In recent years, businesses have increasingly turned to predictive analytics and automation processes to make smarter decisions that drive higher returns.
SwissCognitive Guest Blogger: Arek Skuza – “Harnessing AI in Predictive Analytics and Automation Processes for Smarter Business Decisions”
Artificial intelligence (AI) has been integral in helping companies gain an edge over their competition by providing the ability to accurately predict future trends and automate business processes. This article will discuss the potential applications and benefits of leveraging AI in predictive analytics and automation processes and the challenges that may arise from incorporating this technology into an organization. By understanding the strengths of AI-driven analytics and automation, businesses can make smarter business decisions.
What is Predictive Analytics?
Predictive analytics is a type of data analysis that uses past data to predict future outcomes. It involves using machine learning algorithms to identify patterns and trends in large datasets. Predictive analytics can be used for a wide range of applications, such as predicting customer behavior, forecasting sales, understanding consumer preferences, and identifying new opportunities.
Predictive Modeling in Decision Making
In order to assess historical data and discover trends, predictive analytics models are used. The three most effective models are classification models, clustering models, and time series models.
Classification Models
Classification models are used to predict categorical variables such as yes/no or true/false. They analyze past data and determine which group each row of data belongs to. Furthermore, they are popularly used in fraud detection and credit risk evaluation because they predict questions with binary outputs. The different types of classification models include logistic regression, decision trees, random forest, neural networks, and Naive Bayes.
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Clustering Models
Clustering models use unsupervised machine learning algorithms to group similar rows of data together. Unsupervised machine learning is primarily used to discover hidden patterns or data grouping without the need for human intervention. Therefore, this type of model can be used for segmentation in marketing strategies and the discovery of hidden patterns. A few popular clustering models are k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical clustering.
The graphic below highlights the main differences in machine learning algorithms between classification and clustering models.
Time Series Models
Time series models predict behavior based on time-dependent data. They use regression techniques to forecast future trends from past observations. The types of time series models used in predictive analytics include autoregressive (AR), moving average (MA), ARMA, and ARIMA models. An example of using these models is a call center. In a call center, the amount of calls the center receives per hour can be forecasted. Using this information, the call center can make more informed business decisions like how many workers to hire and how many phones to have for more effective inventory management.
What is Decision Automation?
Decision automation is the use of AI, machine learning, natural language processing (NLP), and robotic process automation (RPA) to automate decisions. It streamlines processes by automating decision-making tasks that are traditionally done manually. Automation increases efficiency and accuracy while reducing costs and time spent on low-value activities. Additionally, it frees up resources that can be used for more complex tasks. Therefore, productivity is increased, and there is a reduction in risk and error in the business decision made.
Decision automation is most effective when applied to routines or repetitive decisions that are part of an organization’s day-to-day processes and functioning. For example, many businesses have already automated part of the hiring process–resume screening. Many companies use an applicant tracking system (ATS) that scans resumes to determine which applicants best fit the job. In fact, more and more organizations are starting to use AI to carry out first-round interviews.
Rule-based Decision Automation
There are two types of decision automation–rule-based and data-driven. Rule-based decision automation is based solely on certain business rules. These rules are defined beforehand, and the system makes decisions based only on those rules. Rules need to be explicitly stated in order for the system to understand them. They also need to be regularly updated, as conditions or circumstances can change over time.
Businesses use rule-based decision automation for tasks like customer satisfaction and risk management. For example, a customer service chatbot can determine the correct response to customer inquiries based on rules. In risk management, AI systems are used to detect fraud and generate alerts when suspicious activity is detected.
Data-driven Decision Automation
On the other hand, data-driven automation is not based on rules but on how a particular situation is unfolding and the uncertainty around it. This type of decision automation uses predictive analytics and artificial intelligence to allow the system to learn from data and make decisions based on past patterns. AI systems are trained with large volumes of data, which helps them identify patterns and relationships between different variables.
Data-driven decision automation is used in applications like automated trading, customer segmentation, self-driving cars, and recommendation systems. For instance, a self-driving car uses AI to process data from the environment, such as traffic signals, obstacles, and other drivers, in order to make decisions about where it should drive.
Benefits of Using Predictive Analytics and Automation for Business Decisions
The benefits of using predictive analytics and automation for business decisions include better security, risk reduction, enhanced operational efficiency, and improved decision-making. In regards to security, automation can identify and stop cyberattacks or fraud faster than manual processes. Risks are reduced because automated systems can better identify potential risks before they become a problem. Automation also increases operational efficiency by streamlining data collection and processing tasks, lessening the strain on human resources. Lastly, automated solutions generate more accurate results, which lead to better decisions.
Conclusion
Businesses should take advantage of the opportunities that predictive analytics and automation provide in order to make smarter decisions. By leveraging AI, organizations can automate decision-making tasks such as customer service, risk management, and automated trading. Automation increases efficiency, accuracy, and productivity while reducing costs and time spent on low-value activities. Most importantly, it improves the quality of decision-making. In order to remain competitive in today’s digital economy, companies must embrace automated solutions and begin leveraging AI for smarter business decisions.
