Essentials of Prescriptive Analytics

Hi, it's Sir Rob. Today I am going to discuss chapter 1 of our course, Business Analytics, Fundamentals of Prescriptive Analytics. This chapter deals with the definition and nature of prescriptive analytics. Data analytics is booming in boardrooms worldwide, promising enterprise-wide strategies for business success. But... What do these imply for businesses? Gaining the correct information, which produces knowledge, allows businesses to create a competitive edge, which is essential to companies successfully leveraging big data. Our topics for this lecture video will start from the face of business analytics, reviewing the other business analytics subjects you already finished as a touchstone to prescriptive analytics. Next we will talk about business analytics. will look at the specific nature of prescriptive analytics that makes it different from the other analytics courses an interesting topic about the big data follows providing insight into the present technology capability that contribute to the growth of data including those generated by sensors of Internet of Things and lastly we will discuss the elements of prescriptive analytics starting with data and on to the processes that manipulate the data into something useful for prescriptive analytics you'd be hard-pressed to find a business today that doesn't use analytics in some shape or form to inform business decisions and measure performance. Worldwide spending on big data analytics solution was predicted to be worth over $274.3 billion by last year, 2022. And it is not just large corporations investing. Research shows that nearly 70% of small businesses spend more than $10,000. a year on analytics to help them better understand their customers markets and business processes the overwhelming majority of executives say that their organization has achieved successful outcomes from big data and artificial intelligence data can also have a big impact on your bottom line with businesses who utilize big data increasing their profits by an average of 8 to 10 percent netflix reportedly saves 1 billion every year by using data analytics to improve its customer retention strategies so what methods of data analytics are businesses using to generate these impressive results and we are going to now review the different business analytics subjects Before going into the details of prescriptive analytics, business analytics is the process by which businesses use statistical methods and technologies for analyzing data in order to gain insights and improve their strategic decision making. There are three types of analytics that businesses use to drive their decision making. One is descriptive analytics. This is the first stage of business analytics and it still accounts for the majority of all business analytics today. Descriptive analytics answers the questions about what happened and why it happened. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past successes or failures. Most management reporting, such as sales, marketing, operations, and finance, uses this type of post-mortem analysis. Descriptive analytics is not used to draw inferences or make predictions about the future from its findings. Rather, it is concerned with representing what has happened in the past. Descriptive analytics are often described. played using visual data representations like Lime, Bar, and Pie charts and although they give useful insights on its own, often act as a foundation for future analysis. Because descriptive analytics uses fairly simple analysis techniques any findings should be easy for the wider business audience to understand for this reason descriptive analytics form the core of the everyday reporting in many businesses annual revenue reports are a classic example of descriptive analytics along with other reporting such as inventory warehousing and sales data which can be aggregated easily and provide a clear snapshot of companies'operations. Another widely used example is social media and Google Analytics tools, which summarize certain groupings based on simple counts of events like clicks and likes. Whilst descriptive data can be useful to quickly spot trends, and patterns, the analysis has its limitations. Viewed in isolation, descriptive analytics may not give the full picture. For more insight, you need to delve deeper into the next types of analytics. The next phase is predictive analytics. Predictive analytics answers the question of what will happen. This is When historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. Predictive analytics like descriptive analytics uses data mining. However, it also uses statistical modeling and machine learning techniques to identify the likelihood of future outcomes based on historical data. To make predictions, machine learning algorithms take existing data and attempt to fill in the missing data with the best possible guesses. In business, predictive models explain. exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. These predictions can then be used to solve problems and identify opportunities for growth. For example, organizations are using predictive analytics to prevent fraud by looking for patterns in the data. criminal behavior optimizing their marketing campaigns by spotting opportunities for cross selling and reducing risk by using past behaviors to predict which customers are more likely likely to default on payments. Another branch of predictive analytics is deep learning, which is a fairly new technology, emerging technology, which mimics human decision-making processes to make even more sophisticated predictions. It is based on the function of the human brain and uses the technique known as artificial neural network. which imitates how the synapses of the brain work to process information and make decisions. For example, through using multiple levels of social and environmental analysis, deep learning is being used to more accurately predict credit scores and in the medical field, it is being used to sort digital medical images such as MRI scans and x-rays to produce an automated prediction for doctors to use in diagnosing patients. One of the most well-known application is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan applications, customer data, and others in order to rank other individuals by their likelihood of making future credit payments on time. The final phase in business or data analytics is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing. the implication of each decision option it is referred to as the final frontier of analytic capabilities prescriptive analytics automatically synthesizes big data multiple disciplines of mathematical sciences and computational sciences, and business rules to make predictions, and then suggests decision options to preempt a prediction issue. or take advantage of prediction opportunities the field of prescriptive analytics borrows heavily from mathematics and computational science using a variety of statistical methods although closely related to both descriptive and predictive analytics prescriptive analytics emphasizes actionable insights instead of data monitoring this is a achieved through gathering data from a range of descriptive and predictive sources and applying them to the decision-making process. Algorithms then create and recreate possible decision patterns that could affect an organization in different ways. What prescriptive