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3 Best Practices For Predictive Data Modeling

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3 Best Practices For Predictive Data Modeling


Predictive modeling is used to develop models that use past occurrences as reference points for organizations to forecast future business-related events and make clever decisions.

It is heavily involved in the strategy-making processes of companies in industries such as healthcare, law enforcement, pharmaceuticals and many more. The practices that can be used to make predictive data modeling error-free can be of great importance to everybody.

Predictive data modeling involves the creation, testing and validation of data models that will be used for predictive analysis in businesses. The lifecycle management of such models is a part of predictive data modeling. Such models, which use data captured by AI systems, machine learning tools, and other sources, can be used in advanced predictive analysis software systems used by organizations. The predictive data modeling process can be broken down into four steps:

  • Developing a model

  • Testing the model

  • Validating the model

  • Evaluating the model

There are a significant number of application areas for predictive analysis, such as financial risk management, international trade, clinical trials, cancer detection and many others. As we can see, each application area specified above is sensitive to mistakes or prediction inaccuracies. An inaccurate prediction could lead to incorrect diagnoses, potential patient deaths or financial turmoil in such industries. Therefore, organizations must implement certain practices to optimize the process of predictive data modeling. They must also continuously monitor the performance of the models.

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1)  Keeping the First Model Simple

As a process, predictive data modeling uses plenty of resources before organizations can expect it to bear fruit for them. Therefore, the competence of IT infrastructure present in the organization to carry out predictive data modeling is vital for streamlining the process without lag or inefficiencies. Accordingly, businesses must invest time and money to firstly make sure that their IT infrastructure is able to handle the process. This can be made sure with actions such as checking network connectivity, checking internet speeds, cybersecurity-related elements, and other factors before your business can use predictive data modeling. Additionally, your business needs to make sure that all your IT tools are aligned perfectly to make the model development process smoother.

More importantly, the first model created by an organization need not be overly complex or fancy. The first model will not be used for hardcore endpoint applications. A simplistic model provides the metrics and behaviors that can be used as a yardstick to test bigger and more complicated data models in the future. During the initial phases, businesses need to answer a few queries related to carrying out the predictive data modeling process. Some of such questions are related to the number of features needed to test a specific hypotheses, whether features that are useful are practical to make for the future, where they can store a model for maximum data security and threat protection, and finally, whether every significant decision-maker believes that the architecture and tools present currently in the organization are good enough to carry out the process.

Having an advanced hardware and software infrastructure conducive to predictive data modeling is vital for the process to be a success. Maintaining the simplicity of the first data model is valuable to train other, more complex models easily in the future.

2)  Validating Models Consistently

Result validation involves organizations running their model and evaluating its results with visualization tools. To carry out the validation process, organizations need to understand how business data is generated, and how it flows through organizational data networks. As we know, today, data analytics is highly integrated into nearly every business aspect. Individuals at every level in an organization use company resources and the web to make calculated business decisions. Information is gathered for predictive model training purposes too. Accordingly, getting the datasets that can be used to train predictive models also requires a lot of effort. The level of effort involved in data collection means that predictive models are quite highly valued, and each model may have the power to influence organizational data compliance (in a good or a bad way), financial bottom lines, as well as the creation of legal risks for the organization. As a result, such high-value assets need to be validated consistently.

Additionally, businesses may be under the impression that model validation is a one-off process and does not need to be carried out in the future. However, as an expert in the field of model training will tell you, that is a misconception. Predictive models need to constantly evolve with time to become more adept at making accurate forecasts, and so, the validation process needs to take place on a consistent basis. Here are some of the tasks that must certainly be carried out in the validation process:

The Thorough Validation of ‘Predictor’ Variables

A model is made up of several variables. Some of those variables may have strong predictive abilities. Such variables are labeled ‘predictors’ due to those capabilities. While predictors are useful for regular business work, they may, in some cases, also cause unwanted risk exposure for their organization when they are used for predictive analysis. For example, the absence of ultra-personal details of users in models may be a conscious effort taken by network administrators to not fall into legal troubles regarding privacy violations of users.

The Validation of Data Distribution

This type of validation is carried out by organizations to get an understanding of the distribution of predictor and target variables. Over time, there may be distribution shifts in such variables and models. If such shifts are detected in variables within data models, such models will have to be retrained with new data as they wouldn’t be able to provide predictive analysis with accuracy.

The Validation of Algorithms

As we know, analytical algorithms are used to train models. Validation must be done for algorithms that train models, which go on to carry out predictive analysis in businesses. Also, only certain types of models can provide clear, interpretable predictions. For example, there are multiple types of models, such as decision trees and neural networks. Decision trees provide more open and interpretable—albeit less accurate—results whereas neural networks do not—with more accurate results. So, decision trees must be validated more frequently as they participate more in predictive analysis. Data administrators need to choose between interpretability and prediction accuracy when carrying out the validation of such algorithms.

