Types of Data Analytics

Data analytics is the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

There are tools, frameworks, and software to analyze data, such as Microsoft Excel and Power BI, Google Charts, Data Wrapper, Infogram, Tableau, and Zoho Analytics.

Algorithms and machine learning also fall into the data analytics field and can be used to gather, sort, and analyze data at a higher volume and faster pace than humans can. Writing algorithms is a more advanced data analytics skill, but one doesn't need deep knowledge of coding and statistical modeling to experience the benefits of data-driven decision making.

4 KEY TYPES OF DATA ANALYTICS

1. Descriptive Analytics

Descriptive analytics is the simplest type of analytics and the foundation the other types are built on. It allows to pull trends from raw data and succinctly describe what happened or is currently happening.

Descriptive analytics answers the question, “What happened?”

For example, while analyzing company’s data and one finds there’s a seasonal surge in sales for one of the products: a laptop. Here, descriptive analytics can tell , “This laptop experiences an increase in sales in October, November, and early December each year.”

Data visualization is a natural fit for communicating descriptive analysis because charts, graphs, and maps can show trends in data—as well as dips and spikes—in a clear, easily understandable way.

2. Diagnostic Analytics

Diagnostic analytics addresses the next logical question, “Why did this happen?”

Taking the analysis a step further, this type includes comparing coexisting trends or movement, uncovering correlations between variables, and determining causal relationships where possible.

Continuing the aforementioned example, one may dig into laptop users’ demographic data and find that they’re between the ages of 15 and 20 . The customers, however, tend to be between the ages of 40 and 55. Analysis of customer survey data reveals that one primary motivator for customers( adults) to purchase the laptop is for personal use for their children. The spike in sales in the fall and early winter months may be due to the holidays that include gift-giving.

Diagnostic analytics is useful for getting at the root of an organizational issue.

3. Predictive Analytics

Predictive analytics is used to make predictions about future trends or events and answers the question, “What might happen in the future?”

By analyzing historical data in tandem with industry trends, one can make informed predictions about what the future could hold for the company.

For instance, knowing that laptop sales have spiked in October, November, and early December every year for the past decade provides ample data to predict that the same trend will occur next year. Backed by upward trends in the laptop industry as a whole, this is a reasonable prediction to make.

Making predictions for the future can help the organization formulate strategies based on likely scenarios.

4. Prescriptive Analytics

Finally, prescriptive analytics answers the question, “What should we do next?”

Prescriptive analytics takes into account all possible factors in a scenario and suggests actionable takeaways. This type of analytics can be especially useful when making data-driven decisions.

Rounding out the laptop example: What should a team decide to do given the predicted trend in seasonality due to winter gift-giving? Perhaps one decides to run an A/B test with two ads: one that caters to product end-users (children/youth) and one targeted to customers (adults ). The data from that test can inform how to capitalize on the seasonal spike and its supposed cause even further. Or, maybe one decides to increase marketing efforts in September with holiday-themed messaging to try to extend the spike into another month. While manual prescriptive analysis is doable and accessible, machine-learning algorithms are often employed to help parse through large volumes of data to recommend the optimal next step. Algorithms use “if” and “else” statements, which work as rules for parsing data. If a specific combination of requirements is met, an algorithm recommends a specific course of action. While there’s far more to machine-learning algorithms than just those statements, they—along with mathematical equations—serve as a core component in algorithm training.

USING DATA TO DRIVE DECISION-MAKING

The four types of data analysis should be used in tandem to create a full picture of the story data tells and make informed decisions. To understand company's current situation, use descriptive analytics. To figure out how the company got there, leverage diagnostic analytics. Predictive analytics is useful for determining the trajectory of a situation—will current trends continue? Finally, prescriptive analytics can help one to consider all aspects of current and future scenarios and plan actionable strategies.