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How and Why to Use Personal Data Mining with HapPhi

Personal data mining (PDM) is a way of using data analysis and machine learning to gain insights from large volumes of unstructured data. It can be used for a variety of purposes, such as fraud detection, customer retention, and employee performance management. In this blog post, we’ll explore why you might consider using PDM, as well as the main PDM tools and resources available.

Written by
June 15, 2022

The concept of personal data mining (PDM) is not new. In fact, it has been around for several years. The idea is to use the power of machine learning and data analysis to make sense of large volumes of data. In the digital world, everything is connected. This connectivity has helped us create a world of information that is both valuable and abundant. However, it has also led to an explosion of digital information that is difficult to manage.

Personal data mining (PDM) is a way of using data analysis and machine learning to gain insights from large volumes of unstructured data. It can be used for a variety of purposes, such as fraud detection, customer retention, and employee performance management. In this blog post, we’ll explore why you might consider using PDM, as well as the main PDM tools and resources available.

What is Personal Data Mining with HapPhi?


Personal data mining is the process of finding insights and patterns in large volumes of unstructured data. It can be applied to any dataset that includes personal data, such as customer emails, social media posts, online reviews, sensor data, etc. Personal data is data that can be used to identify or trace a person, such as their identity (or parts of it), their behaviors, or their attitudes.



Why Use Personal Data Mining?


Personal data mining can be used for a number of purposes, including fraud detection, customer retention, and employee performance management. Fraud detection: Detecting and preventing fraud is a top priority for businesses of all sizes. While it is typically the role of a fraud control team within an organization to investigate fraud, a data-driven approach can help enterprises identify signs of fraud and take preventive measures. Customer retention: Retaining customers is also a critical priority for businesses of all sizes. Personalized offers and recommendations can help strengthen customer relationships and increase the likelihood that customers will continue to remain loyal customers. Employee recognition and engagement: With competitive salary surveys, recognition programs, and social media influencers, it can be challenging for employers to know how best to reward their employees. Personal data mining can help employers identify positive behaviors and provide meaningful recognition to employees.




Examples of How to Use Personal Data Mining


Data mining has numerous practical applications, such as for fraud detection, customer retention, and employee performance management. But which use cases should you prioritize? Here are a few examples:


Fraud detection: Identify high-risk customers and employees. High-risk customers could include those who have previously engaged in fraud or whose behavior suggests they may do so in the future, such as those who consistently order expensive items without checking out. High-risk employees could include those who consistently miss deadlines or who have a history of workplace disputes.


Customer retention: Identify customers who are at risk of churn. Churn is when a customer permanently leaves a company’s service. Customers at risk of churn might be interested in other products or have other reasons for leaving. Retaining these customers can help a company avoid losing key influencers in the customer journey.


Employee recognition and engagement: Recognize top employees. Top employees could be employees who consistently deliver high-quality customer experiences or those who consistently contribute to organizational success. Achieving recognition milestones can be motivating and contribute to employee engagement.


Examples of How to Use Personal Data Mining


Data scientists are becoming increasingly important in organizations of all sizes. But which roles will be the most impactful for your business? Here are a few examples:


Customer experience manager: The core job of a CEM is to ensure that end users of a business’s product have a positive experience. This can include everything from the initial contact with a customer (such as an email exchange), through the customer journey and support processes, to the after-sales experience. CEMs are responsible for bringing the right people together and helping them interact successfully.


Digital transformation manager: The role of a digital transformation manager (DTM) is to help an organization adapt to the growing digital presence of the business. In other words, the DTM leads the transformation of the business from an analog to a digital model.


The Advantages of Personal Data Mining


Personal data mining has numerous advantages, including the ability to:


Gather data from multiple sources and use it to create a holistic view : Data scientists can use personal data mined from multiple sources to create a holistic view of an individual. For instance, if a business sends a customer an email digest offering special discounts on various items, the data scientist can see if that customer also received similar offers from other businesses or if the customer purchased those items online. This enables the data scientist to take a much broader approach to improving customer experiences and boosting business results.


Data scientists can use personal data mined from multiple sources to create a holistic view of an individual. For instance, if a business sends a customer an email digest offering special discounts on various items, the data scientist can see if that customer also received similar offers from other businesses or if the customer purchased those items online. This enables the data scientist to take a much broader approach to improving customer experiences and boosting business results. Detect patterns and make predictions : Once a data scientist has analyzed the data, they can look for patterns that indicate whether an individual is likely to churn or not. This can help a company prepare for potential churn risks by offering preventative measures.


: Once a data scientist has analyzed the data, they can look for patterns that indicate whether an individual is likely to churn or not. This can help a company prepare for potential churn risks by offering preventative measures. Identify high-value customers and employees: Personal data mining can be used to identify high-value customers and employees. High-value customers are those who are likely to generate revenue for a company, while high-risk customers and employees can be identified and monitored to improve their experiences.


Key Principles of Personal Data Mining


There are four key principles to keep in mind when using personal data mining:


Privacy : Ensuring that personal data is kept private and secure is essential. Organizations should have a data protection policy in place that spells out the appropriate processes for collecting, using, and protecting personal data.


: Ensuring that personal data is kept private and secure is essential. Organizations should have a data protection policy in place that spells out the appropriate processes for collecting, using, and protecting personal data. Data source : It’s important to select the right data source for your analysis. This should be based on the goals of the analysis and the volume of data. For instance, sensor data, machine generated content, and social media posts are good sources of unstructured data.


: It’s important to select the right data source for your analysis. This should be based on the goals of the analysis and the volume of data. For instance, sensor data, machine generated content, and social media posts are good sources of unstructured data. Preparing the data: Once you’ve collected the data, you need to prepare it for analysis. This can include normalizing the data, removing unwanted data, and transforming numerical data to a standard format.


How to Start Using Personal Data Mining


If you’ve been curious about the benefits of personal data mining and would like to give it a try, there are a few things you need to know.


What data do you have? Start with a high-level overview of your data assets and their sources. This will give you a good idea of what data you have available and in what formats. From there, you can use the data inventory to see what data you currently have and what needs to be collected.


Start with a high-level overview of your data assets and their sources. This will give you a good idea of what data you have available and in what formats. From there, you can use the data inventory to see what data you currently have and what needs to be collected. Where do you plan to use the data? Once you know where you’ll be using the data, you can start working toward making it available.


Once you know where you’ll be using the data, you can start working toward making it available. How will you manage the data? This is where policies, procedures, and a data governance framework come into play. Data management involves creating and following data retention policies, as well as a data security plan to keep the data safe.


Conclusion


Personal data mining (PDM) is a way of using data analysis and machine learning to gain insights from large volumes of unstructured data. It can be used for a variety of purposes, such as fraud detection, customer retention, and employee performance management. In this blog post, we’ll explore why you might consider using PDM, as well as the main PDM tools and resources available.

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