Data analytics allows businesses to gain an understanding of operational and consumer behavior.

More data has been generated in the last five years than in the entirety of human history combined.

Being able to leverage and learn from your data allows you to make improved business decisions that will distinguish you amongst your competitors. All companies, no matter their size, can utilize modern data analytics practices in order to understand the dynamics that drive their businesses. 

Unlike deep learning, which often requires gigabytes or terabytes of data, data analytics methodologies can be applied across most data sizes. Typically, analytics engines provide consumers with quantitative descriptive metrics to drive business insights. At Sannsyn, we provide both production level analytics engines and exploratory data analysis consulting to help develop robust understanding of your data. 

All data science and big data projects begin with a phase of “exploratory data analysis (EDA)” where we work with our clients to identify trends, hypotheses, and features around their data. We firmly believe that in order to implement a robust data pipeline, they must have a strong qualitative and quantitative understanding of their data. Sannsyn employs a variety of methodologies during the EDA phase of a project including Bayesian Analysis, Frequentist Statistical Testing, Graphical Methods, Feature Extraction, and Unsupervised Learning to help develop hypothesis and identify trends in our clients data to drive immediate return on investment and provide the basis of more advanced data projects such Deep Learning or Reinforcement Learning.

Our production level analytics engines create a robust and interactive environment to provide a quantitative foundation for business decisions. At Sannsyn we create custom API and dashboarding solutions for you to monitor and interface with your data. Our infrastructure is scalable and can handle data of all sizes. Our solutions include building platforms for scheduling analytics jobs using Spark to create aggregate statistics tables from a data lake or NoSQL storehouse, API endpoints to pull cleaned and normalized data for custom analytics, and reporting/visualization using D3.js, Plotly, Tableau, and Power BI.