Business Analytics Part 1 — Main Types
Learning data comes with high importance for each day. This can be for a full-time job, internship, volunteer work, or through a project. Data visualization, data processing, cleaning, transforming, and strategy ties in to Business Analytics. Each have four types with their descriptions for real-world learning knowledge and hands on experience.
Descriptive analytics measures what happened. It allows to break big chunk of data into smaller pieces which involves to extract the relevant information from data. An example would be about customer data. Questions to ask ourselves: How many different segments of buyers are we dealing with? Where are these buyers located? How do high-value customers differ? What are they interested in? What is the income, age, number of children, occupation, and regional breakdown of these buyers? This determines about the cause and effect when storytelling the data.
Diagnostic analytics requires to dig deeper into the data that’s been collected and to have an understanding of why things happened. A scenario would be to look into why more patients have come in to the hospital and their duration of their stay. Its important to have a clear problem to determine the analysis is relevant, collect, clean, and prepare the data, and to use the data software to find insights. This include data visualizations, statistical analysis, and dashboards. By looking into the problem in more detail, it can determine about how accurate the present data is.
Predictive analytics helps to condense the data. With this technique, it uses different statistical data modeling and data mining for studying most recent to past trends. It gives an advantage to business analysts or data scientists to make predictions. An example would be about a marketing campaign done with an organization. We ask ourselves on: Who will respond to this campaign, and for what product and through which channel? What are the potential values for each customer and prospect? Who will stop the subscription to your service and when would that be? Updated data helps us to compare the past, present, and future.
Prescriptive analytics optimizes decision making by determining the best solutions available among various choices for given business constraints. The question to ask ourselves: What should we do? Examples: Venture capital with investment decision-making, banking with fraud detection, and making algorithm recommendations for content duration. Being prescriptive in the present and future shows on what improvements can be done and also what needs to be accomplished depending on timeframe.
