Types of data analysis: 6 steps to put into practice


Types of data analysis


With the digital transformation and the huge amount of data that companies now find available through the most different softwares, whether they are related to data analysis, digital marketing tools, BI or even big data, having access to information has become much more agile.

But what to do with this mass and data? How to get good insights and make the best decisions?

There is an expression created by the London mathematician Clive Humby that states that “data is the new oil” and therefore, we need to talk about data analysis methodologies and the different types of tracking strategies.

This is because the purpose of data analysis is to allow the manager to access relevant information about the company and identify business opportunities, facilitating decision-making through a set of accurate and relevant data.

That way, a good analysis can take place from different approaches and methods. Consequently, to give you a better guidance, we have chosen a data analysis methodology divided into 6 steps.

Check it out!

Types of data analysis

There are several types of data analysis that can be done. Each context calls for a different type and, to know which one is best for your situation, you have to further understand each one.

Because of that, in this topic we will cover the four main types of data analysis. Take a look:

Descriptive data analysis

Descriptive is one of the most common types of data analysis, frequently used in both academic and business environments.

As its name suggests, it is based on real data, in other words, results obtained and recorded in a reliable source of information. Some examples of these sources of data analysis:

  • Reports;
  • Results assessment;
  • Customer segmentation.

Predictive data analysis

Predictive data analysis is also another one which adds up to the most common types of data analysis, especially in the entrepreneurship field.

The purpose of this type of analysis is trying to predict certain scenarios. It is important to remember that predictions will not always come true. This said, the objective of this methodology is defined as the attempt of predicting an event if specific conditions are met.

For example, in a gift shop, it is possible to predict, with this type of data analysis, that sales will increase in December, when Christmas comes. The percentage of this growth can be predicted according to the numbers of the previous years. However, it is important to consider conditions alike.

Prescriptive data analysis

Prescriptive analytics is one of the types of data analysis that are driven by others. In this case, it is directly linked to the predictive. These two types of data analysis must walk along to guarantee a good business strategy.

The objective here is to make decisions regarding the future. Thus, this form of analysis is indicated to draw simulations and predict behaviors.

Diagnostic data analysis

This is one of the less widespread types of data analysis. Like the descriptive one, it aims to analyze a past event and its results. These types of data analysis, then, despite being performed after information that is already consolidated, can be difficult, as they cannot be tested.

The diagnostic analysis, however, aims to find cause and effect relations to unravel an event. The Ishikawa Diagram is a tool that can help with this analysis. Learn more about it:

Data analysis methodology: step by step

Conducting a data analysis process can be a hard task, and considering that, Siteware has prepared a methodology in 6 steps for you:

1st Step – Defining the questions

The first step in our data analysis methodology concerns defining the questions you intend to answer by analyzing the data you have available. That is to say, a diagnostic analysis of the current scenario.

Therefore, the questions must be measurable, clear and concise. Draw up your questions to qualify or disqualify any possible solutions to your problem or specific opportunity.

Imagine the following situation: your company is having trouble with a supplier; they are generating increasing costs for you and also have been unable to offer competitive bids on the business.

In this scenario, a question that you can ask to work it out is:

Is the company able to pay a rescissory fine and hire another provider that can offer better conditions?

Thereby, a cause and effect scenario is analyzed, already measuring the possible changes.

Find out which are the best tools for scenario analysis:

2nd Step – Define what to measure in the data analysis methodology

Using the provider example mentioned above, consider what kind of data you would need to answer your key question.

In this case, it is necessary to know, for example, the costs that this specific supplier represents to your company.

Then, be sure to include any and all reasonable objections that both your company and the supplier may have. If, based on the data analysis methodology, you are going to change suppliers, try to quantify the impacts of this decision.

Step 3 – Define how to measure

In this third step of the data analysis methodology, it is very important to think about how you measure your data. This is because the tracking process can affect the quality of the data collected.

Find below some questions that can be asked in this step:

  • What is your available time frame to analyze the data?
  • What will be the data source?
  • What factors should be included in this analysis?

Taking this into consideration, establish criteria that will allow you to collect data in a timely manner, without affecting its quality.

Step 4 – Collect data

With your questions and your measurement priorities clearly defined, it’s time to collect your data.

In this step of the data analysis methodology, determine what information can be collected from databases or existing sources. Then, collect this data first.

Also, determine in advance a file storage and naming system to help all your team members to collaborate with each other. This process saves up time and prevents them from collecting the same information twice or even more.

If you need to collect data through observation or interviews, develop a survey template in advance to save time. Keep your collected data properly identified and organized.

Step 5 – Perform data analysis

Once you’ve gathered the right data to answer the question you asked in Step 1, you get to the deep analysis of that data.

You can turn to graphs, tables and other visual resources that provide a clearer view of the collected data. At this point, several types of data analysis can be made.

A pivot table, for example, can help you sort and filter data from different variables. During this stage of our data analysis method, using the right softwares can be extremely beneficial.

So, as you manipulate the data, you may need to revise your original question or collect even more data. Either way, this initial study helps you focus your data analysis to better answer your question and any objections that might come up.

Step 6 – Interpret the results

After analyzing your data and possibly running more research, it is finally time to interpret the results. When interpreting your analysis, ask questions like:

  • Does the data answer the original question? If so, in what way?
  • Does the data help you defend against any objections? If so, in what ways?
  • Are there any limitations on your conclusions? If yes, which ones?

If your data interpretation sustainably answers all of these questions and considerations, you have probably reached a productive conclusion.

The only step left is to use the results of this process of data analysis to decide which course of action is going to be the best for your situation.

Next steps

These were the six stages of our data analysis methodology. By following them, you will be able to make decisions based on solid and robustly analyzed data. In addition, your analysis will be more agile and accurate.

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