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Machine Learning for Business

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What is machine learning?

Artificial intelligence includes machine learning as a subset. Instead of being specifically programmed to do so, it focuses on teaching machines to learn from data and evolve with experience. Algorithms are trained in machine learning to identify trends and associations in large datasets and to make the right decisions and predictions based on those findings.

Machine Learning’s significance

Machine learning has moved faster because there is a potentially unlimited amount of data available, data storage is cheap, and computers are getting cheaper and faster.

Many companies are now focusing on more efficient machine learning models that can process larger and more complicated data while delivering faster, more accurate results on massive scales. Businesses can use ML techniques to identify potential risks and profitable opportunities more quickly.

Real-world applications of machine learning drive market outcomes that can have a big effect on a company's product. New methods are being developed quickly in the field, which makes it possible to use machine learning in almost any way. ML is a great way to build models, plans, and designs for businesses that use a lot of data and need a way to understand it accurately and effectively.

History of machine learning?

Arthur Samuel, an American IBMer and pioneer in the fields of computer games and artificial intelligence coined the word machine learning in 1959. In the early days of AI as an academic discipline, some researchers were interested in making machines learn from data. They tried to approach the problem with various symbolic methods, including what they then termed “neural networks," which were mostly perceptrons, and other models that were later found to be reinventions of the generalized linear models of statistics.

Machine learning (ML) was reorganized as a separate discipline in the 1990s and began to grow. Agriculture, accounting, financial market research, insurance, ERP, and data mining are only a few of the business applications of machine learning today.

Business use of Machine Learning

Health-care services

Sensors and devices that track everything from heartbeat rates and steps completed to oxygen and sugar levels and even sleep patterns have produced a huge amount of data that allows physicians to evaluate their patients health in real-time. By looking at photos of the back of the eye, a new ML system can find cancerous lumps on mammograms, skin cancer, and damage to blood vessels caused by diabetes.

Government

ML systems allow public officials to use data to predict possible situations and respond quickly to situations that change quickly. Machine learning will help improve cybersecurity and cyber intelligence. It will also help with counter-terrorism, logistics management, predictive maintenance, and lowering failure rates, among other things.

Sales and marketing

ML is also transforming the marketing industry, with many firms successfully implementing artificial intelligence and ML to boost customer loyalty. ML is also enhancing customer interactions and providing better customer support.

E.g., chatbots and digital assistants

Social networking and e-commerce

E-commerce and social media platforms use machine learning to analyze your past purchases and searches and make recommendations for additional purchases based on what you've already purchased. Several analysts think that AI and machine learning will shape the future of retail. This is because deep learning business systems are getting better at collecting, analyzing, and using data to make people's shopping experiences more personal. Facebook and Amazon are excellent examples of this.

Financial sector

In this industry, machine learning helps investors find new opportunities or figure out when the best time is to invest in certain stocks or sectors. Data mining finds out how risky a customer is and tells cyber security how to find and stop fraud. Machine learning can help with analyzing financial portfolios and figuring out how risky it is to give out loans or insurance.

One of AI and machine learning’s potential strengths in this industry is the capability to test hedge funds and analyze stock market activity to make financial recommendations. ML can get rid of usernames, passwords, and security questions by taking anomaly detection to the next level with facial or voice recognition or other biometric data.

Oil and gas

Machine learning and artificial intelligence are already being used for new oil and gas exploration, looking for mineral deposits in the ground, figuring out why refinery sensors aren't working right, and reorganizing oil distribution to increase efficiency and cut costs.

Machine learning is changing the industry with its case-based interpretation, reservoir simulation, and drilling automation. Above all, machine learning is assisting in the safety of this hazardous industry.

Manufacturing

The vast manufacturing sector is also no stranger to machine learning. The goal of machine learning applications in manufacturing is to improve operations from planning to execution, reduce human error, improve predictive maintenance, and speed up the time it takes to turn over inventory.

Major Machine Learning Vendors (Offer tools and business applications)

· Amazon Web Services

· Databricks

· Google Cloud

· IBM

· Microsoft Azure

· SAS

· SAP

· ORACLE

A Few ML Tools and Applications:

Oracle

Oracle Machine Learning for SQL (OML4SQL) provides powerful in-database algorithms for model building through PL/SQL, along with SQL function functions for applying models to make predictions. This transforms the database into a company-wide analytical tool for data-driven issues like churn prediction, consumer segmentation, fraud and anomaly detection, cross-sell and up-sell opportunity discovery, business basket evaluation, and predictive maintenance.

Oracle Data Miner is an Oracle SQL Developer extension that automates many of the steps in the machine learning process using a drag-and-drop user interface. Oracle SQL Developer is a popular, free integrated development tool for developing and managing Oracle Database in both traditional and Oracle Cloud Service deployments. Users of Oracle Data Miner can share their analytical workflows with other analysts and/or generate SQL scripts to hand over to their IT organization to speed up solution deployment. Oracle Data Miner also has a PL/SQL API for scheduling and automating workflows.

SAP

SAP CoPilot

SAP CoPilot, the company’s digital assistant and bot integration hub, has two goals: to provide business users with a fun, personalized experience, and smartly structured work data to help them achieve better results.