analytics precisely does is a three-stage process. First, it anticipates what will happen and when it will happen, but also why it will happen. And this is the predictive analytics part. Second, it suggests decision options and shows the implication of each decision option. Because it continually takes in new data. it re-predicts and re-prescribes, automatically improving prediction accuracy and prescribing better decision options. What makes predictive analytics especially valuable is their ability to measure the repercussions of a decision based on different future scenarios and then recommend the best course of action to take to achieve a company's goals. The business benefits The benefit of these predictive analytics processes is huge. It enables teams to view the best course of action before making decisions, saving time and money whilst achieving optimal results. Businesses that can harness the power of predictive analytics are using them in a variety of ways. For example, prescriptive analytics allow healthcare decision makers to to optimize business outcomes by recommending the best course of action for patients and providers. They also enable financial companies to know how much to reduce the cost of a product to attract new customers whilst keeping profits high. We will discuss further the benefits and the applications of prescriptive analytics in Chapter 3. We have mentioned a while ago big data involved in predictive and prescriptive analytics. How big is big data? Think of the movie Elysium. It talks about exabytes worth of data downloaded into the character's head. An exabyte of data is equivalent to 1 trillion gigabytes of data or 1 million terabytes of data. data no computer in the computer laboratories that we have in the cbaa can handle this load of data not to mention the simple task of storing them big data primarily refers to data sets that are too large or complex to be dealt with by traditional data processing application hardware and software what exactly Big Data. Put simply, it is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just cannot manage them. But this massive volumes of data can be used to address business problems you wouldn't have been able to tackle before. Predictive analytics ingests hybrid big data and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities. Based on the foregoing discussions, we can now craft a definition of prescriptive analytics. It is a process and technique of combining hybrid data, operations research science, and business rules to make predictions and suggest options and recommend the best course of action. Wikipedia offers a definition. which is not too far from this it says prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigation a future risk and shows the implication of each decision option it enables an enterprise to consider the best course of action to take in the light of information derived from description and predictive analytics."The process that predictive analytics employs follows the basic model of information systems which is data collection, data processing, and information generation. These correspond to the model of input, process, and output. The data inputs to prescriptive analytics may come from multiple sources. internal such as inside a corporation and external also known as environmental data or data outside of the organization the data may be structured which includes numbers and categories as already mentioned as well as semi structured and unstructured data such as texts images sounds and videos according to mongolian DB a leader in 2022 Gartner magic quadrant for cloud database management systems from 80% to 90% of data generated and collected by organizations is unstructured and its volumes are growing rapidly many times faster than the rate of growth for structured databases that is data evolving with respect to velocity that is more data being generated at a faster or a variable pace the second stage is data processing predictive analytics utilizes models and rules which are built into the analytics software mathematical models and computational models are techniques derived from mathematical sciences computer science and related disciplines such as applied statistics, machine learning, operations research, natural language processing, computer vision, pattern recognition, image processing, speech recognition, and signal processing in the case of Internet of Things. The correct application of all these methods and the verification of their results implies the need for resources on a massive scale, including human, computational, and temporal for every prospective analytic project. In order to spare the expense of dozens of people, high-performance machines, and weeks of work, one must consider the reduction of resources and therefore a reduction in the accuracy or reliability of the outcome the preferable route is a reduction that produces a probabilistic result not absolutely correct result within acceptable limits business rules define the business processes and include objectives constraints preferences policies best practices and boundaries or limitations the last stage in prescriptive analytics is the output and in this case it is a threefold output namely the what and when the why and the how the how of course suggests options and recommends the best course of actions the repeat step is due to the evolving nature of data caused by the continuous data collection data evolution happens when there is a significant change in the nature of people's behavior which impacts the type and volume of data that they produce it could also evolve because of implementation of prescriptive analytics recommended course of action therefore a response that worked before might not work in the next round of prescription and thus the need for re-prescription All three phases of analytics can be performed through professional services such as that of IBM or other suppliers of analytics software or technology or a combination. In order to scale predictive analytics technologies need to be adaptive to take into account the growing volume, velocity, variety of data that most mission critical processes and their environments may produce the software vendor and product listed on the slide are some of the most well-known providers of prescriptive analytic software and hardware there it is chapter 1 the definition in nature of prescriptive analytics in summary Prescriptive analytics is the process of using data to determine an optimal course of action. By considering all relevant factors, this type of analytics yields recommendations for next steps. Because of this, prescriptive analytics is a valuable tool for data-driven decision-making. Machine learning algorithms are often used in prescriptive analytics to parse through large amounts of data faster and often more efficiently than humans can using if and else statements algorithms come through data and make recommendations based on a specific combination of requirements it is important to note while algorithms can provide data informed recommendations they cannot replace human discernment prescriptive analytics is a tool to inform decisions and strategies and should be treated as such management's judgment is valuable and necessary to provide context and guard rails to algorithmic outputs. All the slide are the references used in making this lecture video. Thank you for watching and stay safe always.

