Prescriptive Analytics Overview

Summary

The video script delves into prescriptive analytics, a branch of data analysis that goes beyond predictive analytics by suggesting optimal actions based on data. It employs technologies like AI, machine learning, and complex event processing to forecast outcomes and guide decision-making. The script explains the technology's role in revenue generation, cost reduction, and operational optimization, emphasizing its growing importance in fields like healthcare, insurance, and marketing.

Takeaways

• 📊 Prescriptive analytics uses historical data and predictive analytics to forecast outcomes and recommend actions.
• 🔍 It differs from descriptive analytics by focusing on actionable insights rather than just data monitoring.
• 🧠 Prescriptive analytics employs technologies like graph analysis, simulation, complex event processing, neural networks, recommendation engines, and heuristics.
• 📈 It relies heavily on big data and AI algorithms to provide a series of possible outcomes and the best path to a desired destination.
• 🏢 Businesses use prescriptive analytics for revenue generation, managing gross margins, and reducing expenses.
• 📈 It helps in identifying optimal product mixes, managing inventory levels, and minimizing manual processes.
• 🛠️ Prescriptive analytics is an extension of predictive analytics, adding an element of risk assessment when using automated recommendations.
• 💡 It is used in various fields including healthcare, insurance, financial risk management, and sales and marketing operations.
• 🚀 Examples of prescriptive analytics tools include Improvado, RapidMiner, Sisense, KNIME, and Tableau.
• 📚 The script emphasizes the importance of prescriptive analytics in shaping business responses to situations for optimal profitability.

Q & A

What is prescriptive analytics?

Prescriptive analytics is a statistical method used to generate recommendations and make decisions based on the computational findings of algorithmic models. It focuses on finding the best course of action in a scenario given the available data.

How does prescriptive analytics differ from descriptive and predictive analytics?

Descriptive analytics focuses solely on historical data, predictive analytics uses historical data to develop statistical models that forecast future possibilities, while prescriptive analytics takes predictive analytics a step further by predicting consequences for these outcomes.

What technologies are involved in prescriptive analytics?

Technologies involved in prescriptive analytics include graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

What is the role of big data in prescriptive analytics?

Prescriptive analytics relies on big data collection. Both structured and unstructured data gathered by an organization can be used to make prescriptive analysis. Machine learning and artificial intelligence are the driving forces behind the growth of prescriptive analytics.

How is prescriptive analytics different from predictive analytics in terms of risk?

Predictive analytics predicts what may or may not happen, while prescriptive analytics involves an element of risk when using automated recommendations due to the unpredictability of human behavior.

Can you provide an example of how prescriptive analytics works?

An example is in training personnel, where predictive analytics might identify that a significant proportion of learners might not complete a course without a specific skill. Prescriptive analytics can then design an algorithm to detect such individuals and recommend they acquire the necessary skills before enrolling.

In what ways is prescriptive analytics being used in online learning?

Prescriptive analytics is used in online learning to identify what content a learner has already mastered, enabling the presentation of new, unmastered content. It also allows administrators to define rules for automated feedback or actions and can reduce training time by determining previous knowledge and proficiency baselines.

What are the advantages of prescriptive analytics for businesses?

Prescriptive analytics helps businesses optimize processes, campaigns, and strategies, minimize maintenance needs, reduce costs without affecting performance, and increase the likelihood of proper planning for internal growth.

What are some examples of prescriptive analytics tools mentioned in the script?

Some prescriptive analytics tools mentioned are Improvado, RapidMiner, Sisense, KNIME, and Tableau.

How does prescriptive analytics help in decision making?

Prescriptive analytics helps in decision making by providing actionable insights and recommendations based on data analysis, allowing businesses to understand how to face and overcome challenges effectively.

What are the key takeaways from the script about prescriptive analytics?

The key takeaways are that prescriptive analytics works in combination with predictive analytics to find the right ways to achieve business objectives, it needs data to determine near-term outcomes, and it has critical importance in business analytics for shaping responses to situations and ensuring optimum profitability.

Outlines

00:00

🔍 Introduction to Prescriptive Analytics

The paragraph introduces the concept of prescriptive analytics, contrasting it with descriptive and predictive analytics. Descriptive analytics focuses on past data, while predictive analytics uses historical data to forecast future trends. Prescriptive analytics goes a step further by predicting outcomes and suggesting actions. It's defined as a statistical method that uses algorithmic models to recommend actions based on data. The technology behind it includes graph analysis, simulation, complex event processing, neural networks, recommendation engines, and heuristics. Prescriptive analytics relies on big data and AI to help businesses make informed decisions.

