4 Key Types of Data Analytics
1.
Descriptive Analytics
Descriptive
analytics is the simplest type of analytics and the foundation upon which the
other types are built. It enables you to extract trends from raw data and
provide a concise description of what has happened or is currently happening.
Descriptive
analytics answers the question, “What happened?”
For
example, imagine you’re analyzing your company’s data and find there’s a
seasonal surge in sales for one of your products: a video game console. Here,
descriptive analytics can tell you, “This video game console 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 movements,
uncovering correlations between variables, and determining causal relationships
where possible.
Continuing
the aforementioned example, you may dig into video game console users’
demographic data and find that they’re between the ages of eight and 18. The
customers, however, tend to be between the ages of 35 and 55. Analysis of
customer survey data reveals that one primary motivator for customers to
purchase the video game console is to gift it to 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, you can make informed
predictions about what the future could hold for your company.
For
instance, knowing that video game console sales have spiked in October,
November, and early December every year for the past decade provides you with
ample data to predict that the same trend will occur next year. Backed by
upward trends in the video game industry as a whole, this is a reasonable
prediction to make.
Making
predictions for the future can help your 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 video game example: What should your team decide to do given the
predicted trend in seasonality due to winter gift-giving? Perhaps you decide to
run an A/B test with two ads: one that caters to product end-users (children)
and one targeted to customers (their parents). The data from that test can
inform how to capitalize on the seasonal spike and its supposed cause even
further. Or, maybe you decide 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.
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