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4 Types of Data Analytics

Data analytics is a hot topic, but many executives are not aware that there are different categories for different purposes. Often, the best type of data analytics for a company to rely on depends on their particular stage of development. In this post, we will outline the 4 main types of data analytics. Let’s get started.

1. Predictive Data Analytics 

Predictive analytics may be the most commonly used category of data analytics as it is used to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling. But, it's important to know that these two really go hand in hand. 

Let's take a look at an example using an advertising campaign on Facebook for baked goods. Statistical modeling could be used to determine how closely conversion rate correlates with a target audience's geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) different target audiences and provide you with possible revenue values for each demographic. 

2. Prescriptive Data Analytics

Prescriptive analytics is where AI and big data meet to help predict outcomes and what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in machine learning, prescriptive analytics can help answer questions like "What if we try this?" and "What is the best action" without spending the time actually trying out each variable. Basically, it can help you test the right variables and even suggest new variables with a higher chance of generating a positive outcome.

3. Diagnostic Data Analytics

While not as sexy as some of the future data analytics, past data analytics serve an important purpose in guiding the business. Diagnostic data analytics is the process of examining data to understand cause and event, or why something happened. Techniques like drill-down, data discovery, data mining, and correlations are often employed.

In particular, diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two even more specific categories: discover and alerts and query and drilldowns. Query and drilldowns are what you'll use to get more detail from a report. For example, let's say that one of your sales reps closed significantly fewer deals last month. A drilldown could show fewer work days, reminding you that they had used 2 weeks vacation that month explaining the dip.

Discover and alerts can be used to be notified of a potential issue beforehand, such as alerting you to a low amount of man hours which could result in a dip in closed deals. You could also use diagnostic data analytics to “discover” information like who the best candidate for a new position at your company is.

4. Descriptive Data Analytics

Descriptive analytics are the backbone of reporting—it's impossible to have BI tools and dashboards without it. It addresses your basic how many, when, where, and what questions. Once again, this can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example of this a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts.

Ad hoc reports, on the other hand, are designed by you and usually aren't scheduled but are more in-the-moment. They're useful for obtaining more in-depth information about a specific query. An ad hoc report you might run could be on your social media profile looking at the types of people who've liked your page along with what other pages in your industry they've liked as well as any other engagement and demographic information. Its hyperspecificity helps give a fuller picture of your social media audience, and chances are you won't need to view this type of report a second time (unless there's a major change to your audience). 


Now that you've got a good idea of the four different types of data analytics, consider using their more descriptive category names within conversation and writing. Doing so can help to reduce the number of possible misunderstandings and help to deepen the knowledge of those around you of the different types of data analytics.

Additionally, your company is likely already using past data analytics, but it's important to note that this results in business decisions that are reactive rather than proactive. More and more businesses are going to be adopting future data analytics and thus will be able to make predictive choices. If you're not ahead of that curb you may find your business performance lacking as others in your industry begin to adopt and thus reap the rewards of adopting future data analytics.

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