Building an intelligent data solution is an iterative process that requires a team with diverse skills- business experts, data strategists, Big Data engineers, data scientists, and data-informed user experience architects. A great solution involves thinking through the inputs and outputs of your machine learning solution and carefully tuning each part of the system as you learn from your users.
The key to every intelligent data solution is starting with a list of questions and objectives. These two types of information help focus the effort so your data strategy doesn't spin out of control or fall into an analysis paralysis abyss -- all you’ll find there are wasted efforts and valueless outcomes.
Once the basic statistics are produced, it's time to start identifying the underlying patterns and relationships between your data. This is where you may find correlations between variables by running different statistical functions.
It's critical to understand how best to access, move, and transform data in a scalable manner so it's fresh and easy to mine. Oftentimes, the biggest asset in creating intelligent data solutions is Big Data engineering. This starts with identifying and preparing your data and may also include identifying external sources of data.
At this step you identify or design potential algorithms to model your data and answer your key questions. Based on the data you have in hand you'll chose a supervised or unsupervised model. Typically, this involves trying a few different algorithms and learning which works best.
Before trying to identify patterns in your data, it's important to spend time exploring it. This involves running standard statistical functions on data columns or variables, and generating histograms. It may also include running Natural Language Processing algorithms on your freeform text to capture common themes and topics.
Once you have a working model that meets your objectives, it's time to build it into an application. This may be an existing visualization tool or a custom application. Either way, it's very important to consider how the output of your model is presented to a user. This can make or break the value of all of your upstream efforts.