Descriptive analytics are a very basic form of analytics that notify companies of past events and patterns. More than 90 percent of companies use descriptive analytics, according to CI&T.
Along with more advanced types of analytics like predictive analytics and prescriptive analytics, descriptive analytics use big data to provide business solutions in virtually any industry. Descriptive analytics are an important first step to computing data and understanding how data should be applied to a company.
What Are Descriptive Analytics?
“The purpose of descriptive analytics is simply to summarize and tell you what happened,” says Michael Wu, chief scientist at Lithium. Descriptive analytics are “mostly based on standard aggregate functions [like average, maximum and mode] in databases that require nothing more than grade school math. Even basic statistics are pretty rare.”
Most descriptive analytics can be classified into three categories.
- Event counters like number of posts and followers on social media.
- Simple mathematical operations like average response time and average number of replies per post.
- Filtered analytics, such as average posts per week from the United Kingdom vs. average posts per week from Japan.
Descriptive analytics can reveal key performance indicators and details such as the frequency of events, the cost of operations and the root cause of failures, according to a white paper from IBM. The information can be displayed within a report or dashboard view, or companies can set up automated solutions that issue alerts when potential problems arise.
Business Solutions for Descriptive Analytics
Descriptive analytics can benefit managers by showing basic data in charts or reports, Decision Line explains. These documents help answer questions for budgeting, sales, revenue and cost. “How much did we sell in each region? What was our revenue and profit last quarter? How many and what types of complaints did we resolve? Which factory has the lowest productivity? Descriptive analytics also help companies to classify customers into different segments, which enable them to develop specific marketing campaigns and advertising strategies,” Decision Line says.
The Dow Chemical company used descriptive analytics to increase facility utilization across its office and lab spaces globally. The company identified under-utilized space, ultimately increasing facility use 20 percent and generating an annual savings of approximately $4 million in space consolidation, IBM reports.
Wal-Mart mines terabytes of new data each day and pentabytes of historical data to uncover patterns in sales, according to DeZyre. Wal-Mart analyzes millions of online search keywords and hundreds of millions of customers from different sources to look at certain actions. For instance, Wal-Mart examines what consumers buy in store and online, what’s trending on Twitter and how the World Series and weather affect buying patterns.
Moving From Descriptive Analytics
Descriptive analytics are important and useful, but their application is limited. Once past events and patterns are understood, it’s natural to want to use that information to predict what will most likely happen and what a company should do. Companies must make the transition from descriptive analytics to predictive and prescriptive analytics to make the most of their data.
For instance, human resources analytics can examine how long certain employees have stayed with a company, their salary, how many days they were absent in a year and compare it to performance. Using simple demographic and performance indicators can help predict how long an employee with certain qualities will stay with the company, iNostix explains. Companies can also establish best practices based on these insights.
Historical data from Wal-Mart shows that before a hurricane, items besides tools like flashlights are in demand. “We didn’t know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane,” Linda Dillman, former chief information officer at Wal-Mart and now chief information officer at QVC, told The New York Times. Wal-Mart now places Pop-Tarts at checkouts before a hurricane.
The Dramatic Rise of Career Opportunities in Big Data
The United States faces a shortage of 140,000 to 190,000 people with deep analytical skills, according to McKinsey & Company. There could also be a shortage of 1.5 million managers and analysts who are able to use big data to make effective decisions.
“Job postings seeking data scientists and business analytics specialists abound these days,” Management Information Systems Quarterly says. “There is a clear shortage of professionals with the ‘deep’ knowledge required to manage the three V’s of big data: volume, velocity, and variety. There is also an increasing demand for individuals with the deep knowledge needed to manage the three ‘perspectives’ of business decision making: descriptive, predictive, and prescriptive analytics.”