But... What do these imply for businesses? Gaining the correct information, which produces knowledge, allows businesses to create a competitive edge, which is essential to companies successfully leveraging big data. Our topics for this lecture video will start from the face of business analytics, reviewing the other business analytics subjects you already finished as a touchstone to prescriptive analytics. Next we will talk about business analytics.

will look at the specific nature of prescriptive analytics that makes it different from the other analytics courses an interesting topic about the big data follows providing insight into the present technology capability that contribute to the growth of data including those generated by sensors of Internet of Things and lastly we will discuss the elements of prescriptive analytics starting with data and on to the processes that manipulate the data into something useful for prescriptive analytics you'd be hard-pressed to find a business today that doesn't use analytics in some shape or form to inform business decisions and measure performance. Worldwide spending on big data analytics solution was predicted to be worth over $274.3 billion by last year, 2022. And it is not just large corporations investing. Research shows that nearly 70% of small businesses spend more than $10,000.

a year on analytics to help them better understand their customers markets and business processes the overwhelming majority of executives say that their organization has achieved successful outcomes from big data and artificial intelligence data can also have a big impact on your bottom line with businesses who utilize big data increasing their profits by an average of 8 to 10 percent netflix reportedly saves 1 billion every year by using data analytics to improve its customer retention strategies so what methods of data analytics are businesses using to generate these impressive results and we are going to now review the different business analytics subjects Before going into the details of prescriptive analytics, business analytics is the process by which businesses use statistical methods and technologies for analyzing data in order to gain insights and improve their strategic decision making. There are three types of analytics that businesses use to drive their decision making. One is descriptive analytics. This is the first stage of business analytics and it still accounts for the majority of all business analytics today.

Descriptive analytics answers the questions about what happened and why it happened. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past successes or failures. Most management reporting, such as sales, marketing, operations, and finance, uses this type of post-mortem analysis. Descriptive analytics is not used to draw inferences or make predictions about the future from its findings.

