Predictive analytics can guarantee business success

A number of organizations are harnessing the power of predictive analytics to drive measurable ROI. Consider some examples: a large retailer with a major national brand that needed to reduce its customer churn rate (the percentage of customers who stop doing business with the firm). Predictive modelling, applied to the retailer’s purchase data on millions of customers, helped it identify which customers were at risk for leaving and take steps to retain them. Such behaviour modelling has been demonstrated to reduce customer churn by up to 50% annually. This is a 10% improvement in the company’s annual profitability on every customer retained.

Business analysts can apply predictive modeling techniques, such as data mining and statistical pattern recognition, to business problems in a variety of settings. By understanding the complete analytic process, business analysts will be able to more effectively define a business problem, collect data, formulate a predictive model, determine an appropriate analysis method for the particular application, test the effectiveness of their predictive model, communicate their findings, and monitor the performance of their predictive models over time.

Predictive analytics helps organizations make sense of their vast stores of business data by deriving meaning, driving insight, and providing information for informed decision making. Predictive analytics differs from descriptive analytics in that predictive analytics seeks to answer questions about what will happen, rather than what has happened. A simple example illustrates the differences between descriptive and predictive analytics: a retail store may want to combine transactional data (how much was sold), demographic data (who bought it), and weather data (how hot was it) to predict how many sunscreen units to stock on a given day to be ready for customers.

Analyze past and present data to generate predictions about current or future events. With predictive modeling, an analyst can study the financial data and look for trends and patterns that may point to particular business action.

A predictive analytical method may be used in network security, supply chain management, and manufacturing productivity analysis. Predictive analytics is increasingly important for enterprises as it provides business information that can help managers make business decisions.

In recent years, advances in the field of Natural Language Processing and supervised Artificial Intelligence have made it possible to utilize predictive modeling and statistical methods in many domains. Today, most business applications are based upon these techniques.

One of the biggest advantages of predictive analytics is that it is unbiased – it uses only raw and unprocessed data. With this in hand, a businessperson can use his predictive skills to make better guesses at what will happen next. For instance, a financial advisor may use predictive analytics to estimate how his client’s next investment will evolve. He can do this by keeping track of how different patterns in his past investments have led to the current state. This way, he can make better guesses about what will happen next.

Businesses that aim to effectively utilize predictive analytics need to first focus on understanding the business’s data. In doing so, they will be able to identify patterns among the collected data. They should then combine this data with their business knowledge in order to further make sense of it. Predictive capacity planning requires that a business take predictive analytics seriously.

Aside from being the core of predicting the market, predictive analytics can be of great help in other areas. For example, forecasting customer spending habits can be done by using predictive analytics. A business can predict how much money their customers will spend based on their past purchasing behavior. This is a useful tool that can be used to improve the quality and quantity of services that businesses provide to their customers.

Businesses that do not want to deal with all the hassles involved with manually analyzing predictive analytics can use big data technology. There are various tools that allow users to easily analyze data sets from a variety of sources. These tools are also referred to as big data analytics or big data applications. This technology allows a business to make informed decisions based on a large volume of data sets.

Predictive analytics provides business leaders with data-driven insight to determine which customers to contact for marketing, which tax returns to audit, which debtors to approve for increased credit limits, which patients to clinically screen, which customers are likely to leave, which persons of interest to investigate and which equipment to inspect for impending failure.

Predictive analytics is one subset of data analytics, which also encompasses descriptive, diagnostic, and prescriptive analytics. Descriptive analytics provides a picture or profile of the current state of affairs. In descriptive analytics, pictures and profiles are built from historical data. Diagnostic analytics tries to identify the root causes of business problems. Prescriptive analytics provides guidance on what decisions should be made. The latter is the set of techniques that predictive modeling belongs to. Predictive analytics includes forecasting future events and assessing risks for decision-makers with little or no domain knowledge about the problem context or data. Once the business problem has been defined in detail and the data has been collected and consolidated, experts apply predictive techniques to determine how successful a given strategy or tactic

The notion of predictive analytics conjures up images of all-knowing computers and the Terminator. However, in reality, predictive analytics is not about rocket science; it is about using data and statistical models to estimate future behaviour and improve decision-making skills. Predictive analytics does this by defining future outcomes based on patterns in historical data.