05:01

📈 How Prescriptive Analytics Works

This section explains how prescriptive analytics works in practice. It requires clear problem definition and solution awareness. The process involves creating algorithmic models to generate automated recommendations or decisions. An example is given where prescriptive analytics can be used to identify learners lacking specific skills and recommend them to acquire those skills before enrolling in a course. The effectiveness of prescriptive analytics is tailored to the situation and the quality of data available. It is noted that what works for one company may not work for another.

10:01

💹 Benefits and Applications of Prescriptive Analytics

The paragraph discusses the benefits of prescriptive analytics in business, emphasizing its role in revenue generation, gross margin management, and expense reduction. It works in conjunction with predictive analytics to provide insights that can lead to higher profitability. Prescriptive analytics helps businesses avoid risks like overstocking, cash flow issues, and failure to meet targets. It also aids in decision-making regarding sourcing locations, logistics routes, and inventory levels. The paragraph concludes by mentioning that prescriptive analytics is becoming more accessible and is already being integrated into commercial products.

15:04

🚗 Practical Examples and Importance of Prescriptive Analytics

This paragraph provides practical examples of prescriptive analytics, such as Google's self-driving car, which uses the technology to make millions of calculations based on its experience. The importance of prescriptive analytics to businesses is highlighted, as it helps in calculating product replacements, predicting customer preferences for marketing campaigns, predicting equipment failures for maintenance, and understanding customer purchasing habits for credit decisions. The benefits include process optimization, cost reduction without performance loss, and improved customer service.

20:05

🛠️ Tools and Key Takeaways of Prescriptive Analytics

The final paragraph lists various tools for prescriptive analytics, such as Improvado for marketing analytics, RapidMiner for AI and data analytics, Sisense for data transformation and reporting, KNIME for data integration and analytics, and Tableau for business intelligence. The key takeaways are that prescriptive analytics works with predictive analytics to achieve business objectives and requires data to determine outcomes. It is critical for business analytics, helping to shape responses to situations and ensuring profitability.

Keywords

💡Prescriptive Analytics

Prescriptive analytics is a type of data analytics that goes beyond predicting future outcomes to suggest the best course of action. It uses various algorithms and data to provide actionable insights. In the video, prescriptive analytics is described as a statistical method that generates recommendations and aids in decision-making based on computational findings. It is related to descriptive and predictive analytics but focuses on actionable insights rather than just data monitoring.

💡Descriptive Analytics

Descriptive analytics is focused on analyzing historical data to understand what has happened. The video script mentions that descriptive analytics looks at past data to provide insights into trends and patterns. It is one of the three types of analytics discussed, and it forms the foundation upon which predictive and prescriptive analytics are built.

💡Predictive Analytics

Predictive analytics uses historical data to develop statistical models that forecast future possibilities. The video script explains that predictive analytics takes the historical data analyzed by descriptive analytics and uses it to predict future trends. It is an essential step before applying prescriptive analytics, which then suggests actions based on these predictions.

💡Actionable Insights

Actionable insights are practical understandings that can be used to make decisions or take actions. The video emphasizes that prescriptive analytics is particularly focused on providing actionable insights. These insights are derived from data analysis and help businesses decide on the best course of action to achieve their objectives.

💡Algorithmic Models

Algorithmic models are mathematical representations used to analyze data and make predictions or recommendations. In the context of the video, prescriptive analytics relies on these models to process data and suggest the best actions to take. These models are crucial for translating raw data into meaningful, actionable insights.

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn from data and improve over time. The video script mentions machine learning as a key component of prescriptive analytics. It is used to develop algorithms that can predict outcomes and suggest actions, adapting and improving as more data becomes available.

💡Artificial Intelligence (AI)

Artificial intelligence refers to the simulation of human intelligence in machines. The video discusses AI as a driving force behind the growth of prescriptive analytics. AI algorithms are used to process complex data, recognize patterns, and make predictions, which are essential for providing the prescriptive analytics' actionable insights.

💡Data Collection

Data collection is the process of gathering and storing data from various sources. In the video, it is highlighted that prescriptive analytics relies on big data collection, which includes both structured and unstructured data. This data is used to make prescriptive analyses, with the quality of the data directly impacting the accuracy of the insights generated.

💡Risk

Risk in the context of the video refers to the uncertainty or potential negative impact associated with taking a particular course of action. The script notes that while predictive analytics predicts what might happen, prescriptive analytics involves an element of risk when using automated recommendations, as human behavior can be unpredictable.

