Data Analytics Methodologies
ZenaByte offers solutions for the challenges posed by the growing complexity of extracting meaningful information from data. Our expertise spans from descriptive analytics to diagnostic, predictive, and prescriptive analytics.
ZenaByte solutions leverage the most recent advances in Data Mining, Machine Learning, Deep Learning, and Artificial Intelligence (AI), with the purpose of extracting new, meaningful, and actionable information transforming data into an actual value.
ZenaByte solutions are able to answer different, increasingly complex, and valuable questions:
Thanks to tools coming from the world of descriptive and visual analytics (e.g., reporting tools, dashboard, and process mining) we can exploit data to understand what happened in the past and what is happening right now.
What will happen in the future?
Thanks to tools coming from the world of predictive analytics (e.g., machine learning and deep learning models) we can exploit data to understand what will happen in the future by also ensuring (statistically) the quality of the developed models, or more generally, their trustworthiness (ensure privacy, fairness, robustness, and interpretability).
Why something happened?
Thanks to tools coming from the world of diagnostic analytics (e.g., causal models, hidden Markov models, and causal inference) we can exploit data to understand why something happened in the past and why is happening right now.
What can we do to make something happen?
Thanks to the combines use of diagnostic analytics and predictive analytics with AI we can suggest action to put place to ensure, or at least try, to make something happen in the future (e.g., reduce failures or increase sells of a products).
The technological enablers of these tools are libraries in R (e.g., caret), Python (e.g., pandas, scikit-learn, tensorflow, and pytorch) that will be exploited as building blocks for the development of custom, effective, and efficient solution to client problem.
We will also leverage on specific skills in learning from data (e.g., Natural Language Processing, Sentiment Analysis, Graph Machine Learning, and Edge Machine Learning) to be able to empower the ability of our products to describe, diagnose, predict, and prescript new, meaningful, and actionable information from data.