Rather, it is concerned with representing what has happened in the past. Descriptive analytics are often described. played using visual data representations like Lime, Bar, and Pie charts and although they give useful insights on its own, often act as a foundation for future analysis.

Because descriptive analytics uses fairly simple analysis techniques any findings should be easy for the wider business audience to understand for this reason descriptive analytics form the core of the everyday reporting in many businesses annual revenue reports are a classic example of descriptive analytics along with other reporting such as inventory warehousing and sales data which can be aggregated easily and provide a clear snapshot of companies'operations. Another widely used example is social media and Google Analytics tools, which summarize certain groupings based on simple counts of events like clicks and likes. Whilst descriptive data can be useful to quickly spot trends, and patterns, the analysis has its limitations.

Viewed in isolation, descriptive analytics may not give the full picture. For more insight, you need to delve deeper into the next types of analytics. The next phase is predictive analytics. Predictive analytics answers the question of what will happen.

This is When historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. Predictive analytics like descriptive analytics uses data mining. However, it also uses statistical modeling and machine learning techniques to identify the likelihood of future outcomes based on historical data.

To make predictions, machine learning algorithms take existing data and attempt to fill in the missing data with the best possible guesses. In business, predictive models explain. exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. These predictions can then be used to solve problems and identify opportunities for growth.

For example, organizations are using predictive analytics to prevent fraud by looking for patterns in the data. criminal behavior optimizing their marketing campaigns by spotting opportunities for cross selling and reducing risk by using past behaviors to predict which customers are more likely likely to default on payments. Another branch of predictive analytics is deep learning, which is a fairly new technology, emerging technology, which mimics human decision-making processes to make even more sophisticated predictions. It is based on the function of the human brain and uses the technique known as artificial neural network. which imitates how the synapses of the brain work to process information and make decisions.

For example, through using multiple levels of social and environmental analysis, deep learning is being used to more accurately predict credit scores and in the medical field, it is being used to sort digital medical images such as MRI scans and x-rays to produce an automated prediction for doctors to use in diagnosing patients. One of the most well-known application is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan applications, customer data, and others in order to rank other individuals by their likelihood of making future credit payments on time.

The final phase in business or data analytics is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing. the implication of each decision option it is referred to as the final frontier of analytic capabilities prescriptive analytics automatically synthesizes big data multiple disciplines of mathematical sciences and computational sciences, and business rules to make predictions, and then suggests decision options to preempt a prediction issue. or take advantage of prediction opportunities the field of prescriptive analytics borrows heavily from mathematics and computational science using a variety of statistical methods although closely related to both descriptive and predictive analytics prescriptive analytics emphasizes actionable insights instead of data monitoring this is a achieved through gathering data from a range of descriptive and predictive sources and applying them to the decision-making process.

Algorithms then create and recreate possible decision patterns that could affect an organization in different ways. What prescriptive analytics precisely does is a three-stage process. First, it anticipates what will happen and when it will happen, but also why it will happen.

And this is the predictive analytics part. Second, it suggests decision options and shows the implication of each decision option. Because it continually takes in new data.

it re-predicts and re-prescribes, automatically improving prediction accuracy and prescribing better decision options. What makes predictive analytics especially valuable is their ability to measure the repercussions of a decision based on different future scenarios and then recommend the best course of action to take to achieve a company's goals. The business benefits The benefit of these predictive analytics processes is huge. It enables teams to view the best course of action before making decisions, saving time and money whilst achieving optimal results.

Businesses that can harness the power of predictive analytics are using them in a variety of ways. For example, prescriptive analytics allow healthcare decision makers to to optimize business outcomes by recommending the best course of action for patients and providers. They also enable financial companies to know how much to reduce the cost of a product to attract new customers whilst keeping profits high. We will discuss further the benefits and the applications of prescriptive analytics in Chapter 3. We have mentioned a while ago big data involved in predictive and prescriptive analytics.

How big is big data? Think of the movie Elysium. It talks about exabytes worth of data downloaded into the character's head.

