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Chapter 8: Descriptive Analytics
Descriptive Analytics- What is It?
Businesses use descriptive analytics all the time, whether they are aware of
it or not. It’s often called business intelligence. Companies these days have
vast amounts of data available to them, so they would do well to use
analytics to interpret this data to help them with decision making. It helps
them to learn from what happened in the past, and enables them to try to
accurately predict what may happen in the future. In other words, analytics
helps companies anticipate trends. For instance, if sales increased in
November for the past five years, and declined in January, after the
Christmas rush, then one could predict that the same thing is likely to
happen in year six and prepare for it. Companies could use this to perhaps
increase their marketing in January, offering special offers and other
incentives to customers.
Descriptive analytics give insight into what happened in the past (this may
include the distant past, or the recent past, like sales figures for last week.)
They summarize data that describes these past events and make it simple
for people to understand. In a business, for example, we may look at how
sales trends have changed from year to year, or how production costs have
escalated.
Descriptive statistics, basically then, is the name given to the analysis of
data that helps to show trends and patterns that emerge from collected
information. They are a way of describing the data in a way that helps us to
visualize what the data shows.
This is especially helpful where there has been a lot of information
collected. Descriptive statistics are the actual numbers that are used to
describe the information that has been collected from, say, a survey.
Let’s say, for instance, that we issued proficiency tests to all 200 of our
company employees. From their results, we could work out the mean and
the standard deviation. The group of data, which includes all the
information of interest to management, is called a population. It may be big
or small, provided that it includes all the information we’re interested in. In
our example, we’re examining 200 employees, so they are our population.
The properties of the population, such as the mean test result, and the
median, are called parameters.
Perhaps we’re actually interested in the proficiency test results of everyone
employed in the same sector of the industry across the world. It’s not really
possible to obtain such data, so we’d have to use a sample of our 200
employees to represent the entire industry. The properties of this sample
would then not be called parameters, but rather, statistics.
By using what is called inferential statistics, we can use the sample to infer
things about the entire group of employees across the world. The sample
must be an accurate representation of the whole group for this to work.
Obviously, mistakes will be made as a sample never perfectly represents the
population.
How Can Descriptive Analysis Be Used?
Descriptive analysts are able to make information easier to understand and
therefore to use. They do this by turning it into graphs, charts, or pictorial
representations of what has been happening. This way, management and
employees alike can see what has been happening within the company in
the past, and make useful predictions and therefore good decisions for the
future.
External data related to the company may also be used, such as stock
market trends, or international events that may affect this particular
business- for instance, an oil crisis in OPEC at the end of last year will have
an indirect but tangible effect on a trucking transport company in the US
early the following year. The company can use this information to perhaps
stockpile fuel, or take on less labor in the new year.
Based on probabilities, the company takes existing data and fills in what’s
missing with educated guesses. The historical data will be combined,
patterns identified, and then algorithms applied to find the relationships
between the sets of data. The company may then be able to predict
customer behavior such as spending patterns. This will then help them
ensure that the supply chain can keep up with the demand.
Measures in Descriptive Statistics
Descriptive statistics are so-called because they help to describe the data
which has been collected. They are a way of summarizing big groups of
numerical information, by summarizing a sample. (In this way, descriptive
statistics are different from inferential statistics, which uses data to find out
about the population that the data is supposed to represent.)
Two types of statistics are most often used in this descriptive process, and
these are measures of central tendency, and measures of dispersion.
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