Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life

Chapter 13: Descriptive and predictive
analysis
 
Every company will be at an advantage if they have a powerful intelligence
department. With the massive amounts of data available to businesses,
organizations are turning to analytics solutions to make sense of all this
information to help improve decision making. It is an enormous benefit for
any organization to have skilled personnel who can assemble and classify
large amounts of data since this information can have critical importance to
business. This class of analysis examines business on a macro level to
scrutinize quantities of data and identify values that could be used for future
reference. Companies need capabilities to analyze data to be able to forecast
future trends. The intelligence gathered will only have significance if the
team understands and applies the data to make calculations through
methods of descriptive or predictive analysis.
 
 Descriptive and predictive analysis are two approaches of data analysis in
business. Descriptive analytics can tell you what has happened or what is
happening now. Predictive analysis asks questions about the future. This
type of analytic forecasting constructs investigative outlines on the various
stages of industry. These forecasts may comprise of intelligence regarding a
consumer or commodity that will make business decisions clear. Using
information such as customer and employer behavior, the highs and lows of
markets, and other such related data, accurate judgements can be made for
prospective business direction.
 
When there is a need to get a comprehensive picture of what is going on in
the organization, descriptive analysis is applied. It describes the past and
summarizes basic data that describes different aspects of the business.
Reports provide information about sales, customers, operations, finance,
and correlations between the data. Questions about previous business
dealings, details of product users, the status of certain outlets, and sales

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indicators of supplies may be answered. Through this system, the company
will be aware of bestselling products, average salary of customers, annual
spending on niche products, and sales of seasonal commodities. All
information is constructed from data of former activity.
 
Predictive analytics is used when information about forthcoming
commercial activity is required. It provides organizations with actionable
insights based on data, and estimates future outcomes. It predicts customer
behavior in sales and marketing, demands for operation, or determines risk
factors for finance. Predictive analytics will show appropriate charges for a
certain product, the upgrades customers want, and whether customers will
buy a certain product. Such information allows focus on the creation of
products that will influence future growth and raise of business revenues.
Predictive analytics has become an important aspect part of big data since it
allows organizations to predict future outcomes.
 
Both descriptive and predictive approaches to data analytics are important.
Descriptive analytics is used to understand data and tells you what has
happened whereas predictive analytics forecasts techniques and tells you
what will happen. Neither form of analysis may be more important  
because they are both codependent and interlinked, each dependent on the
other. Without feedback from descriptive statistics, predictions would be
impossible. Without these predictions, descriptive information would be
useless. Both types of analytics are useful business supports because they
allow learning from past behaviors and understanding about influencing
future outcomes.
 

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