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Featured
5 min read
Hayley Bresina
In today’s dynamic workplace, understanding your organization and its people is key to driving change. Predictive analytics and behavioral insights help human resources cut costs, improve efficiency, and drive impact. By forecasting future trends and uncovering root causes, predictive models empower HR professionals to act proactively—whether it’s reducing turnover costs, optimizing team structures, or boosting performance. Predictive analytics isn’t just a nice-to-have—it’s a necessity. Organizations that don’t adopt it risk falling behind competitors using data for smarter decisions and better outcomes. At the core of this shift are HR analysts and One AI machine learning. While data scientists are often the go-to for building machine learning models, HR analysts—human resources, people workforce, or employee analysts—bring a unique advantage. Their deep expertise and familiarity with organizational data make them uniquely well-equipped to unlock the full potential of One AI. Here’s why: They Understand the Data Like No One Else HR analysts are the stewards of their organization’s data, familiar with the nuances, outliers, and trends in employee demographics, engagement surveys, and performance metrics. A study in the Journal of Internet and Information Systems (2022) found that domain expertise is crucial for building effective models, particularly in fields like human resources, where understanding context is key (JIER, 2022). Models informed by domain experts were more accurate and actionable, as they can identify the most relevant data and filter out noise. This expertise ensures models are built on high-quality data, leading to trustworthy insights that drive results. They Bridge the Gap Between Data and Strategy One of the key advantages HR analysts bring is their ability to connect data insights with strategic goals. Unlike data scientists, who focus on technical accuracy, HR analysts align data with outcomes that matter—like improving retention, engagement, and performance. The study in the Journal of Internet and Information Systems (2022) also highlights that HR professionals’ ability to align insights with business strategy ensures analytics are not just interesting, but also impactful. One AI supports this alignment with intuitive visualizations and easy-to-use exploratory data analysis (EDA) tools, allowing HR analysts to uncover actionable insights quickly. For example, in a retention model, HR analysts might find that “team size” predicts attrition. By analyzing this, they could discover that large teams lead to disengagement due to weak personal connections and managers struggling to provide feedback. Insights like these help HR leaders adjust span-of-control policies to optimize team size for both employee satisfaction and managerial effectiveness. With One AI’s advanced EDA and visualization, these insights are clearly presented, enabling HR analysts to turn findings into strategic actions that drive organizational change. They Make Machine Learning Accessible and Ethical HR analysts need to present insights to non-technical stakeholders, making explainability key. One AI’s transparency features—like explainable outputs, adjustable parameters, and clear performance metrics—allow analysts to understand and adjust models, ensuring predictions are clear and defensible. Its intuitive interface helps HR professionals and analysts tailor models while keeping performance metrics front and center to avoid underperforming models. This level of transparency fosters trust in analytics, a critical factor in human resources decision-making where outcomes directly impact people. McKinsey (2023) found that explainable AI improves trust and adoption, particularly in high-stakes fields. One AI also helps HR professionals make accountable, actionable insights (McKinsey, 2023). Additionally, HR analysts are well-versed in ethical data use, making them natural stewards of fair AI. The International Journal of Research Publication and Reviews (2023) emphasizes that explainable AI supports accountability and ethical HR practices (IJRPR, 2023). They Thrive with User-Friendly Tools While data scientists often rely on coding and complex algorithms, HR analysts shine with intuitive, user-friendly platforms like One AI. Built for HR, One AI provides guided workflows and automated processes that remove technical barriers without sacrificing analytical depth. For example, when building an attrition model, analysts can start with pre-set best practices, ensuring a solid foundation without needing technical expertise. If adjustments are needed—like modifying thresholds or adding variables—the interface makes it simple, with clear explanations of their impact. This blend of automation and flexibility lets HR analysts focus on actionable insights rather than navigating technical complexities. As The HR Digest (2024) notes, the right tools empower HR professionals to make data-driven decisions, no matter their technical background (The HR Digest, 2024). Unlocking the Potential of HR Analytics When HR analysts combine their expertise with One AI, they do more than build models—they uncover insights that drive organizational change. By blending their deep data understanding with machine learning, analysts can predict outcomes, identify key behavioral drivers, and implement strategies that deliver results. This collaboration helps organizations make informed decisions, enhance employee experiences, and achieve business goals. For decision-makers, the message is clear: Equipping analytics experts with the right tools strengthens and supports the organization. For HR analysts, using One AI allows them to expand their impact, turning data into actionable strategies. The real question isn’t whether HR analysts should use One AI—it’s how quickly will you enable them to lead in People Analytics?