Indeed, predictive analytics can be applied to almost any business challenge. By applying data-driven techniques with a solid understanding of the underlying business, predictive analytics can deliver exceptional return on investment (ROI)—by or better than 10:1. Contrary to popular belief, predictive analytics results in advances for all areas of an enterprise, not just the IT department. Predictive approaches are equally relevant for sales, marketing and support efforts.

The key value of predictive analytics is in the ability to learn from data both present and past, allowing businesses to optimize performance. This section will briefly introduce the basic concepts of predictive analytics and the major considerations that must be addressed when establishing a business case for predictive analytics implementation.

Finance leaders often face complex decision-making scenarios that demand analysis of capital markets and regulatory requirements. Public safety leaders ensure effective allocation of limited resources to protect both at-risk individuals and population groups. Supply chain managers need information to forecast demand, reduce costs, and ensure quality supply chain processes. Human capital managers want tools to assist with talent acquisition, training, and succession planning. Healthcare practitioners require insight into the risk of their patients to better predict and prevent possible conditions.

Predictive analytics can help ensure that products are designed to satisfy customer wants and where resources are deployed to maximize productivity and profits. By automating many analytical tasks it delivers an optimally balanced allocation of talent and capital. It helps identify, monitor, and control fraud in a more cost-effective manner than legacy solutions (for example, by spotting suspicious activities) and as a result can reduce operating expenses significantly. Predicitve analytics helps uncover new marketing opportunities such as identifying the key decision makers within select target companies or geographies and thus minimize waste in contacting wrong decision makers.

Financial services, healthcare providers, and distribution companies all have a major stake in the risks their customers or workforce are exposed to. Using data-driven insight, fraud detection companies match identity theft risk factors against databases containing billions of financial transactions. The resulting profiles reveal fraudulent health care claims, consumer credit activity, and much more. Companies use predictive analytics to profile potential customers and predict whether they’re at risk for churning (leaving a service provider). The resulting segmentation can help target marketing efforts more effectively and reduce losses by avoiding spending money on potentially unprofitable customers. Predictive analytics helps inventory managers decide which products to bring into stock. Most manufacturing operations have the ability to forecast demand based on historical patterns using traditional business intelligence tools.

Predictive analytics is both a tool and a methodology to analyze data from different perspectives and angles in order to predict the future, make decisions with confidence, and optimize business processes. Predictive analytics involves collecting data that can provide some indication of what might happen next, descriptive statistics that reveal trends within the data (outliers or anomalies), visualization techniques to explore datasets (for example, a greater use of one color in a histogram than another might indicate fraud), correlating data sets (for example, ice cream sales and drowning accidents, as shown in the Figure below), and testing hypotheses against available data.

Companies like Wal-Mart, Home Depot, and airlines use predictive analytics to make decisions about who should receive marketing offers, and who should be placed on a long upgrade waiting list. Companies like Capital One and Chase Manhattan Bank use predictive analytics to increase the number of customer accounts approved for credit, and reduce the number of customer accounts declined for credit. Companies like Fidelity Investments and Vanguard use predictive analytics to decide which investments are worthy of management’s investment attention. Government agencies like the U.S. Department of Homeland Security use predictive analytics to prioritize system vulnerability audits and false-positive reduction efforts across their computer networks per Federal Information Security Management Act (FISMA) requirements.

