Building an intelligent data solution is an iterative process that requires a diverse, experienced team.
We think through the user experience and underlying machine learning model, carefully tuning each part of the system as we learn from real-world use.
Start with a list of questions and objectives so your initiative doesn't spin out of control or fall into an analysis paralysis abyss.
Understand how best to access, move, and transform data in a scalable manner so it's fresh and easy to mine for rapid analysis.
Spend time exploring your data. Run statistical functions on data columns or variables and apply NLP on your freeform text.
Identify underlying patterns and relationships between your data using techniques like histograms, frequency diagrams, and correlation analysis.
Chose a supervised or unsupervised approach and learn several models to answer your questions. Tweak parameters and pick the best fit.
Integrate your model into an application. Seriously consider how the output of your model is best presented users.