Q and A | Mark Smith

Exclusive Q&A What are typical challenges organizations face with people analytics? Deploying people analytics that deliver real competitive advantage must begin with the collection and sharing of accurate, comprehensive data that represents the entire workforce. Many enterprises have valuable workforce data scattered across ERP and HCM applications and in siloed BI tools and spreadsheets that are not cohesively tied together. The full variety of data types needed to effectively support people analytics initiatives are often not widely utilized, and this adversely impacts accuracy, reliability and thus confidence in the data. This in turn diminishes the value of the downstream insights and guidance from analytics based on this data. People analytics should also be presented and consumed in the course of regularly using business software, and ideally in a form that is interacted with naturally by humans whether they have data analytics skills or not. This accessibility is essential for translating analytics into actionable insights. Finally, the time-to-value for analytics deployments often misses expectations due to underlying technology that can be laborious to set up and that does not dynamically apply analytics-related data governance rules. This can occur because leveraging embedded machine learning or other emerging digital capabilities usually requires data science expertise not typically found in HR organizations. What is the purpose of people analytics? People analytics serve many important purposes, but the primary mission is twofold. First, to discover the root causes of and impediments to organizational performance that relate to human capital management (HCM). And second, to guide organizations in the best actions to take to maximize opportunities and minimize risks related to the workforce. Critical to the value to be derived from people analytics are the insights related to high-profile HCM themes like employee turnover and absenteeism, compensation equity and competitiveness, diversity and inclusion, labor cost management and various aspects of workforce-related regulatory compliance. These elements of an HCM agenda are applicable to all organizations—no matter the size or industry—and are inextricably connected to how an organization performs overall. Human resources (HR) leaders should pursue the types of people analytics tools that best help managers and executives mine and uncover the potential of their workforce. They should further realize that direct access to people analytics really should be extended to anyone with people management responsibility, without needing to go through HR or anyone else. How do people analytics users know when certain actions need to be taken related to managing the workforce? Analytics technology related to any specific function within the organization should include notifications and alerts that can be configured to the personalized needs of any individual responsible for decision-making or action-taking. Without question, this process should be designed to operate via mobile devices and across any technology the individual uses within the course of their operations. Notifications should provide appropriate context, guide the individual to more information as needed and enable them to quickly take the best actions. The configurability of thresholds for notifications should cover the complete spectrum of HCM objectives so that every level of employee or manager can work together to address mission-critical issues and comply with HR policies. For example, employee turnover can be more effectively addressed and mitigated when analytics tools provide guidance to managers on how their actions might increase or decrease flight risk among team members. Why are industry benchmarks (such as those indicating pay practices) difficult to utilize within people analytics? The first challenge is the availability of benchmarks that represent actual data from similar organizations within the same industry and business stage. And then, even when this data is available, it can be a challenge to match internal jobs to those used in the market survey to appropriately compare data. This task is made more challenging by the increasing number and variety of job functions within organizations, as these jobs may require skills that are newly catalogued or not well understood. This means the volume of jobs and skills relevant to an organization can become quite unwieldy, and it is difficult to manage this data volume for benchmarking purposes without applying data science and machine learning to actual workforce data, thereby accurately performing job and skill associations and segmentations. How can organizations deploying people analytics maximize adoption, and therefore ROI? To maximize ROI organizations must account for two conditions when planning and deploying these tools. First, ensure the availability of a significant amount of accurate and relevant data that can be applied to the business of people management within the organization. This presence and alignment of data is critical because analytics and insights are only as good as the data they’re based on, and the amount of data will have a proportionate impact on the reliability of algorithm-driven predictive analytics. For example, a flight risk prediction model should be powered by a very large and diverse sample size of employee data or the model will ultimately under-perform in certain situations. When this model breakdown happens due to lack of data, analytics adoption and ROI objectives will be compromised. Second, insights generated by the tool must be actionable for every appropriate individual. A tried and true method for achieving actionability of information is to frame results and recommendations as a story, or in the language that people read and speak. People analytics without storytelling will likely not deliver against expected adoption and ROI targets since the time and process needed to understand the meaning and implications of the analytics will typically take place outside the normal flow of work.

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