Understanding Fuzzy Logic for Business Analysis Applications

How can business analysts improve the way they use Business Analytics tools?

Enter business analytics training with a fuzzy logic concept.

Business analytics is a highly respected, yet under-optimized element in the modern enterprise application stack. While we have a countless number of business analytics solutions and platforms that are used around the world by mid-level and top-level managers across diverse industry groups, yet we still find only less than 3% of the users actually acknowledging these tools are 100% accurate and are useful for their predictive analytics workflows.

Definition: What is Fuzzy Logic?

Fuzzy Logic is a logical technique used in computing that can take as many values between Boolean integers between 0 and 1. It is often used to measure the “degrees of truth’ and therefore has found immense popularity in modern machine learning algorithms. It can be comparable to Boolean Logic, If Else, and Yes / NO models that can help decision making become simplified and easier to comprehend when injected with advanced machine learning techniques such as Natural Language Processing, Text Analytics, and Computer Vision.

What had begun as a standard mathematical concept to simplify machine level computing knowledge base has now become the top level technique to advance Artificial Intelligence techniques, specific to business analytics training.

Top applications of Fuzzy Logic are seen in the fields of Financial Analysis software development, Energy Management analysis, Marketing cost analysis, NLP accuracy tracking, neural networking, and so on. In recent years, top level Business Analysis tools have used Fuzzy Logic to develop and advance the building of software for Unified Communications, Video marketing, Recommendations Engine, CRM database management, and so on. If you closely monitor the developments in the BI tools development, you would find Fuzzy Logic to be a strong force in the optimization and normalization of techniques across Business Management, Communications, and Accounting.

Here are some of the best projects I have seen within the business analytics domain that have used Fuzzy Logic workflow in their principles.

Aggregation of Sensory Data

This is an interesting project involving the use of Fuzzy Logic to evaluate how different people perceive sensory stimuli and report their findings. For example, sensory data related to a certain type of tea, coffee, and pickles are highly valued in the food and beverages industry. Similarly, perfume makers and chocolate tasters use Fuzzy Logic data to promote their refined products for a certain class of customers. Pricing of perfumes, liquor, and other sensory-based items are all based on the evaluation of sensory data as measured from Fuzzy Logic algorithms.

We can explain similar developments in the fields of Quality Function Deployment (QFD), Marketing database management, and cause and effect analysis for business analysts.