Nuestra Base Teórica


Para iniciar cualquier viaje en la exploración y explotación de los datos del negocios para generar valor a su información y poder tomar mejores decisiones de negocios, es imperativo entender las fases que que llevan a la analítica avanzada, la relevancia de cada paso y el nivel de maduración del negocio. Con esto será posible elegir la mejor estrategia para nuestro negocio y los beneficios de trabajar y desarrollar soluciones en cada una de las etapas.

A continuación se detalla la base teórica sobre la cual trabajamos en Inferencial:

Estadística | Inferencial - from Data Integration to Advanced Analytics

Starts with Business such case studies, Qualitative analytics, Preliminary data reports, Reporting with visuals, Creating dashboards or Sales forecasting, among other. And, depending on the time frame we can refer to analytics looking backward, to the past that is. Or future, which will refer to predictive analytics. It's important to highlight that Applied Statistics (descriptive, inferential or multivariated) is a constant across all phases, as a key factor to success.


Data Integration

Data is business' most valuable asset, and Data Silos are no longer useful. Data integration allows businesses to combine data residing in different sources and can support your growth strategy, helping you retain customers and increase profitability as well as:

  • Improve decision making

  • Improve customer experience 

  • Streamline operations

  • Increase productivity

  • Predict the future


Data Science
(Business Analytics)

Data Science is a field that can’t do without data. Therefore, it is completely within the realm of Data Analytics. Data Analytics and Business Analytics at the same time is indeed Data Science.There exist data science processes that are not directly and immediately business analytics but are data analytics. Data science is both, past (analysis) and future (predictive).


Business Intelligence

Business Intelligence (BI) is the process of analyzing and reporting historical data. It's not necessarily past-oriented, but there are no predictive analytics involved. Regression, classification, and all the other typically predictive methods are a part of Data Science. Business Intelligence is entirely a subset of Data Science. Thus, when one is dealing with descriptive statistics, reporting or visualization of past events, we're doing both BI and data science.


Advanced Analytics

Is the examination of data or content using sophisticated techniques and tools, typically beyond those of traditional BI, to discover deeper insights, make predictions, or generate recommendations. These techniques can include: 

  • data/text mining

  • machine learning 

  • pattern matching 

  • forecasting & visualization

  • semantic analysis

  • sentiment analysis

  • network &cluster analysis

  • multivariate statistics

  • graph analysis, simulation

  • complex event processing

  • neural networks

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