SAP Leonardo

SAP Leonardo is a cloud-based application that provides a large data repository for structured data storage and retrieval in in-memory databases like SAP HANA. SAP Leonardo can be easily integrated with any on-premises or cloud-based application using implementation application services such as IoT, machine learning, analytics, big data, design thinking, blockchain, and digital intelligence.

SAP Predictive Analysis

The SAP Predictive Analysis and Service solution assists in the better prediction, planning, and execution of maintenance and repair work, reducing both scheduled and unscheduled downtime.

It enables the analysis of data using various visualization methods and offers a range of predictive algorithms to predict future business decisions.

AMAZON

Amazon Monitron

Amazon Monitron is a complete system that uses machine learning to detect unusual behavior in industrial machinery, allowing businesses to perform predictive maintenance and minimize unscheduled downtime.

Amazon Comprehend Medical

Amazon Comprehend Medical is a HIPAA-compliant natural language processing (NLP) service that extracts health data from medical text using machine learning—no prior machine learning knowledge is required.

Amazon Lookout for Vision

Amazon Lookout for Vision is a machine learning service that uses computer vision to detect flaws and irregularities in visual representations (CV). Manufacturing companies can improve quality and cut costs with Amazon Lookout for Vision by easily detecting variations in images of objects at scale.

Legal & Regulatory challenges associated with machine learning

Machine learning can and is already creating social problems and labor market transformations, resulting in job turnover and retooling across nearly all segments of the economy.

Destructive acts: Because of deliberate user behavior, failure, faulty programming, or unexpected AI system actions, ML systems controlling machines can damage property or people. The first deadly crash involving a self-driving car occurred in March 2018. Such risks would grow as self-driving cars, drones, and other AI-operated ML devices become more popular.

Lack of privacy: Machine Learning and AI-driven technologies like Face, speech, and behavior identification applications that can be linked to high-resolution cameras and microphones make it feasible to track each move we make in real-time, not only when we are using electronic devices. A public rating program is currently being assessed on a wide range in China, and in certain cases, it is being linked with governmental screening and security programs.

In real-time, the program keeps track of all the participants. and generates an individual public credit score based on information such as dating habits, friends, workout time, favorite TV channels, mobile use, time, effort spent raising children, and so on. The score is then used to gain admission to colleges, obtain a mortgage, fly, book hotels, and so on.

Biased algorithms: There is a possibility of discriminatory outcomes when ML is used as a decision-making tool that is based on statistical models implemented on big data. For example, in recruitment processes, machine learning algorithms have been reported to produce results that are skewed against women.

It is fair to expect ML to be used as a decision-making method in crime investigation, the justice process, credit scoring, university application processes as well as several other areas as the technology advances.

Fake News and Information: ML &AI systems can filter messages — true or false — to have the ultimate effect on our attitudes and beliefs by collecting and evaluating data about us. ML may also generate faces, voices, messages, and social media posts(tweets), and look them appear the same as they were from a legitimate person.

Hacking: Hacking into networks and cracking encrypted systems is becoming easier for machine learning systems. As time goes on, more powerful malicious software will be capable of propagating on a huge scale.

It is easy to imagine situations in which hackers seize control of autonomous vehicles to inflict damage, or hack hospital equipment and switch off pacemakers and other critical lifesaving technology.

Although some of the problems with Machine Learning are common to previous technologies, others are unique to Machine Learning. Machine Learning has elements that make it more challenging to regulate than earlier technologies, as can be seen from the above. The widespread availability of the computer hardware, software, and fundamental expertise needed to create effective ML systems in their various forms has resulted in the innovative creation of ML applications. This rapid and decentralized development poses a significant challenge in terms of efficiently controlling technology without suffocating the benefits of innovation.

Machine Learning

Artificial Intelligence

Written by Chandra Rawat, ERP Analyst

 

How to Delete an Employee ID and Employee Record in HR (PeopleSoft)

Removing Employee ID and Employee Record in Peoplesoft

Picture yourself in a situation where you need to erase an Employee ID or an instance of an employee ID, perhaps due to mistakenly hiring an employee who didn't show up on the first day or left the organization immediately (poor guy, wonder what made them leave so abruptly... 🙄).

So, what's next? Removing the Employee ID in Peoplesoft... 🤐 Nightmare, isn't it?

For tech enthusiasts, the initial instinct might be to pinpoint all records containing the EMPLID field and execute delete scripts for that employee. Seriously...? Want to know a secret? There's a much simpler solution than this... 😉

Deleting EMPLID

Peoplesoft offers a delivered process (HR_PER502) designed to delete an employee ID. Simply input the employee ID here and let the process take care of the rest.

Set Up HCM > System Administration > Database Processes > ID Delete

While the aforementioned process streamlines our tasks, it's important to note that Peoplesoft advises against deleting an Employee ID that has already undergone Payroll processing.

Imagine deleting an Employee after Payroll has been executed for that ID. This would leave the records in a state where pay calculations are disrupted, making it exceedingly challenging to reconcile the figures, potentially leading to Audit or Compliance issues. Thus, resorting to a back-door approach to rectify an error should be avoided. Instead, I would suggest adding a Termination row in such cases to maintain a clean audit trail.