Predictive analytics can return significant value as a business problem driven or customer driven initiative and this promotes support for the project investment. The most effective predictive analytics are derived from business problems that have a clear return on investment (ROI). In addition, data has to be available to solve the business problem at hand. For example, with customer churn, the predictive model would consider churn data such as actions performed in prior months and indicate which customers are likely to churn during the next month.

to illustrate the potential of predictive analytics in practical terms. In each instance, the business leaders are looking for an area with high risk and large consequences, where they can justify a substantial investment in predictive analytics. The business leader then defines the problem using clear objectives while ensuring that governance is in place from day one. Next, they should hire specialists with domain expertise to establish data sources, develop data models, and test the models using historical data. They should choose techniques that work “out of the box” but also allow for machine learning as the data grows (and it will grow).

A good predictive analytics model can deliver exceptional ROI. These models are constantly calculating and recalculating the factors that contribute towards these outcomes. The more accurate these calculations and predictions, the better we are able to react to future occurrences. Businesses with access to a better, more complete dataset have the potential outperform their competitors. In the context of fraud detection and prevention for example, predictive analytics is used to accurately predict which debtors are at risk of defaulting on their payments or which customers may be involved in criminal activity such as money laundering.

The return on investment (ROI) from new predictive models is unlimited because the primary advantages of using data science are the ability to capture untapped value. The power of predictive analytics has grown exponentially in recent years and bodes well for companies that take advantage of this opportunity. Predictive analytics offers bottom-line business value in a wide range of applications, industries, and business processes. Many organizations today hide within their data—data that may have significant predictive value but are not being leveraged effectively.

A number of organizations are harnessing the power of predictive analytics to drive measurable ROI. Consider some examples: a large retailer with a major national brand that needed to reduce its customer churn rate (the percentage of customers who stop doing business with the firm). Predictive modelling, applied to the retailer’s purchase data on millions of customers, helped it identify which customers were at risk for leaving and take steps to retain them. Such behaviour modelling has been demonstrated to reduce customer churn by up to 50% annually. This is a 10% improvement in the company’s annual profitability on every customer retained.

Business analysts can apply predictive modeling techniques, such as data mining and statistical pattern recognition, to business problems in a variety of settings. By understanding the complete analytic process, business analysts will be able to more effectively define a business problem, collect data, formulate a predictive model, determine an appropriate analysis method for the particular application, test the effectiveness of their predictive model, communicate their findings, and monitor the performance of their predictive models over time.

Predictive analytics helps organizations make sense of their vast stores of business data by deriving meaning, driving insight, and providing information for informed decision making. Predictive analytics differs from descriptive analytics in that predictive analytics seeks to answer questions about what will happen, rather than what has happened. A simple example illustrates the differences between descriptive and predictive analytics: a retail store may want to combine transactional data (how much was sold), demographic data (who bought it), and weather data (how hot was it) to predict how many sunscreen units to stock on a given day to be ready for customers.

Analyze past and present data to generate predictions about current or future events. With predictive modeling, an analyst can study the financial data and look for trends and patterns that may point to particular business action.

A predictive analytical method may be used in network security, supply chain management, and manufacturing productivity analysis. Predictive analytics is increasingly important for enterprises as it provides business information that can help managers make business decisions.

In recent years, advances in the field of Natural Language Processing and supervised Artificial Intelligence have made it possible to utilize predictive modeling and statistical methods in many domains. Today, most business applications are based upon these techniques.

One of the biggest advantages of predictive analytics is that it is unbiased – it uses only raw and unprocessed data. With this in hand, a businessperson can use his predictive skills to make better guesses at what will happen next. For instance, a financial advisor may use predictive analytics to estimate how his client’s next investment will evolve. He can do this by keeping track of how different patterns in his past investments have led to the current state. This way, he can make better guesses about what will happen next.

Businesses that aim to effectively utilize predictive analytics need to first focus on understanding the business’s data. In doing so, they will be able to identify patterns among the collected data. They should then combine this data with their business knowledge in order to further make sense of it. Predictive capacity planning requires that a business take predictive analytics seriously.

