Definition of Analytical Modeling:« Back to Glossary Index
Not everything in business is quantifiable, but most of it is. Understanding the relationships between dozens of different factors and forces influencing a specific outcome can seem impossible, but it’s not. Analytical modeling is an effective and reliable technique for turning a mess of different variables and conditions into information you can actually use to make decisions.
Overview: What is analytical modeling?
Analytical modeling is a mathematical approach to business analysis that uses complex calculations that often involve numerous variables and factors. This type of analysis can be a powerful tool when seeking solutions to specific problems when used with proper technique and care.
3 benefits of analytical modeling
It’s hard to overstate the value of strong analytics. Mathematical analysis is useful at any scale and for almost every area of business management.
1. Data-driven decisions
The primary benefit of leveraging analytical modeling is the security of making data-driven decisions. Leaders don’t have to take a shot in the dark. They can use analytics to accurately define problems, develop solutions and anticipate outcomes.
2. Logical information structure
Analytical modeling is all about relating and structuring information in a sensible way. This means you can use the results to trace general outcomes to specific sources.
3. Can be shared and improved
The objective nature of analytical modeling makes it a perfect way to establish a common foundation for discussion among a diverse group. Rather than trying to get everyone on the same page through personal and subjective theorizing, using analytical data establishes a singular framework for universal reference within an organization.
Why is analytical modeling important to understand?
Like any other business practice, it’s important to understand this kind of analysis so you know what it can and can’t do. Even though it’s a powerful tool in the right hands, it’s not a magic solution that’s guaranteed to fix your problems.
1. Information requires interpretation
Information can be invaluable or completely worthless depending on how you use it. You should always carefully examine the factors and implications of the data in question before basing major decisions on it.
2. Analytics needs good data
Accurate, complete and relevant information are essential for a useful outcome. If poor data is put into a model, poor results will come out. Ensuring quality of data collection techniques is just as important as the modeling itself.
3. Various applications and approaches
Analytical modeling tends to focus on specific issues, questions or problems. There are several different types of models that can be used, which means you need to figure out the one that best fits each situation.
An industry example of analytical modeling
A barbecue restaurant serves customers every day of the week from lunch through dinner. To increase overall profit, management wants to reduce losses from waste and cut down on missed sales. Since they need to start preparing meat days in advance and any leftovers are discarded, the establishment needs to find a way to accurately predict how many customers they will have each day.
The restaurant hires outside contractors to create a predictive analytics model to address this need. The modelers examine varuiys relevant factors, including historical customer attendance in previous weeks, weather predictions and upcoming specials or events of nearby restaurants. They create an initial model and start comparing actual results against predicted results until they’ve reached 90 percent accuracy, which is enough to meet the restaurant’s goals.
3 best practices when thinking about analytical modeling
Think about analytical modeling as a starting point for decisions and a tool that can be continually improved as you use it.
1. Start with a goal
Analytical modeling can’t answer a question that isn’t asked. It’s easy to make the mistake of looking for answers or patterns in general data. This kind of modeling is best used by created calculations to answer a specific initial question, like: “How can we turn more visitors into customers?” or “How can we make this process less wasteful.”
2. Continue to refine parameters
Think of the first model as a rough draft. Once you have an initial model delivering results, it’s important to compare it to reality and find ways to make the results even better.
3. Be consistent
Don’t just turn to analytics when faced with an urgent problem. If you make data mining and analysis a part of your daily operations, you’ll be in a much better position to actually leverage this strategy when the time comes.
Frequently Asked Questions (FAQ) about analytical modeling
1. What are the common forms of analytical models?
There are four main types of models: descriptive, diagnostic, predictive and prescriptive. The right one to use depends on the kind of question you need an answer to.
2. How do you make an analytical model?
Modeling requires access to a full set of relevant data points, relationship conditions and project objectives. For example, when trying to predict the outcome of a certain situation, modelers need to account for every factor that can impact this outcome and understand how each one of those factors influences the results as well as other variables in the calculation in a quantifiable way.
3. What is the purpose of analytical models?
The purpose of analytical modeling is to make sense of a process or situation that has too many variables to estimate accurately. It’s particularly important when dealing with larger operations and processes.
Managing with models
Companies survived for hundreds of years without computing technology to help them do complex modeling. However, that doesn’t mean you will be fine without it. The data revolution has already happened and the capabilities it offers companies can’t be ignored. Business leaders in every industry should be moving modeling to the center of their management practices if they are serious about growing in the years ahead.« Back to Dictionary Index