It's crucial to understand that the above process doesn't eliminate all audit records (beginning with AUDIT_...).

Changing EMPLID

There will be instances where you need to modify the EMPLID to a different identifier, such as when an employee rejoins the company and is assigned a new employee ID instead of going through a Rehire action.

Guess what? You can utilize the same process to update an EMPLID to a new number.

Navigate to Set Up HCM > System Administration > Database Processes > ID Change.

Deleting an Employee Record

But hold on - there's an additional valuable delivered process that assists in removing an Employee Record instance created in error. You can access it through the following navigation:

Set Up HCM > System Administration > Database Processes > ERN Delete Process > ERN Delete Process

The system safeguards against the deletion of EMPLID/EMPL_RCD combinations utilized in crucial processes such as payroll, benefits, and time reporting.

In summary, rather than undertaking intricate database procedures involving numerous delete scripts, it's advisable to utilize the Peoplesoft-delivered processes outlined above. We welcome your feedback and suggestions. 

 

Mastering HCM Software in a Flash: A Practical Guide (Not a Magic Trick)

While mastering any Human Capital Management (HCM) software in just a few days might sound like a magic trick, it's definitely achievable with the right approach. This guide focuses on practical steps and resources to dramatically accelerate your learning curve and become proficient in a new HCM system.

Understanding the Landscape:

Before diving in, it's crucial to understand HCM software and its functionalities. HCM encompasses various HR processes, including:

• Talent Acquisition: Recruitment, screening, candidate management
• Onboarding: New hire paperwork, training programs
• Performance Management: Goal setting, reviews, feedback
• Learning and Development: Training courses, skill development
• Compensation and Benefits: Payroll, time tracking, benefits administration
• Employee Relations: Grievance management, communication

Different HCM software caters to specific functionalities and company sizes. Popular choices include Workday, SAP SuccessFactors, Oracle PeopleSoft, and BambooHR.

Preparing for the Fast Track:

Here's how to set yourself up for success:

1. Gather Information: Start by understanding the specific HCM software you'll be using. If possible, ask your HR department or IT team for resources like user manuals, cheat sheets, or online training materials. Research the vendor's website for tutorials, documentation, and webinars.

2. Identify Learning Style: Are you a visual learner who thrives on videos and demos? Do you learn best by doing, through hands-on practice environments? Knowing your learning style helps you choose the most effective resources.

3. Set Realistic Goals: Don't try to conquer the entire system in a few days. Focus on mastering core functionalities relevant to your initial tasks. Break down learning into manageable chunks, like "mastering new hire onboarding" or "understanding employee self-service features."

4. Find a Learning Buddy: Partner with someone familiar with the software. Ask questions, observe them performing tasks, and learn from their experience.

Mastering the Essentials:

Now, let's get tactical. Here's a breakdown of key areas to focus on in your HCM software blitz:

1. User Interface and Navigation:

• Familiarize yourself with the main dashboard and navigation menus.
• Learn how to search for specific functions, reports, and employee data.
• Understand common icons and functionalities.

2. Core HR Processes:

• Talent Acquisition: Learn how to navigate job postings, manage applicants, and schedule interviews.
• Onboarding: Understand the new hire setup process, including creating profiles, assigning access, and triggering relevant workflows.
• Performance Management: Explore functionalities for setting goals, conducting reviews, and providing feedback.
• Payroll and Benefits: Gain basic understanding of managing employee pay data, deductions, and benefit enrollment.

3. Reporting and Analytics:

• Learn to generate basic reports on key metrics like employee headcount, absenteeism, or training completion rates.
• Understand how to filter and export reports for further analysis.

Maximizing Your Learning:

Here are some strategies to accelerate your knowledge absorption:

• Focus on "Doing": Don't just read, actively explore the software functionalities. Practice using features in a test environment if available.
• Ask Questions: Don't hesitate to ask for help from colleagues, HR department, or IT support.
• Leverage Online Resources: Many HCM software vendors offer comprehensive online learning modules, video tutorials, and user forums.
• Shadow a Pro: Learn by observing how others use the software. Shadow an HR professional who is proficient in the system.
• Take Notes: Jot down key features, shortcuts, and workflows for quick reference later.

Beyond the Basics:

While mastering the fundamentals is crucial, remember that HCM software offers much deeper functionalities. Once comfortable with core features, consider exploring:

• Advanced Reporting: Learn to create custom reports for deeper insights into workforce trends.
• System Integrations: Understand how the HCM software integrates with other systems like payroll processors or learning management tools.
• Automation Features: Explore how to automate tasks like sending reminders or triggering workflows within the system.

Remember:

• Practice Makes Perfect: Regularly use the software to retain your knowledge and build confidence.
• Stay Current: HCM software evolves, so keep an eye out for updates, new features, and training opportunities.
• Seek Continuous Learning: Don't be afraid to experiment and explore advanced functionalities to unlock the software's full potential.

Conclusion:

Mastering an HCM software in a few days is a focused effort, not a magic trick. By following these steps, actively learning, and practicing diligently, you can become