Best Technology Blogs and Tutorials – Lesson91
With
changing time and business requirements, data analytics had been one the most
considerable matters for smooth running business processes and steady growth
even amidst unsteady trends and conditions. Keeping in mind this present day
requirement we are going to follow up with some basic guidelines in respect to
data analysis and its consequences for your business.
What isData Analytics?
Also
known as Data Analysis, Data analytics refers to the quantitative and qualitative
techniques and procedures meant to boost business gains and productivity. In
order to analyze and identify behavioral data and pattern, data is first
extracted and categorized. However, techniques for doing so are known to vary
as per various organizational requirements.
To
fetch the best of something it is often necessary to move to the depths just as
the words quoted by John Dryden say “He who would search for pearls must dive
below”. Therefore, fact-based insights for this topic are also necessary so
that you can make out the most from various data analytics tools for your
pacing up your business’s productivity.
Types ofData Analytics
Ranging
from simple to more complicated and sophisticated, there are 4 different types
of data analytics. More complex the analytics is the better value it is known
to bring. Well, we are here going to discuss all these types of data analysis
starting with the simplest and moving further to the most complex.
Descriptive Analytics
Descriptive
analytics is suitable for finding appropriate answers to what happened?
Consider a healthcare provider for instance; he will search for how many
patients were hospitalized during the last month. Similarly, a manufacturer
would look for a rate of products that returned for past month and a retailer
would strive for average weekly sales.
For
a clearer example, based on factors like monthly revenue for each product,
revenue analysis, income be product group and quality of things produced per
month, a manufacturer would focus on a particular product category.
Descriptive
analysis is considerate about organizing raw data from different sources to
fetch out valuable insights from the past. However, these researches just let
you figure out whether something went wrong or right. The reason behind the
happening is not the focus here. This is hence not suitable for companies that
are highly data-driven. Alternatively, companies look in to combine descriptive
analytics with other types of data analysis.
Diagnostic Analytics
This
category of data analysis moves to a higher level of complexity for bringing up
the answers to why something happened? Under this historical data is compared
with other data in order to find the appropriate reasons for a happening. Diagnostic
analytics helps to drill down deep and identify patterns and fetch out
dependencies.
Diagnostic
analytics is the best when you are looking for deeper insights into a
considered issue. At the same time, it is important that the company holds
detailed information for clearance on their part. Otherwise, individual data
collection for all different issues will make the process time-consuming.
Let’s
again consider an example from different industries; a healthcare provider
would compare the response of patients from various promotional camps held in
different regions. Similarly, a retailer would run down the sales as per
different subcategories. All this would consider measuring the consequences of
something that has taken place.
Predictive Analytics
As
the name suggests, predictive analytics look forward to predicting and
answering the future interrogations of what would likely happen? The results
fetched through descriptive and diagnostic analytics are gathered here in order
to identify clusters, exceptions, and tendencies. Being highly predictable for
future tendencies, this is among the ideal data analytics tools for companies
for forecasting.
Despite
the numerous advantages that prevail with predictive analysis, it is important
to recognize that forecasting is just an estimate and estimates are not
guaranteed for being accurate. The accuracy of estimates is determined by the
quality of data and stability of the situation. Therefore, it is important to
consider continuous optimization and careful treatment as the major elements.
The proactive approach of predictive data analytics makes the go easier.
Consider
the example of a telecom company; they will try to identify the number of users
who are likely to reduce their expenses to carry forward the target marketing
activities for the same. A management team will recognize the risks involved in
investments for the company’s expansion with the help of forecasting and cash
flow analysis.
Prescriptive Analytics
Does
prescriptive analytics focus on finding the next step to be taken answering
questions like what action to perform? With a view of optimum utilization of
promising trends or eliminating a problem that would hold power in future this
is an important element of analysis. For instance, with the help of customer
analytics and sales records, a multinational company can easily identify
opportunities and trends for repeat purchases and take a further suitable step.
This
up-to-the-mark data analysis technique requires historical data along with
other external information as per the respective state of statistical
algorithms. Besides this, prescriptive analytics utilizes sophisticated
technologies and tools including algorithms, business rules, and machine
learning. Things, therefore, become easier to manage and implement. Before a
company actually adopts prescriptive analytics, expected added value and
required efforts should be compared.
In
order to identify whether there had been any analysis trends, we will have to
have a look at the results of several recent surveys.
As
per the Global Data and Analysis survey, over 2,000 employees were asked about
the most appropriate category that can well define the decision-making process
of their company. Also, they were asked the type of analytics they rely the
most on. The results for different categories were as below:
As
per the analysis from the survey, at different stages of a company’s
development, there may be a need for one or more types of data analytics
models. Moreover, the companies striving for detailed decision-making would
find Descriptive analytics to be deficient hence they will have to add up
diagnostic and predictive analytics to the list.
There
is a different face to the results of the same survey. Executives looking for
sophisticated and faster decision-making are increasing and this would
gradually increase the preference for predictive analytics among different
companies.
