The world creates over 2.5 quintillion bytes of data every day. Over the last two years alone 90% of all the data was generated. This scenario makes it crucial for businesses and organizations to make sense of all this data. For that purpose, Machine Learning plays a vital role in the task of moving from Data to Decisions. In order to accomplish that, the process can be divided in four steps: Understanding Data, Prediction, Decision-Making and Causal Inference.
Step 1: Understanding Data
The first step is Understanding the Data, both the technical aspects and the domain knowledge required to solve the problem. Descriptive Statistics, Dimensionality Reduction, Clustering and Data Visualization, are very useful to summarize, prepare and get some initial insights from the data. It’s pivotal that the analyst understands the data before the analysis moves any further. It’s also very important to ask the right questions from the beginning and that makes domain knowledge decisive.
Step 2: Prediction
The next step is Prediction, that is to figure out what is likely to happen. Not all predictive problems are the same, there are Regression and Classification problems. These are Supervised Learning methods, the difference is that the target is numeric in Regression and a class in Classification. There are many predictive models for each problem, like the traditional Linear Regression and Logistic Regression. For both Regression and Classification problems, more advanced models are flourishing in the past few years. Neural Networks, these models are especially good to deal with unstructured data. In general, Prediction is a very powerful tool to model uncertainty and provide a clearer view of the future.
Step 3: Decision-Making
After understanding the data and making predictions of what will happen, it’s time to decide what to do next. This step is Decision-making in a data-driven approach. One key aspect of Decision-making is modelling uncertainty, for that purpose predictive models are essential. Another very important point is to balance risk and rewards, in order to make optimal decisions. The goal is to make actions that lead to immediate rewards for the business, but also enable better information collection for future decisions, in other words balance exploitation and exploration. To accomplish all that, it’s crucial to recognize the state dynamics of the business problem. That is how an action impacts the current state and what is the rate at which is possible to gather information. After the business identifies the scenario, the challenge is to make the right decision.
Step 4: Causal Inference
The next step is about Causal Inference and how it can provide the tools necessary to understand and quantify the relationship between cause and effect. In the search for causality, one key aspect is Randomized Controlled Trial, that is a random selection of elements for a control group, where no action has been applied, and a treatment group, where the action under analysis was applied, then gather data from both groups. Scientific method comes to action with Hypothesis Testing, a systematic method that enables to accept or reject hypotheses, based on the data generated from the experiment. Causal Inference plays a vital part in Machine Learning, since it looks for relationship of cause and effect within the data. This is crucial to determine what really works and improve decision-making.
There are countless applications of Machine Learning in all sorts of industries. Companies in areas such as Retail, Finance, Insurance, Marketing, Healthcare and many more rely heavily on Machine Learning to solve their business problems. Methods from traditional Statistics like Linear Regression, Logistic Regression and Time Series Analysis, to more complex cutting edge Deep Learning techniques are helping businesses to move from Data to Decisions. As the world changes and generates tremendous amounts of data every day, it’s crucial for business, governments and organizations to move from gut instinct-driven decisions to data-driven decisions.
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