Compare Model-Prediction Accuracy Tests

To know the actual competence of a model, it must be compared with other models for accuracy. The most accurate models must be used in predictive analysis systems. This is also a validation task and must be carried out regularly if newer, more accurate models enter the fray with time. After all, the improvements in predictive analysis performance carry on perpetually.

Additionally, tasks such as auditing of models, and keeping track of every validation log entry are included under the umbrella of validation. Finally, the performance of models is monitored before and after deployment. Before deployment, businesses must test them for operational glitches that may impact their decision-making and predictive capabilities. Pre-deployment checking is essential because most models chosen for predictive analytics are used in real-world environments.

After a model is deployed, it needs to be monitored for wear, as, generally, models tend to degrade over time. So, validation helps with phasing such models out from a predictive analytics system and replacing them with new, useful ones. With constant validation, models could become less error-prone and more time-efficient. Constant validation is a potent practice as it improves the predictive data modeling process in several ways.

3) Recognizing Data Imbalances

Imbalanced data is a classification issue where the number of observations per class is not equally distributed. As a result, there may be a higher number of observations for a given class—known as a majority class—and much fewer observations for one or other classes—known as minority classes. Data imbalances cause inaccuracies in predictive analysis.

A data imbalance in a model can cause it to be erratic, and not very useful. For example, let’s talk about a fraud forecasting system proposed for a bank. Now, the bank may have a record of 95%— meaning that 95% of its transactions turn out to be non-fraudulent. In an imbalanced system, a system may state that the bank is 100% safe. Now, while the system may be right, and the bank will face fraud only 5% of the time, the forecast system will be in trouble whenever any fraud takes place because the system had clearly stated that the safety quotient was at a 100%.

Predictive data modeling is a tough task in the current digital world due to certain potential weaknesses that may creep into its functioning. By following the best practices, businesses can be sure of avoiding poor forecasting.



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Radware launches a spinoff of its cloud security business

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Cloud Computing News

Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.


Radware, a provider of cyber security and application delivery solutions, has revealed the spinoff of its Cloud Native Protector (CNP) business to form a new company called SkyHawk Security.

To accelerate Skyhawk Security’s development and growth opportunities, an affiliate of Tiger Global Management will make a $35 million strategic external investment, resulting in a valuation of $180 million. Tiger Global Management is a leading global technology investment firm focused on private and public companies in the internet, software, and financial technology sectors.

Skyhawk Security is a leader in cloud threat detection and protects dozens of the world’s leading organizations using its artificial intelligence and machine learning technologies. Its Cloud Native Protector provides comprehensive protection for workloads and applications hosted in public cloud environments. It uses a multi-layered approach that covers the overall security posture of the cloud and threats to individual workloads. Easy-to-deploy, the agentless solution identifies and prevents compliance violations, cloud security misconfigurations, excessive permissions, and malicious activity in the cloud.

“We recognize the growing opportunities in the public cloud security market and are planning to capitalize on them,” said Roy Zisapel, Radware’s president and CEO. “We look forward to partnering with Tiger Global Management to scale the business, unlock even more security value for customers, and position Skyhawk Security for long-term success.”

The spinoff, which adds to Radware’s recently announced strategic cloud services initiative, further demonstrates the company’s ongoing commitment to innovation. Skyhawk Security will have the ability to operate with even greater sales, marketing, and product focus as well as speed and flexibility. Current and new CNP customers will benefit from future product development efforts, while CNP services for existing customers will continue without interruption.

Radware does not expect the deal to materially affect operating results for the second quarter or full year of 2022.

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How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game

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How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game

AR, VR, and AI in sports are changing how fans experience and engage with their favorite games.

That’s why various organizations in the sports industry are leveraging these technologies to provide more personalized and immersive digital experiences.

How do you get a sports fan’s attention when there are so many other entertainment options? By using emerging technologies to create unforgettable experiences for them! Innovative organizations in the sports industry are integrating AR, VR and AI in sports marketing and fan engagement strategies. Read on to discover how these innovative technologies are being leveraged to enhance the game-day experience for sports fans.  

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AUGMENTED REALITY IN SPORTS

AR is computer-generated imagery (CGI) that superimposes digitally created visuals onto real-world environments. Common examples of AR include heads-up displays in cars, navigation apps and weather forecasts. AR has been around for decades, but only recently has it become widely available to consumers through mobile devices. One of the best ways sports organizations can use AR is to bring historical moments to life. This can help fans connect to the past in new ways, increase brand affinity and encourage them to visit stadiums to see these experiences in person. INDE has done just that, creating an augmented reality experience that lets fans meet their favorite players at the NFL Draft.