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Featured
8 min read
Hayley Bresina
So, you used a One AI recipe to build a strong machine learning model and deployed it—congratulations! But the journey doesn’t end there. Building a model isn’t just about making predictions; it’s about transforming them into insights that help you understand your organization and take purposeful actions to improve processes and address challenges. For instance, identifying the key drivers of high performance can inform targeted training programs, while understanding factors behind low performance might guide role adjustments or address issues affecting outcomes. In One AI, our visualization templates go beyond raw predictions, offering strategic insights and actionable opportunities for data-driven decisions. Built from tried-and-true visuals refined over years with clients, these templates simplify complex results and make them easier to communicate to stakeholders. By using these ready-made templates, you save time and benefit from visuals that have proven successful across organizations. Let’s explore some of the visualizations available in our templates—tools you can deploy and customize to maximize value for your organization. Visualizations That Empower Strategic Decision-Making 1. Model Performance We believe in transparency, which is why model performance is front and center. This visualization includes key metrics like F1 Score, Precision, and Recall for both classes (high and low performance). Showing performance for each class is critical, especially in imbalanced datasets common in people analytics, like voluntary attrition or new hire failure. For instance, if only 10% of employees are exceptional performers and the model always predicts average performance, it might appear highly accurate overall but completely fail to identify high performers. Breaking down metrics by class ensures you get a true picture of your model’s effectiveness and avoids misleading conclusions. 2. Drivers Visualization What factors drive high or low performance in your organization? The Drivers Visualization shows the most important factors for each outcome and how much they contribute. The drivers are ranked by their impact, making it easy to see what matters most. For example, previous performance ratings or tenure might strongly influence whether someone is likely to perform well or not. Understanding these drivers helps you focus on the right areas to improve performance or predict outcomes more effectively. 3. Filtered Drivers Visualization One of the most powerful aspects of these visualizations is their flexibility. By filtering the Drivers Visualization by specific groups (e.g., department, tenure, location), you’ll see how the drivers rearrange themselves for that subgroup. This dynamic capability, powered by SHAP, lets you drill down into group-specific insights. It’s a game-changer for organizations looking to create tailored strategies that address unique needs. 4. Feature Directionality and Impact Sometimes, the same driver can influence both high and low performance but in different ways. This visualization helps you understand not only the directionality (positive or negative impact) but also the magnitude of each feature’s contribution. For instance, a factor like salary might slightly predict low performance while strongly predicting high performance. These insights guide where to focus your efforts—start with features that have high impact and are feasible to influence. 5. Where Does High Performer Potential Sit? This visualization provides a clear breakdown of high-performance likelihood across key dimensions such as organizational units and job levels. It categorizes employees into low, medium, or high likelihood of achieving top performance, making it easier to pinpoint where high-potential employees are concentrated. For example, executives may have a higher likelihood, while other groups may need targeted development. The ability to compare across dimensions ensures that your strategies are focused and effective, helping to drive meaningful improvements throughout the organization. 6. Individual Predictions Drilling down to individual employees, this view shows each person’s likelihood of being a high performer, alongside the features most influencing their prediction. While machine learning is most accurate at an aggregate level, individual predictions are still valuable for tailored actions, such as identifying candidates for leadership development programs. Note: This visualization must be permissioned and should be used thoughtfully. The Value of SHAP and Customization All these visualizations use SHAP (SHapley Additive exPlanations), a method that ensures transparency and flexibility in understanding model predictions. SHAP works by building a small, individualized model for each data point (such as an employee) to explain why the main model made its prediction. This means SHAP doesn’t just tell you what the prediction is—it shows you how much each factor contributed to that prediction. Because SHAP works at this detailed level, you can filter or split results by any dimension relevant to your data, such as department, tenure group, or geographic location. The visualizations then dynamically adjust to reflect those specific groups, helping you uncover trends and insights tailored to your organization’s needs. From Insights to Action The ultimate goal of these visualizations is not just to analyze your data but to inspire action. By identifying the key drivers of performance, understanding how they vary across groups, and pinpointing areas for improvement, you can make strategic decisions that align with your organizational goals. Use these tools to: Design interventions that address critical factors; for example, focus on key drivers of low performance. Develop targeted programs to support specific groups; for example, create training for new hires or managers. Inform long-term planning and organizational strategies; for example, guide succession planning or hiring strategies. Remember, machine learning is a tool to help you understand and shape your organization. The visualizations in One AI are here to ensure you’re not just building models but also building a stronger, more informed organization. Curious about One AI?
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