Aside from being the core of predicting the market, predictive analytics can be of great help in other areas. For example, forecasting customer spending habits can be done by using predictive analytics. A business can predict how much money their customers will spend based on their past purchasing behavior. This is a useful tool that can be used to improve the quality and quantity of services that businesses provide to their customers.

Businesses that do not want to deal with all the hassles involved with manually analyzing predictive analytics can use big data technology. There are various tools that allow users to easily analyze data sets from a variety of sources. These tools are also referred to as big data analytics or big data applications. This technology allows a business to make informed decisions based on a large volume of data sets.

Predictive analytics provides business leaders with data-driven insight to determine which customers to contact for marketing, which tax returns to audit, which debtors to approve for increased credit limits, which patients to clinically screen, which customers are likely to leave, which persons of interest to investigate and which equipment to inspect for impending failure.

Predictive analytics is one subset of data analytics, which also encompasses descriptive, diagnostic, and prescriptive analytics. Descriptive analytics provides a picture or profile of the current state of affairs. In descriptive analytics, pictures and profiles are built from historical data. Diagnostic analytics tries to identify the root causes of business problems. Prescriptive analytics provides guidance on what decisions should be made. The latter is the set of techniques that predictive modeling belongs to. Predictive analytics includes forecasting future events and assessing risks for decision-makers with little or no domain knowledge about the problem context or data. Once the business problem has been defined in detail and the data has been collected and consolidated, experts apply predictive techniques to determine how successful a given strategy or tactic

The notion of predictive analytics conjures up images of all-knowing computers and the Terminator. However, in reality, predictive analytics is not about rocket science; it is about using data and statistical models to estimate future behaviour and improve decision-making skills. Predictive analytics does this by defining future outcomes based on patterns in historical data.

Indeed, predictive analytics can be applied to almost any business challenge. By applying data-driven techniques with a solid understanding of the underlying business, predictive analytics can deliver exceptional return on investment (ROI)—by or better than 10:1. Contrary to popular belief, predictive analytics results in advances for all areas of an enterprise, not just the IT department. Predictive approaches are equally relevant for sales, marketing and support efforts.

The key value of predictive analytics is in the ability to learn from data both present and past, allowing businesses to optimize performance. This section will briefly introduce the basic concepts of predictive analytics and the major considerations that must be addressed when establishing a business case for predictive analytics implementation.

Finance leaders often face complex decision-making scenarios that demand analysis of capital markets and regulatory requirements. Public safety leaders ensure effective allocation of limited resources to protect both at-risk individuals and population groups. Supply chain managers need information to forecast demand, reduce costs, and ensure quality supply chain processes. Human capital managers want tools to assist with talent acquisition, training, and succession planning. Healthcare practitioners require insight into the risk of their patients to better predict and prevent possible conditions.

Predictive analytics can help ensure that products are designed to satisfy customer wants and where resources are deployed to maximize productivity and profits. By automating many analytical tasks it delivers an optimally balanced allocation of talent and capital. It helps identify, monitor, and control fraud in a more cost-effective manner than legacy solutions (for example, by spotting suspicious activities) and as a result can reduce operating expenses significantly. Predicitve analytics helps uncover new marketing opportunities such as identifying the key decision makers within select target companies or geographies and thus minimize waste in contacting wrong decision makers.

Financial services, healthcare providers, and distribution companies all have a major stake in the risks their customers or workforce are exposed to. Using data-driven insight, fraud detection companies match identity theft risk factors against databases containing billions of financial transactions. The resulting profiles reveal fraudulent health care claims, consumer credit activity, and much more. Companies use predictive analytics to profile potential customers and predict whether they’re at risk for churning (leaving a service provider). The resulting segmentation can help target marketing efforts more effectively and reduce losses by avoiding spending money on potentially unprofitable customers. Predictive analytics helps inventory managers decide which products to bring into stock. Most manufacturing operations have the ability to forecast demand based on historical patterns using traditional business intelligence tools.