VIRTUAL REALITY IN SPORTS

VR is a computer-generated simulation of an artificial environment that lets you interact with that environment. You experience VR by wearing a headset that transports you to a computer-generated environment and lets you see, hear, smell, taste, and touch it as if you were actually there. VR can be especially impactful for sports because it lets fans experience something they would normally not be able to do. Fans can feel what it’s like to be a quarterback on the field, a skier in a race, a trapeze artist, or any other scenario they’d like. The VR experience is fully immersive, and the user is able to interact with the content using hand-held controllers. This enables users to move around and explore their virtual environment as if they were actually present in it.

ARTIFICIAL INTELLIGENCE IN SPORTS

Artificial intelligence is machine intelligence implemented in software or hardware and designed to complete tasks that humans usually do. AI tools can manage large amounts of data, identify patterns and make predictions based on that data. AI is already influencing all aspects of sports, from fan experience to talent management. Organizations are using AI to power better digital experiences for fans. They’re also using it to collect and analyze data about fan behavior and preferences, which helps organizers better understand what their customers want. AI is also changing the game on the field, with organizations using it to make better decisions in real time, improve training and manage player health. Much of this AI is powered by machine learning, which is a type of AI that uses data to train computer systems to learn without being programmed. Machine learning is the reason why AI is able to evolve and get better over time — it allows AI systems to adjust and improve based on new data.

MERGING THE REAL AND VIRTUAL

VR and AR are both incredible technologies that offer unique benefits. VR, for example, is an immersive experience that allows you to fully imagine and explore another virtual space. AR, on the other hand, is a technology that allows you to see and interact with the real world while also being able to see digital content superimposed on top of it. VR and AR are both rapidly evolving and can have a significant impact on sports marketing. By using both technologies, brands and sporting organizations can create experiences that bridge the real and virtual. This can help sports marketers create more engaging experiences that truly immerse their customers in the game.

Technologies like AR, VR and AI in sports are making it possible for fans to enjoy their favorite games in entirely new ways. AR, for example, can help sports lovers experience historical moments, VR lets them immerse themselves in the game, and AI brings them more personalized and immersive digital experiences. The best part is that sports fans can also use these technologies to interact with one another and feel even more connected. 

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The Dark Side of Wearable Technology

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The Dark Side of Wearable Technology

Wearable technology, such as smartwatches, fitness trackers, and other devices, has become increasingly popular in recent years.

These devices can provide a wealth of information about our health and activity levels, and can even help us stay connected with our loved ones. However, there is also a dark side to wearable technology, including issues related to privacy, security, and addiction. In this article, we will explore some of the darker aspects of wearable technology and the potential risks associated with these devices.

1. Privacy Concerns

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Source: Deloitte

Wearable technology can collect and transmit a significant amount of personal data, including location, health information, and more. This data is often shared with third parties, such as app developers and advertisers, and can be used to track and target users with personalized advertising. Additionally, many wearable devices lack robust security measures, making them vulnerable to hacking and data breaches. This can put users’ personal information at risk and expose them to identity theft and other cybercrimes.

2. Security Risks

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Source: MDPI

Wearable technology can also pose security risks, both to the individual user and to organizations. For example, hackers can use wearable devices to gain access to sensitive information, such as financial data or personal contacts, and use this information for malicious purposes. Additionally, wearable technology can be used to gain unauthorized access to secure areas, such as buildings or computer systems, which can be a major concern for organizations and governments.

3. Addiction Issues

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Source: Very Well Mind

The constant connectivity and access to information provided by wearable technology can also lead to addiction. The constant notifications and the ability to check social media, emails and other apps can create a constant need to check the device, leading to addiction-like symptoms such as anxiety, insomnia and depression.

4. Health Risks

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Source: RSSB 

Wearable technology can also pose health risks, such as skin irritation and allergic reactions caused by the materials used in the device. Additionally, the constant use of wearable technology can lead to poor posture and repetitive stress injuries, such as carpal tunnel syndrome. It is important for users to be aware of these risks and to take steps to protect their health, such as taking regular breaks from using the device and practicing good ergonomics.

Conclusion

Wearable technology has the potential to be a powerful tool for improving our health, fitness, and overall well-being. However, it is important to be aware of the darker aspects of wearable technology and the potential risks associated with these devices. By understanding the privacy, security, addiction, and health risks associated with wearable technology, users can take steps to protect themselves and their personal information. Additionally, by being aware of these risks, organizations can take steps to protect their employees and customers from the potential negative effects of wearable technology.

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