💡Optimization

Optimization in the video refers to the process of making something as effective, efficient, or functional as possible. Prescriptive analytics is used to optimize various business processes by providing insights into the best actions to take. For example, it can help optimize inventory levels, marketing campaigns, and customer service to improve overall business performance.

💡Learning Management Systems (LMS)

Learning Management Systems are software applications for the administration, documentation, tracking, reporting, and delivery of educational courses, training programs, or learning and development programs. The video script provides examples of how prescriptive analytics is being used in LMS to tailor content to learners' needs, provide automated feedback, and reduce training time by assessing prior knowledge.

Highlights

Technology has enabled forecasting enterprise trends and predicting success in ways unimaginable in the past.

Descriptive analytics focuses on historical data, while predictive analytics uses this data to forecast future possibilities.

Prescriptive analytics goes a step further by predicting consequences for forecasted outcomes and recommending actions.

Prescriptive analytics uses statistical methods to generate recommendations and make decisions based on algorithmic models.

It focuses on finding the best course of action in a scenario given the available data.

Prescriptive analytics is related to descriptive and predictive analytics but emphasizes actionable insights.

Prescriptive analytics presents a series of possible outcomes and the best path to a desired destination.

It uses AI, machine learning, pattern recognition, and other technical tools to chart a course for moving forward.

Prescriptive analytics involves graph analysis, simulation, complex event processing, neural networks, recommendation engines, and heuristics.

It relies on big data collection and the integration of structured and unstructured data for analysis.

Ayata is one of the largest prescriptive analytics firms, built around AI and machine learning.

Prescriptive analytics is an extension of predictive analytics with an element of risk involved in automated recommendations.

It requires clarity of thought and unique algorithmic models to generate automated recommendations or decisions.

Prescriptive analytics can be used in training personnel to detect skill gaps and recommend necessary courses.

It is tailored to specific situations and needs, and varies according to the quality of data available for analysis.

Prescriptive analytics is growing in online learning, with applications in adaptive learning and reducing training time.

It depends on the data available and the ultimate objective of the exercise, working in tandem with predictive analytics.

Prescriptive analytics helps businesses generate revenue, manage gross margins, and reduce expenses.

It helps businesses understand how to face and overcome challenges, similar to a doctor's prescription for illnesses.

Prescriptive analytics matters to businesses as it optimizes processes, campaigns, and strategies, and reduces costs without affecting performance.

Examples of prescriptive analytics tools include Improvado, RapidMiner, Sisense, KNIME, and Tableau.

Prescriptive analytics works in combination with predictive analytics to find the right ways to achieve business objectives.

Analytic solutions aim to provide better support for making the right decisions, categorized into descriptive, predictive, and prescriptive analytics.

