Loading...
You are here:  Home  >  Data Education  >  BI / Data Science News, Articles, & Education  >  BI / Data Science Articles  >  Current Article

Fundamentals of Descriptive Analytics

By   /  July 6, 2017  /  No Comments

descriptive analyticsDescriptive Analytics, the conventional form of Business Intelligence and data analysis, seeks to provide a depiction or “summary view” of facts and figures in an understandable format, to either inform or prepare data for further analysis. It uses two primary techniques, namely data aggregation and data mining to report past events. It presents past data in an easily digestible format for the benefit of a wide business audience.

A common example of Descriptive Analytics are company reports that simply provide a historic review of an organization’s operations, sales, financials, customers, and stakeholders. It is relevant to note that in the Big Data world, the “simple nuggets of information” provided by Descriptive Analytics become prepared inputs for more advanced Predictive or Prescriptive Analytics that deliver real-time insights for business decision making.

Descriptive Analytics helps to describe and present data in a format which can be easily understood by a wide variety of business readers. Descriptive Analytics rarely attempts to investigate or establish cause and effect relationships. As this form of analytics doesn’t usually probes beyond surface analysis, the validity of results is more easily implemented. Some common methods employed in Descriptive Analytics are observations, case studies, and surveys. Thus, collection and interpretation of large amount of data may be involved in this type of analytics.

In Descriptive, Predictive, and Prescriptive Analytics Explained, the author argues that both in Predictive and Prescriptive Analytics, the data analyst has to “investigate” beyond surface data. While the predictive data analyst uses investigation to understand the future, the prescriptive data analyst uses investigation to suggest probable actions. In contrast to both, the descriptive analyst simply offers the existing data in a more understandable format without any further investigation. Thus, Descriptive Analytics is more suited for a historical account or a summary of past data. Most statistical calculations are generally applied to Descriptive Analytics.

In Information Week’s Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive, Dr. Michael Wu, Chief Scientist of Lithium Technologies in San Francisco, describes Descriptive Analytics as the simplest form of Data Analytics, which captures Big Data in small nuggets of information. As Wu observes, 80% of Business Analytics falls within the ambit of Descriptive Analytics. Also, review the article 3 Types of Analytics: Descriptive, Predictive, and Prescriptive.

In this article on the different types of Data Analytics, the author hints that any good Data Scientist may try to use the results of Descriptive Analytics and further tweak the data or trends or pattern analysis to forecast future trends in business. The author notes that with the help of Big Data, all three types of Data Analytics are now used to better understand the customer. With huge amount of multi-channel customer data coming in, the data-driven businesses are far better positioned to gauge the individual preferences of customers and design appropriate personalized offerings. The majority of industry literature echoes the sentiment that with Predictive and Prescriptive Analytics, the business data that once simply described past events can now view “nuggets of useful information,” thanks to Big Data-powered Descriptive Analytics.

Examples of Descriptive Analytics

Here are some common applications of Descriptive Analytics:

  • Summarizing past events such as regional sales, customer attrition, or success of marketing campaigns.
  • Tabulation of social metrics such as Facebook likes, Tweets, or followers.
  • Reporting of general trends like hot travel destinations or news trends.

According to Four Types of Big Data Analytics and Examples of Their Use, as soon as the “volume, velocity, and variety” of Big Data invades the limited business data silos, the game changes. Now, powered by the hidden intelligence of massive amounts of market data, Descriptive Analytics takes new meaning. Whenever Big Data intervenes, vanilla-form Descriptive Analytics is combined with the extensive capabilities of Prescriptive and Predictive Analytics to deliver highly-focused insights into business issues and accurate future predictions based on past data patterns. Descriptive Analytics mines and prepares the data for use by Predictive or Prescriptive Analytics. Big Data lends a wide context to the “nuggets of information” for telling the whole story. Also view this presentation from Information Builders on four popular types of Business Analytics.

According to a recent Forbes study titled EY-Forbes-Insights: Data and Analytics Impact Index “people and culture” can influence the intelligence gathered from Business Analytics. This study conducted jointly by Forbes Insights and EY interviewed global executives and concluded that:

  • Every modern business needs to build its Data Analytics framework, where the latest data technologies like Big Data play a crucial role.
  • Data and technology should be made available at every corner of an enterprise to develop and nurture a widespread data-driven culture.
  • If data and analytics are aligned with overall business goals, then day-to-day business decisions will be more driven by data-driven insights.
  • As people drive businesses, the manpower engaged in Data Analytics must be competent and adequately trained to support enterprise goals.
  • A centrally managed team must lead the analytics production and consumption efforts in the enterprise to bring behavioral change towards a data culture.
  • The concept of Data Analytics must be spread through both formal data centers and informal social networks for an inclusive growth.

Descriptive Analytics: Industry Applications

In McKinsey’s 2016 Analytics Study Defines the future of Machine Learning, you will find that US retail(40%) industry and GPS-based services (60%) are showing 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.     

The Role of Descriptive Analytics in Future Data Analysis

As data-driven businesses continue to use the results from Descriptive Analytics to optimize their supply chains and enhance their decision-making powers, Data Analytics will move further away from Predictive Analytics toward Prescriptive Analytics or rather towards amash-up of predictions, simulations, and optimization.”

The future of Data Analytics lies in not only describing what has happened, but in accurately predicting what might happen in the future. This claim is explained in the article titled The Future of Analytics Is Prescriptive, Not Predictive. This article cites a GPS navigation system, where Descriptive Analytics is used to provide directional cues. However, such analysis is reinforced by “Predictive Analytics” offering important details about the journey like the time duration. Now, if the GPS system is further powered by Prescriptive Analytics, then the navigation system will not only provide directions and time, but also the quickest way to reach the destination. The best part of such a super-charged navigation system is that it can even compare several traveling routes and recommend the best solution.

As Data Mining and Machine Learning jointly offer solutions to predict customer segments and marketing ROIs, the future Predictive Analytics techniques will continue to evolve into Prescriptive Analytics, creating a mash-up of “predictions, simulations, and optimization.”

 

Photo Credit: everything possible/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

You might also like...

The Data Governance Playbook: Sixteen Steps to Better Data Privacy

Read More →