Predictive analytics is both a tool and a methodology to analyze data from different perspectives and angles in order to predict the future, make decisions with confidence, and optimize business processes. Predictive analytics involves collecting data that can provide some indication of what might happen next, descriptive statistics that reveal trends within the data (outliers or anomalies), visualization techniques to explore datasets (for example, a greater use of one color in a histogram than another might indicate fraud), correlating data sets (for example, ice cream sales and drowning accidents, as shown in the Figure below), and testing hypotheses against available data.

Companies like Wal-Mart, Home Depot, and airlines use predictive analytics to make decisions about who should receive marketing offers, and who should be placed on a long upgrade waiting list. Companies like Capital One and Chase Manhattan Bank use predictive analytics to increase the number of customer accounts approved for credit, and reduce the number of customer accounts declined for credit. Companies like Fidelity Investments and Vanguard use predictive analytics to decide which investments are worthy of management’s investment attention. Government agencies like the U.S. Department of Homeland Security use predictive analytics to prioritize system vulnerability audits and false-positive reduction efforts across their computer networks per Federal Information Security Management Act (FISMA) requirements.

Predictive analytics can return significant value as a business problem driven or customer driven initiative and this promotes support for the project investment. The most effective predictive analytics are derived from business problems that have a clear return on investment (ROI). In addition, data has to be available to solve the business problem at hand. For example, with customer churn, the predictive model would consider churn data such as actions performed in prior months and indicate which customers are likely to churn during the next month.

to illustrate the potential of predictive analytics in practical terms. In each instance, the business leaders are looking for an area with high risk and large consequences, where they can justify a substantial investment in predictive analytics. The business leader then defines the problem using clear objectives while ensuring that governance is in place from day one. Next, they should hire specialists with domain expertise to establish data sources, develop data models, and test the models using historical data. They should choose techniques that work “out of the box” but also allow for machine learning as the data grows (and it will grow).

A good predictive analytics model can deliver exceptional ROI. These models are constantly calculating and recalculating the factors that contribute towards these outcomes. The more accurate these calculations and predictions, the better we are able to react to future occurrences. Businesses with access to a better, more complete dataset have the potential outperform their competitors. In the context of fraud detection and prevention for example, predictive analytics is used to accurately predict which debtors are at risk of defaulting on their payments or which customers may be involved in criminal activity such as money laundering.

The return on investment (ROI) from new predictive models is unlimited because the primary advantages of using data science are the ability to capture untapped value. The power of predictive analytics has grown exponentially in recent years and bodes well for companies that take advantage of this opportunity. Predictive analytics offers bottom-line business value in a wide range of applications, industries, and business processes. Many organizations today hide within their data—data that may have significant predictive value but are not being leveraged effectively.

A number of organizations are harnessing the power of predictive analytics to drive measurable ROI. Consider some examples: a large retailer with a major national brand that needed to reduce its customer churn rate (the percentage of customers who stop doing business with the firm). Predictive modelling, applied to the retailer’s purchase data on millions of customers, helped it identify which customers were at risk for leaving and take steps to retain them. Such behaviour modelling has been demonstrated to reduce customer churn by up to 50% annually. This is a 10% improvement in the company’s annual profitability on every customer retained.

Business analysts can apply predictive modeling techniques, such as data mining and statistical pattern recognition, to business problems in a variety of settings. By understanding the complete analytic process, business analysts will be able to more effectively define a business problem, collect data, formulate a predictive model, determine an appropriate analysis method for the particular application, test the effectiveness of their predictive model, communicate their findings, and monitor the performance of their predictive models over time.

Predictive analytics helps organizations make sense of their vast stores of business data by deriving meaning, driving insight, and providing information for informed decision making. Predictive analytics differs from descriptive analytics in that predictive analytics seeks to answer questions about what will happen, rather than what has happened. A simple example illustrates the differences between descriptive and predictive analytics: a retail store may want to combine transactional data (how much was sold), demographic data (who bought it), and weather data (how hot was it) to predict how many sunscreen units to stock on a given day to be ready for customers.

