared in an effective way that preserves the insights gained. The Report Generator organizes that knowledge and its results in an easy-to-understand format.
        reporting
        Optimization: Since variables change over time, it is necessary to optimize and improve models so that they continue to serve their original purpose or to evolve from this purpose based on new inputs or changing characteristics.