What Type Of Data Analysis Should I Use

The most successful businesses and organizations are those that constantly larn and accommodate. No matter what industry you're operating in, it'southward essential to understand what has happened in the past, what'due south going on now, and to anticipate what might happen in the hereafter. And so how do companies exercise that?

The answer lies in data analytics. Nigh companies are collecting information all the time—but, in its raw course, this data doesn't actually mean anything. It's what you lot practise with the information that counts. Data analytics is the procedure of analyzing raw data in society to draw out patterns, trends, and insights that can tell you something meaningful virtually a detail area of the business. These insights are so used to make smart, data-driven decisions.

The kinds of insights y'all get from your data depends on the blazon of analysis you lot perform. In data analytics and information science, there are four principal types of information analysis: Descriptive, diagnostic, predictive, and prescriptive. In this post, we'll explicate each of the four unlike types of information analysis and consider why they're useful. If you're interested in a particular type of assay, jump straight to the relevant section using the clickable menu below.

Types of data analysis: Descriptive
Types of data analysis: Diagnostic
Types of data analysis: Predictive
Types of data assay: Prescriptive
Key takeaways and further reading

And so, what are the iv main types of data assay? Allow's find out.

The four main types of data analytics: Descriptive, diagnostic, predictive, and prescriptive

1. Types of data analysis: Descriptive (What happened?)

Descriptive analytics looks at what has happened in the by. As the proper name suggests, the purpose of descriptive analytics is to simply describe what has happened; it doesn't attempt to explain why this might have happened or to establish cause-and-effect relationships. The aim is solely to provide an hands digestible snapshot.

Google Analytics is a good example of descriptive analytics in action; it provides a simple overview of what's been going on with your website, showing yous how many people visited in a given fourth dimension flow, for instance, or where your visitors came from. Similarly, tools like HubSpot will show you how many people opened a detail electronic mail or engaged with a certain entrada.

A screenshot taken from Google Analytics, showing one of the types of data analysis (descriptive) for a website

At that place are two master techniques used in descriptive analytics: Data aggregation and information mining. Data aggregation is the process of gathering information and presenting information technology in a summarized format. Permit's imagine an ecommerce company collects all kinds of data relating to their customers and people who visit their website. The aggregate data, or summarized data, would provide an overview of this wider dataset—such as the boilerplate client age, for example, or the average number of purchases made.

Information mining is the analysis part. This is when the annotator explores the information in order to uncover any patterns or trends. The outcome of descriptive analysis is a visual representation of the data—as a bar graph, for example, or a pie chart.

So: Descriptive analytics condenses large volumes of data into a clear, elementary overview of what has happened. This is often the starting point for more than in-depth analysis, as we'll now explore.

2. Types of data analysis: Diagnostic (Why did information technology happen?)

Diagnostic analytics seeks to delve deeper in order to understand why something happened. The main purpose of diagnostic analytics is to identify and reply to anomalies within your data. For instance: If your descriptive assay shows that there was a twenty% drib in sales for the month of March, you'll desire to discover out why. The side by side logical step is to perform a diagnostic analysis.

In lodge to get to the root cause, the analyst volition start by identifying any additional data sources that might offer further insight into why the drop in sales occurred. They might drill down to find that, despite a good for you volume of website visitors and a good number of "add together to cart" actions, very few customers proceeded to actually cheque out and brand a purchase.

Upon farther inspection, it comes to lite that the majority of customers abandoned transport at the indicate of filling out their commitment address. Now nosotros're getting somewhere! It'south starting to wait similar there was a problem with the address course; perhaps it wasn't loading properly on mobile, or was simply as well long and frustrating. With a little scrap of digging, you're closer to finding an explanation for your data anomaly.

Diagnostic analytics isn't only near fixing problems, though; you lot can also apply information technology to see what's driving positive results. Perhaps the information tells yous that website traffic was through the roof in October—a whopping 60% increase compared to the previous month! When you lot drill down, information technology seems that this spike in traffic corresponds to a celebrity mentioning one of your skincare products in their Instagram story.

This opens your eyes to the power of influencer marketing, giving you something to think about for your time to come marketing strategy.

When running diagnostic analytics, there are a number of different techniques that you might employ, such as probability theory, regression analysis, filtering, and time-serial analysis. You lot can learn more than most each of these techniques in our introduction to information analytics.

So: While descriptive analytics looks at what happened, diagnostic analytics explores why it happened.

