Our Theoretical Base
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Inferencial
To begin any journey in the exploration and exploitation of business data to generate value from its information and be able to make better business decisions, it is imperative to understand the phases that lead to advanced analytics, the relevance of each step and the level of business maturation. With this it will be possible to choose the best strategy for our business and the benefits of working and developing solutions at each of the stages.
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The theoretical basis on which we work at Inferential is detailed below:
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Starts with Business such case studies, Qualitative analytics, Preliminary data reports, Reporting with visuals, Creating dashboards or Sales forecasting, among others. 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 thatApplied Statistics (descriptive, inferential or multivariated) is a constant across all phases, as a key factor to success.
01
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:
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​Improve decision making
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Improve customer experience
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Streamline operations
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Increase productivity
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Predict the future
02
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).
03
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.
04
Advanced Analytics
It 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:
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data/text mining
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machine learning
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pattern matching
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forecasting & visualization
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semantic analysis
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sentiment analysis
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network & cluster analysis
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multivariate statistics
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graph analysis, simulation
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complex event processing
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neural networks
The most powerful Applied Statistics tools at your service.
