[S2E1] Prescriptive Analytics | 5 Minutes With Ingo

Summary

In the video, Ingo explores the concept of 'prescriptive analytics,' illustrating its application through a practical example of deciding whether to bring an umbrella based on weather forecasts and personal schedules. He explains that by integrating predictions with optimization techniques, one can make informed decisions. Ingo then outlines the progression from business intelligence to predictive analytics, highlighting the value of prescriptive analytics in influencing future outcomes through machine learning and optimization schemes. The video concludes with a brief mention of optimization techniques like evolutionary algorithms for decision-making.

Takeaways

• 🕰️ The discussion introduces 'prescriptive analytics,' a method that uses predictions to determine the best course of action.
• ☔ The example of deciding whether to bring an umbrella based on weather forecasts illustrates how prescriptive analytics works.
• 🗓️ Prescriptive analytics involves considering various data sources, like calendars and maps, to make informed decisions.
• 🚗 It suggests that knowing whether you need to leave the house or the distance to your destination can influence decision-making.
• 🚦 The script highlights how traffic predictions can affect the decision to leave early to avoid congestion.
• 📊 The speaker outlines four styles of analytics: Business Intelligence (BI), historical data analysis, predictive analytics, and prescriptive analytics.
• 📈 BI reports provide insights from past data but do not predict future events, unlike predictive analytics.
• 🤖 Predictive analytics uses machine learning to forecast future events, such as the likelihood of rain.
• 🔝 Prescriptive analytics is valued for its ability to not only predict but also to suggest actions that can optimize outcomes.
• 🛠️ Optimization schemes can range from simple brute force methods to more complex heuristics or evolutionary algorithms for larger datasets.
• 📝 The script concludes by emphasizing the importance of prescriptive analytics in combining predictions with actionable insights.

Q & A

What is the main topic discussed in the script?

The main topic discussed in the script is 'prescriptive analytics,' which involves using predictions combined with optimization schemes to determine the best course of action.

Why is knowing the weather forecast useful for prescriptive analytics?

Knowing the weather forecast is useful for prescriptive analytics because it allows individuals to predict future conditions and make informed decisions, such as whether to bring an umbrella, based on the likelihood of rain.

What is the difference between business intelligence (BI) and prescriptive analytics?

Business intelligence (BI) focuses on analyzing past data to provide historical insights, whereas prescriptive analytics uses predictions and optimization to suggest the best actions to take in the future.

How does the script use the example of an umbrella to illustrate prescriptive analytics?

The script uses the umbrella example to show how one might decide whether to bring an umbrella based on the weather forecast, personal calendar, distance to work, and traffic predictions, ultimately choosing the best course of action.

What are the four different styles of analytics mentioned in the script?

The four different styles of analytics mentioned are business intelligence (BI), historical analysis (looking at data from multiple years), predictive analytics, and prescriptive analytics.

Why is predictive analytics considered more useful than just historical analysis?

Predictive analytics is considered more useful because it uses machine learning and data science techniques to predict future events, such as the likelihood of rain, which can help in making proactive decisions.

What role do optimization schemes play in prescriptive analytics?

Optimization schemes in prescriptive analytics help in evaluating different options for action, predicting their outcomes, and selecting the course of action that leads to the best future result.

Can you provide an example of an optimization technique mentioned in the script?

An example of an optimization technique mentioned in the script is evolutionary algorithms, which can be used for various problem types and work efficiently on large search spaces.

How does the script relate the concept of prescriptive analytics to everyday life?

The script relates prescriptive analytics to everyday life by using the example of deciding whether to bring an umbrella based on various factors like weather forecast, personal schedule, and traffic conditions.

What is the significance of combining machine learning models with optimization schemes in prescriptive analytics?

Combining machine learning models with optimization schemes in prescriptive analytics allows for the prediction of future outcomes based on current data and the evaluation of different actions to determine the most effective course of action.

How does the script suggest using data from different sources to make a decision?

The script suggests using data from different sources such as weather forecasts, personal calendars, maps for distance calculations, and traffic predictions to make a comprehensive decision, like deciding when to leave for work in the rain.