Analyze past and present data to generate predictions about current or future events. With predictive modeling, an analyst can study the financial data and look for trends and patterns that may point to particular business action.

A predictive analytical method may be used in network security, supply chain management, and manufacturing productivity analysis. Predictive analytics is increasingly important for enterprises as it provides business information that can help managers make business decisions.

In recent years, advances in the field of Natural Language Processing and supervised Artificial Intelligence have made it possible to utilize predictive modeling and statistical methods in many domains. Today, most business applications are based upon these techniques.

One of the biggest advantages of predictive analytics is that it is unbiased – it uses only raw and unprocessed data. With this in hand, a businessperson can use his predictive skills to make better guesses at what will happen next. For instance, a financial advisor may use predictive analytics to estimate how his client’s next investment will evolve. He can do this by keeping track of how different patterns in his past investments have led to the current state. This way, he can make better guesses about what will happen next.

Businesses that aim to effectively utilize predictive analytics need to first focus on understanding the business’s data. In doing so, they will be able to identify patterns among the collected data. They should then combine this data with their business knowledge in order to further make sense of it. Predictive capacity planning requires that a business take predictive analytics seriously.

Aside from being the core of predicting the market, predictive analytics can be of great help in other areas. For example, forecasting customer spending habits can be done by using predictive analytics. A business can predict how much money their customers will spend based on their past purchasing behavior. This is a useful tool that can be used to improve the quality and quantity of services that businesses provide to their customers.

Businesses that do not want to deal with all the hassles involved with manually analyzing predictive analytics can use big data technology. There are various tools that allow users to easily analyze data sets from a variety of sources. These tools are also referred to as big data analytics or big data applications. This technology allows a business to make informed decisions based on a large volume of data sets.

Predictive analytics provides business leaders with data-driven insight to determine which customers to contact for marketing, which tax returns to audit, which debtors to approve for increased credit limits, which patients to clinically screen, which customers are likely to leave, which persons of interest to investigate and which equipment to inspect for impending failure.

Predictive analytics is one subset of data analytics, which also encompasses descriptive, diagnostic, and prescriptive analytics. Descriptive analytics provides a picture or profile of the current state of affairs. In descriptive analytics, pictures and profiles are built from historical data. Diagnostic analytics tries to identify the root causes of business problems. Prescriptive analytics provides guidance on what decisions should be made. The latter is the set of techniques that predictive modeling belongs to. Predictive analytics includes forecasting future events and assessing risks for decision-makers with little or no domain knowledge about the problem context or data. Once the business problem has been defined in detail and the data has been collected and consolidated, experts apply predictive techniques to determine how successful a given strategy or tactic

The notion of predictive analytics conjures up images of all-knowing computers and the Terminator. However, in reality, predictive analytics is not about rocket science; it is about using data and statistical models to estimate future behaviour and improve decision-making skills. Predictive analytics does this by defining future outcomes based on patterns in historical data.

Indeed, predictive analytics can be applied to almost any business challenge. By applying data-driven techniques with a solid understanding of the underlying business, predictive analytics can deliver exceptional return on investment (ROI)—by or better than 10:1. Contrary to popular belief, predictive analytics results in advances for all areas of an enterprise, not just the IT department. Predictive approaches are equally relevant for sales, marketing and support efforts.

The key value of predictive analytics is in the ability to learn from data both present and past, allowing businesses to optimize performance. This section will briefly introduce the basic concepts of predictive analytics and the major considerations that must be addressed when establishing a business case for predictive analytics implementation.

Finance leaders often face complex decision-making scenarios that demand analysis of capital markets and regulatory requirements. Public safety leaders ensure effective allocation of limited resources to protect both at-risk individuals and population groups. Supply chain managers need information to forecast demand, reduce costs, and ensure quality supply chain processes. Human capital managers want tools to assist with talent acquisition, training, and succession planning. Healthcare practitioners require insight into the risk of their patients to better predict and prevent possible conditions.

