Descriptive Analytics

Descriptive Analytics

Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. Data mining and data aggregation methods organize the data and make it possible to identify patterns and relationships in it that would not otherwise be visible. Querying, reporting and data visualization may be applied to yield more insight.

Through descriptive analytics we can:

Detect

Detect, for example, which products or services are selling better and in what regions.

Visualize

Visualize, for example, where are the people who are writing about a specific topic through social media and how influential they are.

Observe

Observe, for example, the historical evolution of the demands of a particular product or service, for different time periods.

Identify

Identify, for example, which news are becoming trending topics in social media.

Calculate

Calculate and visualize, for example, different KPIs that summarize the state of the business and warn of possible problems.

Reveal

Find out, for example, which employees have better performance and how influential they are across the company.

A Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively a company is achieving key business objectives. Organizations use KPIs at multiple levels to evaluate their success at reaching targets. High-level KPIs may focus on the overall performance of the enterprise, while low-level KPIs may focus on processes in departments such as sales, marketing or a call center.

How does it work?

Descriptive analytics is based on working on a storage system where all the relevant business data is concentrated. This system can be a medium of files distributed Hadoop-style and derivatives, NoSQL databases, or more traditional SQL systems; it depends on the quantity and complexity of the data to be managed. On this storage layer, data processing technologies are deployed, both in batch mode and online, so that the aggregations and queries necessary for the analysis can be made.

Once the technological platform is deployed, different data visualization strategies are applied to summarize the state of the business. In collaboration with the client, a series of key metrics or KPIs calculated on the data can be defined, either to be visualized or to define a series of rules about them, in such a way that they generate automatic warnings when they deviate from their expected values.

Case Study: Coca-Cola Enterprises: The Thirst for HR Analytics Grows

There are numerous examples where the HR reporting and analytics team have partnered with the HR function and provided insights that have helped to develop more impactful processes and deliver greater outcomes for the business. As with many organisations it is the engagement data with which the majority of HR insight is created. Developing further insight beyond standard survey outputs has meant that CCE has begun to increase the level of insights developed through the method, and by using longitudinal data they have started to track sentiment in the organisation.

Benefits of descriptive analytics

Examples
Descriptive analytics can benefit managers by showing basic data in charts or reports. 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 organisations to classify customers into different segments, which enable them to develop specific marketing campaigns and advertising strategies.

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.

Wal-Mart mines terabytes of new data each day and pentabytes of historical data to uncover patterns in sales. 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 much stronger in combination with predictive and prescriptive analytics. 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. Organisations can also establish best practices based on these insights.

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