Outlines

00:00

🌧️ Prescriptive Analytics in Everyday Decision Making

Ingo introduces the concept of 'prescriptive analytics,' using the example of deciding whether to bring an umbrella based on weather forecasts. He explains that prescriptive analytics combines predictions with optimization schemes to determine the best course of action. In the example, Ingo suggests considering multiple factors like personal calendar, distance to work, and traffic predictions to decide whether to bring an umbrella or leave early. This approach illustrates how simple forecasts can lead to complex decision-making processes, which is especially valuable in business scenarios.

05:04

🔍 Exploring Optimization Techniques in Analytics

Ingo discusses optimization schemes used in prescriptive analytics, using a dataset that predicts people's happiness based on the type of car they buy. He explains that while brute force optimization can be used for a small number of options, heuristics and techniques like evolutionary algorithms are necessary for larger search spaces. These methods help find the optimal solution more efficiently. The conversation concludes with a brief mention of the importance of these techniques in achieving the best outcomes in decision-making processes.

Keywords

💡Prescriptive Analytics

Prescriptive analytics is an advanced form of data analytics that provides guidance on the best course of action by using predictive analytics to forecast future outcomes and optimization techniques to determine the best decision. In the video, it is used to illustrate how one might decide whether to bring an umbrella based on weather predictions, calendar appointments, and traffic conditions, showcasing its application in everyday decision-making.

💡Weather Forecast

A weather forecast is a prediction of weather conditions for a specific time and place. In the context of the video, the weather forecast is a crucial input for prescriptive analytics, as it helps determine the likelihood of rain and influences the decision to carry an umbrella, demonstrating the practical application of weather data in daily life.

💡Optimization Schemes

Optimization schemes are methods used to find the best solution for a problem, often involving the maximization or minimization of a certain objective. The video discusses how optimization schemes can be combined with predictions to determine the most effective action, such as leaving early for work to avoid traffic when it is predicted to rain.

💡Business Intelligence (BI)

Business intelligence refers to the strategies and technologies used by enterprises for the data analysis of business information. In the video, BI is presented as a form of analytics that looks into the past, providing historical data like the number of rainy days in a year, which is useful but does not predict future events.

💡Predictive Analytics

Predictive analytics is a branch of advanced analytics that makes use of data, machine learning, and statistical algorithms to identify the likelihood of future outcomes based on historical data. The video script uses the example of a machine learning model predicting the chance of rain to illustrate how predictive analytics can inform decision-making.

💡Machine Learning

Machine learning is a type of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. In the video, machine learning models are mentioned as a tool for predictive analytics to forecast events like rain, which is vital for prescriptive analytics.

💡Data Science

Data science is a field that uses scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. The video implies the use of data science techniques in conjunction with machine learning to make informed predictions and decisions.

💡Heuristics

Heuristics are problem-solving strategies that use readily accessible, though loosely accurate, information to control the problem-solving process. The video mentions the use of heuristics in optimization when dealing with a large search space, suggesting that they are a practical approach to finding solutions efficiently.

💡Evolutionary Algorithms

Evolutionary algorithms are a subset of heuristic optimization algorithms that use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The video script highlights their utility in solving complex optimization problems with large search spaces.

💡Decision Making

Decision making is the process of selecting a course of action from multiple alternatives based on a set of criteria or preferences. The video's main theme revolves around how prescriptive analytics aids in decision making by combining predictions with optimization to determine the best possible outcome.

💡Search Space

In optimization, the search space refers to the set of all possible solutions to a given problem. The video script discusses how the size of the search space can influence the choice of optimization techniques, with heuristics and evolutionary algorithms being useful for larger spaces.

Highlights

Introduction to the concept of 'prescriptive analytics' in the context of weather forecasting and decision-making.

The importance of combining predictions with optimization schemes for decision-making.

Example of using weather forecasts to decide whether to bring an umbrella based on various factors.

The significance of checking one's calendar to determine the necessity of leaving the house on a rainy day.

Consideration of the distance between home and office and the mode of transportation in decision-making.

Inclusion of traffic predictions in the decision-making process due to potential increased congestion on rainy days.

The transformation of a simple weather forecast into a complex decision-making problem through data integration.

Differentiation between business intelligence (BI), which looks into the past, and predictive analytics, which forecasts the future.

The limitations of BI reports in providing actionable insights for future events.

The role of machine learning models in predictive analytics to estimate the likelihood of future events.

The value of prescriptive analytics in not only predicting the future but also in changing it through optimized actions.

The process of evaluating different action options and their potential outcomes to select the best course of action.

The application of optimization schemes to determine the best course of action in various scenarios.