3. Types of information analysis: Predictive (What is probable to happen in the time to come?)

Predictive analytics seeks to predict what is likely to happen in the futurity. Based on past patterns and trends, data analysts tin devise predictive models which estimate the likelihood of a hereafter event or outcome. This is peculiarly useful as it enables businesses to programme ahead.

Predictive models use the human relationship between a ready of variables to make predictions; for instance, you might employ the correlation between seasonality and sales figures to predict when sales are likely to drop. If your predictive model tells you that sales are likely to go down in summer, you might utilize this information to come up with a summertime-related promotional campaign, or to decrease expenditure elsewhere to brand up for the seasonal dip.

Perhaps you own a restaurant and want to predict how many takeaway orders yous're probable to get on a typical Saturday night. Based on what your predictive model tells you, you might decide to get an extra delivery driver on hand.

In improver to forecasting, predictive analytics is too used for classification. A commonly used classification algorithm is logistic regression, which is used to predict a binary outcome based on a set of contained variables. For case: A credit card company might use a predictive model, and specifically logistic regression, to predict whether or not a given client will default on their payments—in other words, to allocate them in one of two categories: "will default" or "will not default".

Based on these predictions of what category the customer will autumn into, the company can quickly assess who might be a good candidate for a credit card. You can learn more most logistic regression and other types of regression assay here.

Machine learning is a branch of predictive analytics. Only as humans employ predictive analytics to devise models and forecast future outcomes, car learning models are designed to recognize patterns in the data and automatically evolve in order to make accurate predictions. Yous can acquire more than near the cardinal similarities and differences between (human-led) predictive analytics and machine learning here.

Equally you tin run across, predictive analytics is used to forecast all sorts of time to come outcomes, and while information technology tin can never be one hundred percent accurate, it does eliminate much of the guesswork. This is crucial when it comes to making business decisions and determining the most appropriate course of activeness.

So: Predictive analytics builds on what happened in the by and why to predict what is likely to happen in the future.

4. Types of data analysis: Prescriptive (What's the all-time course of action?)

Prescriptive analytics looks at what has happened, why information technology happened, and what might happen in guild to decide what should exist done side by side. In other words, prescriptive analytics shows you how you can best take advantage of the future outcomes that have been predicted. What steps tin you take to avoid a future trouble? What can you do to capitalize on an emerging trend?

Prescriptive analytics is, without doubt, the most complex type of analysis, involving algorithms, car learning, statistical methods, and computational modeling procedures. Essentially, a prescriptive model considers all the possible determination patterns or pathways a company might take, and their likely outcomes.

This enables you to see how each combination of conditions and decisions might bear on the future, and allows you to measure the bear upon a certain conclusion might have. Based on all the possible scenarios and potential outcomes, the company can make up one's mind what is the all-time "route" or action to accept.

An oft-cited example of prescriptive analytics in action is maps and traffic apps. When figuring out the best manner to go you from A to B, Google Maps will consider all the possible modes of transport (e.1000. bus, walking, or driving), the current traffic weather and possible roadworks in club to calculate the best road. In much the same manner, prescriptive models are used to calculate all the possible "routes" a company might accept to reach their goals in order to make up one's mind the best possible pick.

Knowing what actions to take for the best chances of success is a major advantage for whatsoever blazon of organization, then it's no wonder that prescriptive analytics has a huge role to play in business.

So: Prescriptive analytics looks at what has happened, why information technology happened, and what might happen in social club to determine the all-time grade of action for the hereafter.

5. Key takeaways and further reading

In some ways, data analytics is a scrap like a treasure hunt; based on clues and insights from the past, y'all can work out what your next move should be. With the right type of analysis, all kinds of businesses and organizations can use their data to brand smarter decisions, invest more wisely, meliorate internal processes, and ultimately increase their chances of success. To summarize, at that place are four main types of data assay to exist aware of:

Descriptive analytics: What happened?
Diagnostic analytics: Why did it happen?
Predictive analytics: What is likely to happen in the future?
Prescriptive analytics: What is the best grade of action to take?

Now you're familiar with the different types of data analysis, you can offset to explore specific analysis techniques, such as time series analysis, cohort assay, and regression—to name merely a few! Nosotros explore some of the most useful information analysis techniques in this guide. If y'all're non already familiar, it'southward as well worth learning about the different levels of measurement (nominal, ordinal, interval, and ratio) for data.

Ready for a hands-on introduction to the field? Requite this free, five-twenty-four hour period data analytics short course a become! And, if yous'd like to learn more, cheque out some of these splendid complimentary courses for beginners. Then, to encounter what it takes to showtime a career in the field, check out the post-obit: