The 5 Core Methods of Quantitative Data Analysis

The number of organizations specializing in data collection has reached an all-time high.

From manufacturing to plant operations, data can create knowledge basis and build stronger economic models. But that's only when good quantitative data analysis tactics are applied.

Strong data, like great ideas, are useless if not properly applied. A whole bunch of numbers will just be a bunch of numbers until a method of analysis is applied.

Once you've collected your data, follow these 5 methods to allow data analysis to transform those numbers into growth for your business.

1. Correlation

Analyzing your data through correlation will allow you to make comparative adjustments to your operations. Find out if as one variable increases other variables decrease.

This might be a random pattern that happens to look like a correlative relationship. It might also be a direct relationship that you would have missed if not for quantitative data analysis.

Find direct relationships of increase or decrease between data sets. Don't jump to conclusions but make notes and monitor these relationships closely.

2. Regression

Regression as an analysis method is important for making future predictions. It can allow you to monitor seasonal changes relative to performance or stock prices relative to productivity.

Regression can let you know whether training on a particular program increases or decreases instances of error. Regression is similar to correlation. Causes shouldn't necessarily be inferred from the relationship between the two data sets without further study.

3. Averages

If you gather enough data over a long period of time, you can create a series of averages. This can be important to stockholders or investors to see before they make decisions.

This can affect hiring practices or the purchasing of supplies. Be sure that you include external factors before you make any decisions based on your averages.

Behavior can be predicted to some degree, but basing your decisions on averages can prohibit growth by urging you to be more conservative with your budget, for example.

4. Deviation

When combined with averages and means, measuring deviation can give you a broader scope of what to expect from your measured factors.

Standard deviation will tell you how high or how low a given measurement can reach. Knowing how much deviation is comfortable will allows you predict a margin of error and how high to set the bar for success.

Quantitative data analysis that entails studying deviation is a great way to set standards for your budget, employee performance, or profit margins.

5. ANOVA or Analysis of Variance

Studying variance is a core comparative method to drawing a relationship between two sample groups.

Is their relationship significant or do the figures show a random pattern? Does more time off for workers lead to better productivity? Do shorter team sprints lead to higher or lower margins of error?

ANOVA will show you whether or not their relationship has any significance.

Quantitative Data Analysis Has Impact

Remember: The task of data isn't to tell you "why" something is working. Data analysis can show you how well your hypotheses are working.

Our IT solutions are based on web-based visualization platforms which allow you to view as-built, intelligent 3D Models linked to selected types of analytical operation or maintenance data. You can directly integrate the information into your existing CMMS.

Contact us today to find out how you can harness the power of analytics for your business.

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