Conclusion
There are varied types of
analytics and companies are hence free to choose their sphere of work and the
depths till which they need to dive into an analysis. They can pick the one
which allows them to satisfy business needs in the most appropriate manner.
On one hand where descriptive
and diagnostic analytics allow working with a reactive approach, predictive and
prescriptive analytics avail proactive approach for the users. However, as per
current trends, more and more companies are posed with the need to adopt
advanced data analytics and are known to adopt it.
Proper
business analytics has to go through different business processes. Depending on
the workflow and requirement for analysis companies have had varied preferences
for data analytics considering the four major types namely - Descriptive,
Diagnostic, Predictive and Prescriptive Analytics.
With
changing time and business requirements, data analytics had been one the most
considerable matters for smooth running business processes and steady growth
even amidst unsteady trends and conditions. Keeping in mind this present day
requirement we are going to follow up with some basic guidelines in respect to
data analysis and its consequences for your business.
What isData Analytics?
Also
known as Data Analysis, Data analytics refers to the quantitative and qualitative
techniques and procedures meant to boost business gains and productivity. In
order to analyze and identify behavioral data and pattern, data is first
extracted and categorized. However, techniques for doing so are known to vary
as per various organizational requirements.
To
fetch the best of something it is often necessary to move to the depths just as
the words quoted by John Dryden say “He who would search for pearls must dive
below”. Therefore, fact-based insights for this topic are also necessary so
that you can make out the most from various data analytics tools for your
pacing up your business’s productivity.
Types ofData Analytics
Ranging
from simple to more complicated and sophisticated, there are 4 different types
of data analytics. More complex the analytics is the better value it is known
to bring. Well, we are here going to discuss all these types of data analysis
starting with the simplest and moving further to the most complex.
Descriptive Analytics
Descriptive
analytics is suitable for finding appropriate answers to what happened?
Consider a healthcare provider for instance; he will search for how many
patients were hospitalized during the last month. Similarly, a manufacturer
would look for a rate of products that returned for past month and a retailer
would strive for average weekly sales.
For
a clearer example, based on factors like monthly revenue for each product,
revenue analysis, income be product group and quality of things produced per
month, a manufacturer would focus on a particular product category.
Descriptive
analysis is considerate about organizing raw data from different sources to
fetch out valuable insights from the past. However, these researches just let
you figure out whether something went wrong or right. The reason behind the
happening is not the focus here. This is hence not suitable for companies that
are highly data-driven. Alternatively, companies look in to combine descriptive
analytics with other types of data analysis.
Diagnostic Analytics
This
category of data analysis moves to a higher level of complexity for bringing up
the answers to why something happened? Under this historical data is compared
with other data in order to find the appropriate reasons for a happening. Diagnostic
analytics helps to drill down deep and identify patterns and fetch out
dependencies.
Diagnostic
analytics is the best when you are looking for deeper insights into a
considered issue. At the same time, it is important that the company holds
detailed information for clearance on their part. Otherwise, individual data
collection for all different issues will make the process time-consuming.
Let’s
again consider an example from different industries; a healthcare provider
would compare the response of patients from various promotional camps held in
different regions. Similarly, a retailer would run down the sales as per
different subcategories. All this would consider measuring the consequences of
something that has taken place.
Predictive Analytics
As
the name suggests, predictive analytics look forward to predicting and
answering the future interrogations of what would likely happen? The results
fetched through descriptive and diagnostic analytics are gathered here in order
to identify clusters, exceptions, and tendencies. Being highly predictable for
future tendencies, this is among the ideal data analytics tools for companies
for forecasting.
Despite
the numerous advantages that prevail with predictive analysis, it is important
to recognize that forecasting is just an estimate and estimates are not
guaranteed for being accurate. The accuracy of estimates is determined by the
quality of data and stability of the situation. Therefore, it is important to
consider continuous optimization and careful treatment as the major elements.
The proactive approach of predictive data analytics makes the go easier.
Consider
the example of a telecom company; they will try to identify the number of users
who are likely to reduce their expenses to carry forward the target marketing
activities for the same. A management team will recognize the risks involved in
investments for the company’s expansion with the help of forecasting and cash
flow analysis.
Prescriptive Analytics
Does
prescriptive analytics focus on finding the next step to be taken answering
questions like what action to perform? With a view of optimum utilization of
promising trends or eliminating a problem that would hold power in future this
is an important element of analysis. For instance, with the help of customer
analytics and sales records, a multinational company can easily identify
opportunities and trends for repeat purchases and take a further suitable step.
This
up-to-the-mark data analysis technique requires historical data along with
other external information as per the respective state of statistical
algorithms. Besides this, prescriptive analytics utilizes sophisticated
technologies and tools including algorithms, business rules, and machine
learning. Things, therefore, become easier to manage and implement. Before a
company actually adopts prescriptive analytics, expected added value and
required efforts should be compared.
WhatTypes Of Data Analysis Do Companies Prefer?
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