Predictive analytics can help ensure that products are designed to satisfy customer wants and where resources are deployed to maximize productivity and profits. By automating many analytical tasks it delivers an optimally balanced allocation of talent and capital. It helps identify, monitor, and control fraud in a more cost-effective manner than legacy solutions (for example, by spotting suspicious activities) and as a result can reduce operating expenses significantly. Predicitve analytics helps uncover new marketing opportunities such as identifying the key decision makers within select target companies or geographies and thus minimize waste in contacting wrong decision makers.

Financial services, healthcare providers, and distribution companies all have a major stake in the risks their customers or workforce are exposed to. Using data-driven insight, fraud detection companies match identity theft risk factors against databases containing billions of financial transactions. The resulting profiles reveal fraudulent health care claims, consumer credit activity, and much more. Companies use predictive analytics to profile potential customers and predict whether they’re at risk for churning (leaving a service provider). The resulting segmentation can help target marketing efforts more effectively and reduce losses by avoiding spending money on potentially unprofitable customers. Predictive analytics helps inventory managers decide which products to bring into stock. Most manufacturing operations have the ability to forecast demand based on historical patterns using traditional business intelligence tools.

Predictive analytics is both a tool and a methodology to analyze data from different perspectives and angles in order to predict the future, make decisions with confidence, and optimize business processes. Predictive analytics involves collecting data that can provide some indication of what might happen next, descriptive statistics that reveal trends within the data (outliers or anomalies), visualization techniques to explore datasets (for example, a greater use of one color in a histogram than another might indicate fraud), correlating data sets (for example, ice cream sales and drowning accidents, as shown in the Figure below), and testing hypotheses against available data.

Companies like Wal-Mart, Home Depot, and airlines use predictive analytics to make decisions about who should receive marketing offers, and who should be placed on a long upgrade waiting list. Companies like Capital One and Chase Manhattan Bank use predictive analytics to increase the number of customer accounts approved for credit, and reduce the number of customer accounts declined for credit. Companies like Fidelity Investments and Vanguard use predictive analytics to decide which investments are worthy of management’s investment attention. Government agencies like the U.S. Department of Homeland Security use predictive analytics to prioritize system vulnerability audits and false-positive reduction efforts across their computer networks per Federal Information Security Management Act (FISMA) requirements.

Predictive analytics can return significant value as a business problem driven or customer driven initiative and this promotes support for the project investment. The most effective predictive analytics are derived from business problems that have a clear return on investment (ROI). In addition, data has to be available to solve the business problem at hand. For example, with customer churn, the predictive model would consider churn data such as actions performed in prior months and indicate which customers are likely to churn during the next month.

to illustrate the potential of predictive analytics in practical terms. In each instance, the business leaders are looking for an area with high risk and large consequences, where they can justify a substantial investment in predictive analytics. The business leader then defines the problem using clear objectives while ensuring that governance is in place from day one. Next, they should hire specialists with domain expertise to establish data sources, develop data models, and test the models using historical data. They should choose techniques that work “out of the box” but also allow for machine learning as the data grows (and it will grow).

A good predictive analytics model can deliver exceptional ROI. These models are constantly calculating and recalculating the factors that contribute towards these outcomes. The more accurate these calculations and predictions, the better we are able to react to future occurrences. Businesses with access to a better, more complete dataset have the potential outperform their competitors. In the context of fraud detection and prevention for example, predictive analytics is used to accurately predict which debtors are at risk of defaulting on their payments or which customers may be involved in criminal activity such as money laundering.

The return on investment (ROI) from new predictive models is unlimited because the primary advantages of using data science are the ability to capture untapped value. The power of predictive analytics has grown exponentially in recent years and bodes well for companies that take advantage of this opportunity. Predictive analytics offers bottom-line business value in a wide range of applications, industries, and business processes. Many organizations today hide within their data—data that may have significant predictive value but are not being leveraged effectively.