While not so long ago, one of the main issues when making decisions about something was a lack of relevant data, it seems that few are complaining about the same thing now. On the contrary, data is overpowering and overwhelming, which makes it equally difficult for decision makers to make the right call. The focus is now on finding the right or the best data that can serve your purpose and their correct interpretation. And that is an extremely challenging task.
People are obsessing over “big data” and there are many tools and techniques supposed to help organizations use data in the best possible ways, but more often than not, the results achieved fail to meet the expected ones. So, what data is available and how to know what to use? Let’s take a look at how data are analyzed and what the advantages and disadvantages of each method are.
Table of Contents
Mean
The most simple one is, of course, the arithmetic mean, a.k.a. “the average.” The sum of a list of numbers divided by the number of items on the list is used to identify the overall trend of a data set or provide a quick snapshot of your data. It’s by far the fastest and easiest to calculate. However, it has many disadvantages. If analyzed without a proper context, it can lead to wrong conclusions. It is sometimes closely related to the mode and the median (two other measurements near the average), but if you take a data set with a high number of outliers, you just can’t get the accuracy you need.
Standard deviation
Commonly represented with the Greek letter sigma, standard deviation measures the spread of data around the mean. In the case of high standard deviation, the data are spread more widely from the mean. While it gives you a quick overview of the dispersion of data points, it can be equally deceptive as the mean, if taken alone. This is especially true if there is a strange pattern, such as an abnormal curve or a large number of outliers. So, you should never use it as the only technique.
Regression
Regression can help you tell how dependent and explanatory variables affect each other and it’s commonly charted on a scatterplot. The regression line shows if the relationships are strong or weak. Businesses use this model to determine trends over time. For example, if you wish to see how the Australian media report on your company, you have to turn to the data provided by social media analytics, among others. Still, you need to know that regression is not very nuanced. And you need experts in the field to help you combine the data obtained using this method with others in order to get the best big picture.
Sample size determination
If you’re supposed to measure a large data set, you don’t always need to obtain information from every member. But you can use a sample. If you manage to get a good sample, you’ll save a lot of time and money analyzing the data. It’s always preferable to have a relevant sample to using data that relates to the things outside your sphere of interest. You have to be careful, though, to select the right size of a sample. If you want to be accurate and that’s where proportion and standard deviation come in handy. The problem could be that your assumptions might be wrong in the first place, thus making your analysis totally useless.
Hypothesis Testing
Also known as t testing, hypothesis testing determines whether a certain premise is true for your data set or population. You treat the results of a hypothesis test as statistically significant. If you establish that the results couldn’t have happened by random choice. Actually, the randomness of data is probably the biggest problem you can encounter in data analysis.
All these methods of data analysis have their merits as well as pitfalls. However, no decision making can do without them, which means it’s vital how you approach data analysis. As we’ve already established, it is a complex and challenging process that should be left to professionals.
After all, if you want to make the best decision possible. You need to know exactly what you’re looking for and what kind of data you need. Unfortunately, the success of any analysis is heavily reliant on each phase of the process. And you need to avoid making any mistakes along the way. If you wish to obtain relevant and correct data which you’re going to use when you making decisions.
