What is data analytics?
A broad definition of analytics is the review of data to discover, understand, and communicate meaningful patterns. Or more simply, analytics is the useful insights from raw data. Analytics can refer to program or product specific insights such as Google Analytics, Facebook Analytics, Twitter Analytics, etc.
But there are four broad types of analytics that can be applied to generally any data you’re working with. These types are descriptive, diagnostic, predictive, and prescriptive analytics. (The most common are descriptive, predictive, and prescriptive.) Each one builds on the previous one.
What is the difference between descriptive, diagnostic, predictive, and prescriptive analytics?
Descriptive analytics defined
Descriptive analytics, as the name implies, describes what has happened in the past, which is sometimes referred to as ‘historical data.’ Descriptive analytics answers the question “What has happened?”
Descriptive analytics examples
A vast number of analytics fall into this category. Examples of descriptive analytics include number of sales last year, variance in churn rate month over month, average revenue per customer, etc. Basically, any instance where raw data from the past (1 minute or 1 year ago) is summarized can be classified as descriptive analytics.
Diagnostic analytics defined
Diagnostic analytics focuses on the underlying cause and is less common than the other three types of analytics. It drills down to a single issue or problem in isolation. Diagnostic analytics answers the question “Why is this happening?”
Diagnostic analytics examples
Some diagnostic analytics examples include a marketing manager reviewing a campaign performance of different geographical regions, a sales director analyzing the number of sales for each product, or a customer success team looking at the response time of customers who churn.
Predictive analytics defined
Predictive analytics is a calculated guess of what may happen in the future based on past performance. While it can’t actually predict the future, it creates a forecast using algorithms that factor in past performance (descriptive analytics) and other possible variables. Predictive models create a picture of the future that can then be used to make more informed, data-backed decisions (see prescriptive analytics below). Predictive analytics answers the question “What is likely to happen?”
Predictive analytics examples
Some common examples of predictive analytics are budgets that forecast expenses for the upcoming year, a credit score that estimates the likelihood of someone making on-time payments in the future, and revenue predictions that help executives understand how much profit might be possible for the business.
Prescriptive analytics defined
Prescriptive analytics takes what’s likely to happen (predictive analytics) and suggests strategies or actions moving forward. It’s advice for achieving a possible outcome. Prescriptive analytics answers the question “What should we do?”
Prescriptive analytics examples
Prescriptive analytics examples include optimizing supply chain management based on demand trends (predictive analytics), suggesting the fastest route home based on driving conditions, or planning employee work shifts based on the busiest times for a restaurant or retailer.
What’s the difference between analytics and analysis?
There’s an interesting distinction between analytics and analysis. Analytics focuses on the entire methodology (i.e. the tools and techniques) for obtaining useful insights from data. Data analysis is a subset of that methodology focused on compiling and reviewing data to aid in decision making.
Additional resources to learn more about descriptive, diagnostic, predictive, and prescriptive analytics
Want to learn more about algorithms and how they impact the way we live and conduct business? Check out these resources for more in-depth study.