Transcripts

technology has given us the ability to

forecast

enterprise trends and predict success

in ways the business leaders of

yesterday

couldn't fathom in the past

successful businesses had to rely on

small sample sizes

simple questionnaires and other ways of

gathering of data

to predict general trends but not

anymore

this is because of prescriptive

analytics

so today we will be discussing the

overview of

prescriptive analytics

before we define prescriptive analytics

let's take a look on the differences

between the three types of analytics

the descriptive analytics is focused

solely on historical data

you can think of predictive analytics

as then using this historical data to

develop

statistical models that will then

forecast

about future possibilities

the prescriptive analytics takes the

predictive

analytics of a step further

and takes the possible forecasted

outcomes

and predicts consequences for these

outcomes

so you might find yourself thinking what

what on earth is prescriptive analytics

especially if you don't spend your days

buried

in google analytics and other types of

data analysis software prescriptive

analytics

is an statistical method used to

generate recommendations

and make decisions based on the

computational findings of

algorithmic models

it focuses on finding the best course of

action in a scenario given the available

data

it's related to both descriptive

analytics

and predictive analytics but emphasizes

actionable insights instead of data

monitoring

so the answer is surprisingly simple

prescriptive analytics is one of the key

branches

of data analytics it takes

large amounts of data and hypothetical

actions or situations

and presents a series of possible

outcomes

it then shows you what paths that could

lead to these

outcomes including the best possible

path

to a desired destination

so it's not a fortune telling nor

is it an exact science but using

artificial intelligence or ai

algorithms machine learning

pattern recognition and a lot of other

technical tools

prescriptive analytics can help you

chart

a course for moving forward

either in the immediate future or

four months or years down the road

what technology goes into prescriptive

analytics

so we have there the graph analysis

simulation complex event processing

that involves combining data from

multiple sources

to infer patterns and model complex

circumstances

we also have the neural networks or the

combinations of various

machine learning algorithms designed to

process

complex data the recommendation

engines which are computer algorithms

designed to predict positive or negative

preference based on what users

have chosen in the past i also have the

heuristics

or alternative methods of problem

solving

that can approximate an answer when

finding a definite one fails

and lastly the machine learning so

prescriptive analytics

relies on big data collection

all of the data an organization

gathers is structured or

unstructured it can be used to make

prescriptive analysis

the machine learning and artificial

intelligence

are the driving forces behind the growth

of

prescriptive analytics you have to take

note of that

one of the largest prescriptive

analytics

firms is the ayata

it has built its entire prescriptive

system

around ai and machine learning

which it says is built on

artificial intelligence controlling and

combining the science of predictions

with the science of decision making

so what's difference between predictive

analytics and prescriptive analytics

with predictive analytics it is

understood that predictions

may or may not happen

for prescriptive analytics however there

is an

element of risk when using automated

recommendations

human behavior can be unpredictable

so prescriptive analytics is considered

as an extension of predictive

analytics

now how does prescriptive analytics work

it requires clarity of thought

you have a problem in front of you and

you are aware of the solution as well

prescriptive analytics lies in finding

the right way to arrive

at the solution given the data

you have on hand in technical terms

we can say that those using this

technique

need unique algorithmic models

and precise directions to generate

automated recommendations or decisions

so prescriptive analytics begins with

acknowledging the fact

that there is a problem that requires a

solution

everyone knows that no one can recommend

a solution

without understanding the problem

so we have here an example training

personnel

can use predictive analytics to learn

that

a significant proportion of learners

might not be able to complete a specific

course without acquiring a particular

skill

so prescriptive analytics can help you

design

an algorithm that can detect

people lacking specific expertise in

question it can then proceed

to send an automated message or

recommendation to such persons

urging them to acquire the skills before

enrolling for the training course

so one should note that a specific

recommendation

would only apply to a particular

situation

therefore what might work for the

training

needs for one company might not

necessarily work

for another a prescriptive analytics

model is

tailored depending on the situation

and needs it also varies

according to the quality of data that is

available for analysis

so we have here the examples of

prescriptive analytics

in online learning so the use of

prescriptive analytics

is growing and can already be found

in some popular learning management

systems or

lms and learning technologies

first there are some tools that use

prescriptive analytics to identify

what content the learner has already

learned

so that new content not yet mastered

is presented instead this is an example

of how prescriptive analytics is

finding its way into adaptive learning

second some lmss enable

administrators to define specific rules

in order

for automated feedback or actions to

take place

for example if an employee is

struggling to complete a training course

the system may recommend they look at a

different

resource to obtain skills needed for the

previous

course lastly some

lmss are promising to reduce

training time for employees by

determining

previous knowledge and proficiency

baselines

in order to recommend which training

courses

or resources are best suited for the

learner

although the use of prescriptive

analytics

seems fairly small scale for the moment

it is sure to evolve steadily over the

years

as machine learning and our artificial

intelligence

becomes more accessible

okay so before discussing the advantages

of prescriptive analytics we shall

clarify

that prescriptive analytics depends on

the following two factors

the data available on hand the ultimate

objective of the exercise

it works in tandem with predictive

analytics

to provide better insights so businesses

can achieve a higher level of

profitability in the three

crucial areas of the company

first generation of revenue

prescriptive analytics applications

can provide detailed as well as timely

information

about the customer's preferences

it also allows business managers to

identify

new opportunities for cross-selling

and accelerating the regular sales

cycles

at the same time