The use of brute force optimization techniques when the number of options is small.

The necessity of heuristic methods for efficient optimization in large search spaces.

Introduction of evolutionary algorithms as a widely used technique for optimization in complex problems.

Conclusion emphasizing the importance of prescriptive analytics in combining machine learning with optimization for better decision-making.

Transcripts

Hey Ingo, do you know what time it is?

I guess it's time for Five Minutes with Ingo?

I mean, it's also time for all the rain, look at that.

But this actually gives me an idea.

I would like to discuss something which is called 'prescriptive analytics.'

The idea here is, if you know for example the weather forecast, the prediction for tomorrow,

you could try to figure out what is the best course of action.

For example, should you bring your umbrella or not?

So, let's go over there and discuss this a little bit yes?

So, the idea behind prescriptive analytics really is you take predictions, and then you

combine those predictions with optimization schemes.

So for example let's take the umbrella example.

If you know it's very likely that it's going to rain tomorrow, should you bring the umbrella?

Hmm, well, you could just say yeah, sure, why not?

But I would say, let's have a look at your calendar first, let's figure out if you actually

need to leave the house.

So let's say you have an appointment in the office at 8:00 in the morning, then yeah sure,

take the umbrella because you leave the house.

But wait, is the office actually close enough to your home?

So maybe it's not, and you need to take the car.

So let's say the distance is ten miles.

You figured this out by looking on a map.

So now you looked into the calendar, and into the map, so you already had two different

data sources.

So, since you need to take the car, the next thing that would be, well everybody's taking

the car because it's raining.

So there will be more traffic.

So you take maybe traffic predictions in as well.

So you have the rain prediction, you have the map information, you have all these pieces

of information, bring them together and now the best course of action is actually, well

take the car, but leave early because everyone will be on the road.

So here you see, actually how an easy forecast, a weather forecast, can turn into pretty complex

decision making, well, problem here really.

And that is an easy case.

So for most business cases, things are much more difficult.

So let's think about how analytics in general can help us with that, let's go over to the

whiteboard and discuss this.

So in general there are four different styles of analytics.

Let's start here at the bottom with BI or business intelligence, which really is just

a look into the past.

For example a BI report could deliver the piece of information that it has rained 237

days in the last year.

And while this might be an important piece of information, it doesn't really tell you

anything about what is going to happen tomorrow.

So, this brings me to the next level, if you do this for one year you can do this for multiple

years, and you can look into the data of multiple years.

Let's say you had seen 237 days of rain last year, 242 the year before, and then 250 the

year before that, and based on all those BI reports you can create another prediction.

You are kind of confident that it is going to rain more than 200 days in the next year.

While this is a prediction, it doesn't tell you, again, something about tomorrow.

So, in my opinion also not that useful.

Brings me to the next level, or layer here which is predictive analytics.

This is where we use all our machine learning and data science techniques.

A machine learning model here can actually tell you what is the likelihood of rain for

tomorrow.

So if you know there's 95% of a chance of rain for tomorrow, you can at least take this

into account and do something, let's say bring your umbrella, that is good.

But doing something is really the keyword here, and that brings me to the top level

here which is also the level which provides the biggest value.

Because knowing your future is one thing, but if you are able to change this future,

that is actually even more valuable.

And the way how you are doing this is you take those predictions, and you have different

options for your actions.

And you go through all those options, you predict how those options will effect your

future, and then of course you pick the course of action which delivers you the best future

and let's say the best outcome for tomorrow.

Not getting wet and getting into the office on time.

So this is why Prescriptive Analytics is so important, because it provides so much value

by combining machine learning models and optimization schemes.

Ingo, can you discuss more about the optimization schemes for our course of action?

Sure!

Well in general you can use any optimization scheme.

So here is a little dataset here on the bottom here.

We have a couple of columns we created our predictive models on, and you had to predict

the happiness of people.

Based on, for example, the type of car they are buying.

And this is now interesting, because you could basically go through all the car brands and

all the options just to figure out how would a certain person, how would this car purchase

effect the happiness of this person.

And if the number of options are small, you can literally go through all the options.

And that's kind of a brute force optimization technique.

But if the search space is very large, you need to use more heuristics to actually get

to the perfect outcome in a more efficient way.

One widely used technique, evolutionary algorithms, so they work really for all kinds of different

problem types and on large search bases.

And that's it for today, thank you!