6 Types of Data Analysis That Help Decision-Makers| MAE

There are different methods of data analysis used by companies today. Some are similar to each other, some are complementary. Below, we’ll focus on the six key ways of analysing data that we believe to be most frequently used in various software projects. Let’s take a look.

1. Exploratory Analysis

The goal of this type of analysis is to visually examine existing data and potentially find relationships between variables that may have been unknown or overlooked. It can be useful for discovering new connections to form a hypothesis for further testing.

With an exploratory analysis, you try to get a general view of the data you have on-hand and let it speak for itself. This approach looks at digital information to find relationships and inform you of their existence, but it does not establish causality. For that, you’d have to conduct further analytical procedures.

2. Inferential Analysis

This type of analysis is all about using a sample of digital information to make inferences about a larger population. It is a common statistical approach that is generally present across various types of data analysis, but is particularly relevant in predictive analytics. This is because it essentially allows you to make forecasts about the behaviour of a larger population.

Here, it’s important to remember that the chosen sampling technique dictates the accuracy of the inference. If you choose a sample that isn’t representative of the population, the resulting generalizations will be inaccurate.

3. Descriptive Analysis

Now, we’ll be going into the terminology that is most widely used in the context of software development and that you may have already heard of.

Descriptive analysis is probably the most common type of data analysis employed by modern businesses. Its goal is to explain what happened by looking at historical data. So, it is precisely what is used to track KPIs and determine how the company is performing based on the chosen metrics.

Things like revenue or website visitor reports all rely on descriptive analysis, as do CRM dashboards that visually summarize leads acquired or deals closed. This approach merges data from various sources to deliver valuable insights about the past. Thus, helping you identify benchmarks and set new goals.

Find out how we helped a FinTech Company Unify Data from Disparate Systems

Of course, this technique isn’t very helpful in explaining why things are the way they are or recommending best courses of action. Hence, it’s a good idea to pair descriptive analysis with other types of data analytics.

4. Diagnostic Analysis

Once you’ve established what has occurred, it’s important to understand why it happened. This is where diagnostic analysis plays a role. It helps uncover connections between data and identify hidden patterns that may have caused an event.

By continuously leveraging this type of data analysis, you can quickly determine why issues arise by looking at historical information that may pertain to them. Thus, any problems that are interconnected can be swiftly discovered and dealt with.

For example, after conducting a descriptive analysis, you may learn that contact centre calls have become 60 seconds longer than they used to be. Naturally, you’d want to find out why. By employing diagnostic analysis at this point, your analysts will try to identify additional data sources that may explain this.

After looking at real-time call analytics, they might discover that some newly hired agents are struggling with finding relevant information to help the client, and this causes extra time to be spent on the search. Now that the problem is clearly identified, you’ll be able to deal with it in the most suitable manner.

Discover how a leading insurance firm benefited from a Real-Time Call Analytics Platform

5. Predictive Analysis

Now we’re moving on to some of the most exciting types of data analysis. Predictive analytics, as the name suggests, delivers forecasts regarding what is going to happen in the future. Often, it incorporates artificial intelligence and machine learning technologies to provide more accurate predictions in a faster manner.

Read up on the Role of AI in Business

Predictive analysis usually looks at patterns from historical data as well as insights about current events to make the most reliable forecast about what might happen in the future.

For example, in the manufacturing industry, historical information on machine failure can be supplemented with real-time data from connected devices to predict asset malfunctions and schedule timely maintenance.

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