so businesses can use prescriptive

analytics

to generate higher levels of revenue

second management of gross margins

prescriptive analytics techniques

when employed along with predictive

analytics can provide

gainful insights into the optimal

product mix

for the given and anticipated market

conditions

so a company experiences a higher

productivity so it enhances the profit

profitability aspect third

is reduction of expenses when you apply

prescriptive analytics techniques it

becomes

easy to manage inventory levels

you have a definite plan of action to

achieve

a specific objective so there is

no need to store inventory for long

durations

it also ensures to minimize

manual processes and cost

so a company ends up controlling the

expenses better

okay so as discussed earlier

prescriptive analytics

helps businesses understand

how to face and overcome challenges

so it is sort of like the prescription a

doctor

gives to cure illnesses

doctors prescribe medicines

that patients have to take in the right

dosage

to get cured so similarly

prescriptive analytics applications help

businesses to avoid untoward incidents

such as storage of resources reduction

in cash flows

and non-achievement of targets

organizations use

prescriptive analytics techniques to

decide

optimum sourcing locations

logistics routes and optimum quantity

to reduce expenses and save costs

it considers various factors like

demand and supply position in the market

so it ensures to hold the right levels

of inventory

to ensure proper capital utilization

today all the commercial

of the shelf products have

prescriptive analytics applications

built into it it enables

organizations to account for all kinds

of constraints

depending on the situation now

one of the best examples is

that of the google self-driving car

the vehicle has to make millions of

calculations

much in the same way we do when driving

our cars

based on the experience gathered by the

system

on its numerous trips it

updates the decision-making process

and it becomes better equipped to handle

a situation as it arises

so why anal prescriptive analytics

matters to your business because it

calculates

past sales of a product to determine the

number of replacements

second you have to know the tendency of

customers in certain products

to launch marketing campaigns according

to

users needs because you have to know

what your customers wants

and customers needs

third predict equipment failures which

provides

for maintenance at the right time

especially on the manufacturing field

and fourth you have to know customers

purchasing habits

and punctuality of payment to determine

whether it is appropriate to grant

credit

so prescriptive analytics has

benefits such as the optimization of

processes campaigns and strategies

it minimizes maintenance needs and

interconnects them for better conditions

it reduces costs without affecting

performance

and it increases the likelihood that

companies will approach

and plan for internal growth properly

we also have the qualitative research

method so we have to know the

characteristics

that distinguish it and then the

production optimization

we also have the efficient supply chain

management and lastly improve

customer service and experience

so due to its complexity there are still

few companies that use prescriptive

analysis however

prescriptive analysis benefits have

already become

evident in many fields this includes

the health care insurance

financial risk management and

sales and marketing operations

so instead of just predicting what will

happen to your business

prescriptive analysis makes

tweaks to certain variables to provide

the best

possible outcome and course of action

so prescriptive analytics focuses on

finding the best

course of action given the available

data

emphasizing actionable insights rather

than

data monitoring

the first prescriptive analytics tool

is the improvado it is a data analytics

tool designed by marketers

for marketers and provides a way for

them to get all

of their data in one place in real time

through automated dashboards and reports

so this tool pulls your data

from various marketing platforms such as

the google analytics the crms

email platforms you also have the

facebook

so improvado is best for analytics and

marketing

and leaders who need a tool to collect

data from all of their marketing

platforms

and aggregate it into a single

destination

next is the rapidminer

this offers artificial intelligence

and prescriptive analytics to companies

through an open and extensive data

analytics platform the rapidminer

is designed for analytics teams

and unifies the entire lifecycle

of data science from the data

preparation

stage to machine learning to

prescriptive analytics

analytic models the platform's

visual interface features pre-built

data connectivity you also have the

workflow component

and the machine learning

next is the sisense

science lets users easily transform

their data

into stunning interactive reports

so the tools visualization capabilities

include a drag and drop simple user

interface

that allows for charts and more complex

graphics

along with interactive visualizations

to be easily created so

this tool boasts over 100 data

connectors

and it is good for analytics teams

looking for a complete view of their

data with minimal assistance

from their id department

we also have the nine it is an

open source business intelligence or bi

tool for data integration reporting

and analytics it features a visual

interface that includes

nodes and for a range of activities

from extracting data to presenting it

so the analytics platform is primarily

designed to be used by data scientists

providing statistical functions

advanced machine learning and predictive

algorithms you also have the workflow

control

and more

so the nime is an open source

platform and it offers a variety of

integrations

for their platform

lastly is the tableau it is a

business intelligence or bi tool that

helps

organizations turn their data into

impactful

actionable insights this is a

drug-and-drop

tool that features helps users

create interactive dashboards with

advanced visual analytics

so this is a user-friendly platform

that provides an easy way to connect to

data

stored almost anywhere in nearly

any format

so you have the key takeaways of the

prescriptive analytics

so prescriptive analytics works in

combination with predictive

analytics in order to find the right

ways to achieve

the objectives of the business and it

needs data

to determine near-term outcomes

okay so analytic solutions always

aim to provide better support to make

the right decisions

the decision supporting capabilities can

be segregated

into three different categories

you have the pres descriptive analytics

it asks you what happened the predictive

analytics it will ask you what will be

the consequences

and prescriptive analytics it will ask

you

what should be done to get the best out

of the situation

so you see that prescriptive analytics

has critical importance

in business analytics it shapes

the way you respond to a particular

situation

therefore if used correctly

it can help mitigate every risk to

ensure that the business

displays optimum profitability