DATA ANALYTICS:
Data analytics is the science of analysing raw data to make conclusions
about that information.
Data analytics help a business optimize its performance, perform
more efficiently, maximize profit, or make more strategically guided
decisions.
The techniques and processes of data analytics have been automated
into mechanical processes and algorithms that work over raw data for
human consumption.
Types of Data Analytics
There are four types of data analytics:
1. Predictive (forecasting)
2.Descriptive (business intelligence and data mining)
3.Prescriptive (optimization and simulation)
4.Diagnostic analytics
Predictive Analytics: Using predictive analytics, the data are
transformed into useful knowledge. Predictive analytics uses data to
estimate the chance of a condition arising or the likely course of an
occurrence.
In order to anticipate future events, predictive analytics uses a
number of statistical techniques from modelling, machine learning,
data mining, and game theory. These techniques examine both
current and past data. Techniques that are used for predictive
analytics are:
• Linear Regression
• Time series analysis and forecasting
• Data Mining
• There are three basic cornerstones of predictive analytics:
• Predictive modelling
• Decision Analysis and optimization
• Transaction profiling
Descriptive Analytics: In order to understand how to approach future
events, descriptive analytics examines data and analyses prior events.
,By analysing historical data, it examines prior performance and
analyses performance to determine what caused past success or
failure. This kind of analysis is used in almost all management
reporting, including that for sales, marketing, operations, and finance.
In order to categorize consumers or prospects into groups, the
descriptive model quantifies relationships in data. Descriptive
analytics uncovers a variety of interactions between the client and the
product, in contrast to predictive models that concentrate on
forecasting the behaviour of a specific customer.
Common examples of Descriptive analytics are company reports that
provide historic reviews like:
• Data Queries
• Reports
• Descriptive Statistics
• Data dashboard
Prescriptive Analytics: In order to produce a prediction, prescriptive
analytics automatically combine large data, mathematical science,
business rules, and machine learning. They then propose a choice
alternative to capitalize on the prediction.
Prescriptive analytics goes beyond forecasting outcomes by
additionally recommending actions that will benefit from the
forecasts and outlining the implications of each decision option for
the decision maker. In addition to predicting what will happen and
when prescriptive analytics also considers why it will happen.
Moreover, prescriptive analytics can recommend options on how to
seize a future opportunity or lessen a future risk, and it can also
explain the implications of each option.
Prescriptive analytics, for instance, can help strategic planning in the
healthcare industry by leveraging operational and consumption data
mixed with data from outside elements like the economy and
population demographics.
, Diagnostic Analytics: In this study, historical data is typically
preferred over other data when attempting to provide an answer or
resolve a query. We look for any dependencies and patterns in the past
data related to the specific issue.
Companies utilise this analysis, for instance, because it provides
significant insight into a problem. They also retain extensive records
at their disposal, as doing so would make data collection individual and
time-consuming for each problem.
Common techniques used for Diagnostic Analytics are:
• Data discovery
• Data mining
• Correlations
Data analytics relies on a variety of software tools ranging from
spreadsheets, data visualization, and reporting tools, data mining
programs, or open-source languages for the greatest data
manipulation. Data analytics techniques can reveal trends and metrics
that would otherwise be lost in the mass of information. This
information can then be used to optimize processes to increase the
overall efficiency of a business or system.
Example:
For example, manufacturing companies often record the runtime,
downtime, and work queue for various machines and then analyse the
data to better plan the workloads so the machines operate closer to
peak capacity.
Data analytics is the science of analysing raw data to make conclusions
about that information.
Data analytics help a business optimize its performance, perform
more efficiently, maximize profit, or make more strategically guided
decisions.
The techniques and processes of data analytics have been automated
into mechanical processes and algorithms that work over raw data for
human consumption.
Types of Data Analytics
There are four types of data analytics:
1. Predictive (forecasting)
2.Descriptive (business intelligence and data mining)
3.Prescriptive (optimization and simulation)
4.Diagnostic analytics
Predictive Analytics: Using predictive analytics, the data are
transformed into useful knowledge. Predictive analytics uses data to
estimate the chance of a condition arising or the likely course of an
occurrence.
In order to anticipate future events, predictive analytics uses a
number of statistical techniques from modelling, machine learning,
data mining, and game theory. These techniques examine both
current and past data. Techniques that are used for predictive
analytics are:
• Linear Regression
• Time series analysis and forecasting
• Data Mining
• There are three basic cornerstones of predictive analytics:
• Predictive modelling
• Decision Analysis and optimization
• Transaction profiling
Descriptive Analytics: In order to understand how to approach future
events, descriptive analytics examines data and analyses prior events.
,By analysing historical data, it examines prior performance and
analyses performance to determine what caused past success or
failure. This kind of analysis is used in almost all management
reporting, including that for sales, marketing, operations, and finance.
In order to categorize consumers or prospects into groups, the
descriptive model quantifies relationships in data. Descriptive
analytics uncovers a variety of interactions between the client and the
product, in contrast to predictive models that concentrate on
forecasting the behaviour of a specific customer.
Common examples of Descriptive analytics are company reports that
provide historic reviews like:
• Data Queries
• Reports
• Descriptive Statistics
• Data dashboard
Prescriptive Analytics: In order to produce a prediction, prescriptive
analytics automatically combine large data, mathematical science,
business rules, and machine learning. They then propose a choice
alternative to capitalize on the prediction.
Prescriptive analytics goes beyond forecasting outcomes by
additionally recommending actions that will benefit from the
forecasts and outlining the implications of each decision option for
the decision maker. In addition to predicting what will happen and
when prescriptive analytics also considers why it will happen.
Moreover, prescriptive analytics can recommend options on how to
seize a future opportunity or lessen a future risk, and it can also
explain the implications of each option.
Prescriptive analytics, for instance, can help strategic planning in the
healthcare industry by leveraging operational and consumption data
mixed with data from outside elements like the economy and
population demographics.
, Diagnostic Analytics: In this study, historical data is typically
preferred over other data when attempting to provide an answer or
resolve a query. We look for any dependencies and patterns in the past
data related to the specific issue.
Companies utilise this analysis, for instance, because it provides
significant insight into a problem. They also retain extensive records
at their disposal, as doing so would make data collection individual and
time-consuming for each problem.
Common techniques used for Diagnostic Analytics are:
• Data discovery
• Data mining
• Correlations
Data analytics relies on a variety of software tools ranging from
spreadsheets, data visualization, and reporting tools, data mining
programs, or open-source languages for the greatest data
manipulation. Data analytics techniques can reveal trends and metrics
that would otherwise be lost in the mass of information. This
information can then be used to optimize processes to increase the
overall efficiency of a business or system.
Example:
For example, manufacturing companies often record the runtime,
downtime, and work queue for various machines and then analyse the
data to better plan the workloads so the machines operate closer to
peak capacity.