An exabyte of data is equivalent to 1 trillion gigabytes of data or 1 million terabytes of data. data no computer in the computer laboratories that we have in the cbaa can handle this load of data not to mention the simple task of storing them big data primarily refers to data sets that are too large or complex to be dealt with by traditional data processing application hardware and software what exactly Big Data. Put simply, it is larger, more complex data sets, especially from new data sources.

These data sets are so voluminous that traditional data processing software just cannot manage them. But this massive volumes of data can be used to address business problems you wouldn't have been able to tackle before. Predictive analytics ingests hybrid big data and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities. Based on the foregoing discussions, we can now craft a definition of prescriptive analytics.

It is a process and technique of combining hybrid data, operations research science, and business rules to make predictions and suggest options and recommend the best course of action. Wikipedia offers a definition. which is not too far from this it says prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigation a future risk and shows the implication of each decision option it enables an enterprise to consider the best course of action to take in the light of information derived from description and predictive analytics."The process that predictive analytics employs follows the basic model of information systems which is data collection, data processing, and information generation. These correspond to the model of input, process, and output. The data inputs to prescriptive analytics may come from multiple sources. internal such as inside a corporation and external also known as environmental data or data outside of the organization the data may be structured which includes numbers and categories as already mentioned as well as semi structured and unstructured data such as texts images sounds and videos according to mongolian DB a leader in 2022 Gartner magic quadrant for cloud database management systems from 80% to 90% of data generated and collected by organizations is unstructured and its volumes are growing rapidly many times faster than the rate of growth for structured databases that is data evolving with respect to velocity that is more data being generated at a faster or a variable pace the second stage is data processing predictive analytics utilizes models and rules which are built into the analytics software mathematical models and computational models are techniques derived from mathematical sciences computer science and related disciplines such as applied statistics, machine learning, operations research, natural language processing, computer vision, pattern recognition, image processing, speech recognition, and signal processing in the case of Internet of Things. The correct application of all these methods and the verification of their results implies the need for resources on a massive scale, including human, computational, and temporal for every prospective analytic project. In order to spare the expense of dozens of people, high-performance machines, and weeks of work, one must consider the reduction of resources and therefore a reduction in the accuracy or reliability of the outcome the preferable route is a reduction that produces a probabilistic result not absolutely correct result within acceptable limits business rules define the business processes and include objectives constraints preferences policies best practices and boundaries or limitations the last stage in prescriptive analytics is the output and in this case it is a threefold output namely the what and when the why and the how the how of course suggests options and recommends the best course of actions the repeat step is due to the evolving nature of data caused by the continuous data collection data evolution happens when there is a significant change in the nature of people's behavior which impacts the type and volume of data that they produce it could also evolve because of implementation of prescriptive analytics recommended course of action therefore a response that worked before might not work in the next round of prescription and thus the need for re-prescription All three phases of analytics can be performed through professional services such as that of IBM or other suppliers of analytics software or technology or a combination. In order to scale predictive analytics technologies need to be adaptive to take into account the growing volume, velocity, variety of data that most mission critical processes and their environments may produce the software vendor and product listed on the slide are some of the most well-known providers of prescriptive analytic software and hardware there it is chapter 1 the definition in nature of prescriptive analytics in summary Prescriptive analytics is the process of using data to determine an optimal course of action. By considering all relevant factors, this type of analytics yields recommendations for next steps. Because of this, prescriptive analytics is a valuable tool for data-driven decision-making. Machine learning algorithms are often used in prescriptive analytics to parse through large amounts of data faster and often more efficiently than humans can using if and else statements algorithms come through data and make recommendations based on a specific combination of requirements it is important to note while algorithms can provide data informed recommendations they cannot replace human discernment prescriptive analytics is a tool to inform decisions and strategies and should be treated as such management's judgment is valuable and necessary to provide context and guard rails to algorithmic outputs. All the slide are the references used in making this lecture video. Thank you for watching and stay safe always.

Transcript for:Essentials of Prescriptive Analytics

Transcript for:
Essentials of Prescriptive Analytics