Descriptive Analytics | Understanding the Past. Inferring the Future.
Descriptive analyses or statistics do precisely what the phrase infers; they “describe”. Descriptive analysis summarizes raw data and makes that data easily deciphered. It describes the past, where the past refers to any point of time when an event has occurred, whether it was one minute ago, or one year ago. The technique uses data aggregation and data mining to provide insight into the past and answer the question, “What has happened?” This, in turn, helps us understand how the past might influence future outcomes.
Descriptive statistics are valuable to describe items like total stock in inventory, average dollars spent per customer, and year-over-year change in sales. Common instances of descriptive analytics are reports that provide chronicled bits of insights in to a company’s production, financials, operations, sales, finance, inventory, and clients. Descriptive analytics can be utilized when we have to comprehend, at an aggregate level, what is happening in the organization, and when we want to outline and portray different aspects of the business.
When Should You Use Descriptive Analytics?
Descriptive analysis is an appropriate way to understand attributes of particular data. Deeper analysis provides the following:
- It estimates and outlines the data by organizing it in tables and graphs to help meet targets
- It provides information about the fluctuation or vulnerability of the data
- It provides indications of unexpected patterns and perceptions that should be considered when doing formal analysis
Basic Principles of Descriptive Analytics
Data given by descriptive analytics end up as prepared inputs for further developed predictive or prescriptive analytics that deliver real-time insights for business decision making. Descriptive analytics seldom endeavors to explore or set up circumstances and connections to end-results. Some of the common methods employed in descriptive analytics are observations, case studies, and surveys. Accumulation and translation of a substantial amount of data is involved in this type of analytics, with most statistical calculations generally being applied to descriptive analytics.
Descriptive Analytics Illustrations
Here are some common applications of descriptive analytics:
- Summarizing past events such as territorial customer attrition, sales, or success of marketing campaigns.
- Tabulating of social media metrics such as Facebook preferences, tweets, or followers.
- In an analytics study conducted by McKinsey in 2016, the US retail (40%) industry and GPS-based services (60%) showed rapid adoption of descriptive analytics to track teams, customers, and assets across locations to capture enhanced insights for operational efficiency. McKinsey also claimed that in today’s business climate, the three most critical barriers to data analytics are lack of organizational strategy, lack of involved management, and lack of available talent. Another report suggests that descriptive analytics has made great strides in Supply Chain Mapping (SCM), manufacturing plant sensors, and GPS vehicle tracking, to gather, organize, and view past events.
- Investors and brokers perform analytical and empirical analysis on their investments, which helps them in settling on better investment decisions in the future.
- Descriptive analysis can also be called post-mortem analysis. It is utilized for almost all administration reporting, such as marketing, sales, finance, and operations. To gain the competitive edge, organizations utilize advanced analytics, which likewise underpins them in estimating future trends. The forecasting allows companies to make optimized decisions, thus increasing their profitability.
Descriptive analytics can be utilized in future analysis as data-driven organizations keep on using the outcomes from descriptive analytics to optimize their supply chains and improve their decision-making power. Data analytics will now, however, move further away from predictive analytics toward prescriptive analytics, or rather, towards a “blend of forecasts, simulations, and optimization.”
Mastech InfoTrellis – a pioneering data and analytics company – is a leader in the implementation and use of advanced analytics applications across a wide array of industries. Get in touch today to learn more about how you can gain actionable insights through an analytics hub, and thus, improve your organization’s decision-making capabilities.