5 Advanced Analytics Algorithms for Your Big Data Initiatives

5 Advanced Analytics Algorithms for Your Big Data Initiatives

Getting started with your advanced analytics initiatives can seem like a daunting task, but these five fundamental algorithms can make your work easier.

There is a fervor in the air when it comes to the topics of big data and advanced analytics. Top analyst firms have written extensively on what initiatives around these concepts can do to revolutionize businesses in a digital era. Fortune 500 companies around the world are investing heavily in big data and advanced analytics and are seeing direct benefits to their company’s top and bottom lines. The problem is that many companies want to achieve incredible results as well but are not sure exactly where to start.

Advanced analytics often starts with a single use case. This includes the application of new methods of data transformation and analysis to uncover previously unknown trends and patterns within their data. When this new information is then applied to business processes and operating norms, it has the potential to transform your business.

To extract greater value from your data, put these five categories of algorithms to work.

Linear regression is one of the most basic algorithms of advanced analytics. This also makes it one of the most widely used. People can easily visualize how it is working and how the input data is related to the output data.

Linear regression uses the relationship between two sets of continuous quantitative measures. The first set is called the predictor or independent variable. The other is the response or dependent variable. The goal of linear regression is to identify the relationship in the form of a formula that describes the dependent variable in terms of the independent variable. Once this relationship is quantified, the dependent variable can be predicted for any instance of an independent variable.

One of the most common independent variables used is time. Whether your independent variable is revenue, costs, customers, use, or productivity, if you can define the relationship it has with time, you can forecast a value with linear regression.

Logistic regression sounds similar to linear regression but is actually focused on problems involving categorization instead of quantitative forecasting. Here the output variable values are discrete and finite rather than continuous and with infinite values as with linear regression.

The goal of logistic regression is to categorize whether an instance of an input variable either fits within a category or not. The output of logistic regression is a value between 0 and 1. Results closer to 1 indicate that the input variable more clearly fits within the category. Results closer to 0 indicate that the input variable likely does not fit within the category.

Logistic regression is often used to answer clearly defined yes or no questions. Will a customer buy again? Is a buyer credit worthy? Will the prospect become a customer? Predicting the answer to these questions can spawn a series of actions within the business process which can help drive future revenue.

Classification and regression trees use a decision to categorize data. Each decision is based on a question related to one of the input variables. With each question and corresponding response, the instance of data gets moved closer to being categorized in a specific way. This set of questions and responses and subsequent divisions of data create a tree-like structure. At the end of each line of questions is a category. This is called the leaf node of the classification tree.

These classification trees can become quite large and complex. One method of controlling the complexity is through pruning the tree or intentionally removing levels of questioning to balance between exact fit and abstraction.

Posted on 7wData.be.