References
www.ibm.com
www.flexrule.com
www.tibco.com
www.ibm.com
www.blastanalytics.com
About the Author:
Arek Skuza is an experienced technology leader, with over 10 years experience in project management and working with technical cross-functional and cross-organizational teams. He has expertise in AI strategy design & implementation, Robotic Process Automation implementation, Machine Learning projects leading, Artificial Intelligence-powered product launch management and Go-to-Market strategies & Data Monetization strategies. Arek has consulted for major companies such as Shell Energy, Discovery Networks and IKEA, helping to monetize & leverage data to drive sales, engagement, retention & referrals.
In recent years, businesses have increasingly turned to predictive analytics and automation processes to make smarter decisions that drive higher returns.
SwissCognitive Guest Blogger: Arek Skuza – “Harnessing AI in Predictive Analytics and Automation Processes for Smarter Business Decisions”
What is Predictive Analytics?
Predictive analytics is a type of data analysis that uses past data to predict future outcomes. It involves using machine learning algorithms to identify patterns and trends in large datasets. Predictive analytics can be used for a wide range of applications, such as predicting customer behavior, forecasting sales, understanding consumer preferences, and identifying new opportunities.
Predictive Modeling in Decision Making
In order to assess historical data and discover trends, predictive analytics models are used. The three most effective models are classification models, clustering models, and time series models.
Classification Models
Classification models are used to predict categorical variables such as yes/no or true/false. They analyze past data and determine which group each row of data belongs to. Furthermore, they are popularly used in fraud detection and credit risk evaluation because they predict questions with binary outputs. The different types of classification models include logistic regression, decision trees, random forest, neural networks, and Naive Bayes.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Clustering Models
Clustering models use unsupervised machine learning algorithms to group similar rows of data together. Unsupervised machine learning is primarily used to discover hidden patterns or data grouping without the need for human intervention. Therefore, this type of model can be used for segmentation in marketing strategies and the discovery of hidden patterns. A few popular clustering models are k-means clustering, mean-shift clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical clustering.
The graphic below highlights the main differences in machine learning algorithms between classification and clustering models.
Time Series Models
Time series models predict behavior based on time-dependent data. They use regression techniques to forecast future trends from past observations. The types of time series models used in predictive analytics include autoregressive (AR), moving average (MA), ARMA, and ARIMA models. An example of using these models is a call center. In a call center, the amount of calls the center receives per hour can be forecasted. Using this information, the call center can make more informed business decisions like how many workers to hire and how many phones to have for more effective inventory management.
What is Decision Automation?
Decision automation is the use of AI, machine learning, natural language processing (NLP), and robotic process automation (RPA) to automate decisions. It streamlines processes by automating decision-making tasks that are traditionally done manually. Automation increases efficiency and accuracy while reducing costs and time spent on low-value activities. Additionally, it frees up resources that can be used for more complex tasks. Therefore, productivity is increased, and there is a reduction in risk and error in the business decision made.
Decision automation is most effective when applied to routines or repetitive decisions that are part of an organization’s day-to-day processes and functioning. For example, many businesses have already automated part of the hiring process–resume screening. Many companies use an applicant tracking system (ATS) that scans resumes to determine which applicants best fit the job. In fact, more and more organizations are starting to use AI to carry out first-round interviews.
Rule-based Decision Automation
There are two types of decision automation–rule-based and data-driven. Rule-based decision automation is based solely on certain business rules. These rules are defined beforehand, and the system makes decisions based only on those rules. Rules need to be explicitly stated in order for the system to understand them. They also need to be regularly updated, as conditions or circumstances can change over time.
Businesses use rule-based decision automation for tasks like customer satisfaction and risk management. For example, a customer service chatbot can determine the correct response to customer inquiries based on rules. In risk management, AI systems are used to detect fraud and generate alerts when suspicious activity is detected.
Data-driven Decision Automation
On the other hand, data-driven automation is not based on rules but on how a particular situation is unfolding and the uncertainty around it. This type of decision automation uses predictive analytics and artificial intelligence to allow the system to learn from data and make decisions based on past patterns. AI systems are trained with large volumes of data, which helps them identify patterns and relationships between different variables.
Data-driven decision automation is used in applications like automated trading, customer segmentation, self-driving cars, and recommendation systems. For instance, a self-driving car uses AI to process data from the environment, such as traffic signals, obstacles, and other drivers, in order to make decisions about where it should drive.
Benefits of Using Predictive Analytics and Automation for Business Decisions
The benefits of using predictive analytics and automation for business decisions include better security, risk reduction, enhanced operational efficiency, and improved decision-making. In regards to security, automation can identify and stop cyberattacks or fraud faster than manual processes. Risks are reduced because automated systems can better identify potential risks before they become a problem. Automation also increases operational efficiency by streamlining data collection and processing tasks, lessening the strain on human resources. Lastly, automated solutions generate more accurate results, which lead to better decisions.
Conclusion
Businesses should take advantage of the opportunities that predictive analytics and automation provide in order to make smarter decisions. By leveraging AI, organizations can automate decision-making tasks such as customer service, risk management, and automated trading. Automation increases efficiency, accuracy, and productivity while reducing costs and time spent on low-value activities. Most importantly, it improves the quality of decision-making. In order to remain competitive in today’s digital economy, companies must embrace automated solutions and begin leveraging AI for smarter business decisions.
References
www.ibm.com
www.flexrule.com
www.tibco.com
www.ibm.com
www.blastanalytics.com
About the Author:
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