As HR applications continue to evolve, HR needs to consider the new data sources and types that are coming towards them. It is no longer sufficient to simply track basic employee information. With the evolution of HR, companies now have wide ranging options including video interviews, dynamic and social learning and development solutions, social monitoring for talent identification and internal collaboration, external survey benchmarks (including but not limited to salary, skills, performance, behavior, and personality), application logs, and predictive models for understanding cultural fit and preparedness for new jobs.
This complexity provides the need for HR Big Data. To deal with the variety and velocity of social data, video, documents, and transactional logs, HR departments need to work with other departments that may have already needed to work with these new data sources. Social monitoring is typically associated with marketing, video is often seen as a corporate communications or public relations tool, document management is seeing a new renaissance with the development of social and cloud-based software solutions, and transactional and network logs are core IT tools.
It is important to differentiate between basic HR analytics and data integration and real HR Big Data because there are significant technical differences between these two use cases. If your CIO hears the demand for HR Big Data, they will immediately think about the challenges of new and emerging technologies that your organization may not be prepared for. If you have a strategic need to be more social, integrate video into your business processes at scale, or to granularly analyze user activities, you may very well need an actual Big Data deployment to track and analyze all of the information correctly.
To make HR Big Data work, HR needs to work hand-in-hand with the CIO. Despite the hype stating that the CIO is losing its grip on the technology budget, the CIO office is still uniquely qualified to implement emerging technologies and properly integrate them with current technology investments. Even if IT lacks the internal resources to fully carry out Big Data, the technical aspects of sourcing a Big Data consultancy require fluency in the size, scale, redundancy, and performance associated with developing the next generation of data management and analytics.
In addition, by having Big Data capabilities, HR can start thinking about the next generation of HR technologies. DataHive is looking forward to advances in several human resource areas as data evolves.
1) Workforce management is currently handled by some combination of time clocks and productively metrics handled through analytic solutions. But what if we could move to the next level of workforce tracking and productivity? DataHive suggests learning a lesson from the NBA, which is attaching wearable sensors to their employees. Companies have to balance the need for data with privacy concerns, perhaps by using geofencing capabilities to ensure that these sensors can only work in very defined areas. But the information associated with understanding how employees work together to create real-life team dynamics or to compare desk jockeys with social chatterers could be invaluable. Perhaps those wanderers really are just talking all day long. Or perhaps they serve as important social hubs that provide cross-departmental communication. Personalized sensors, used correctly, could be an important step forward to track not only when work is done, but how work is done.
2) Employee productivity is often tracked and measured in incremental ways, but DataHive finds that this approach often either massively overrates or underrates the value being claimed. On the one hand, consider an application that provides 9 seconds of productivity a day, then scales to 16,000 employees to provide 144,000 seconds or 2,400 minutes or 40 employee-hours of business productivity every day. Is this really providing value? Consider that 7% is often used as a rule of thumb for how quickly US public markets grow year after year. This means providing 7% more value year after year, which means a minimum of 7% extra productivity. Companies need to find 33.6 minutes of time saved per day every year just to keep pace with the market, much less get ahead. On the other hand, recent vendor-based BYOD studies (so, there’s a grain of salt right there…) claim to provide 30 minutes or an hour of productivity per day. If that is true, than any other productivity gains should be leading to wildly profitable results where these companies are annihilating the market.
However, with a Big Data approach, companies can start to get closer than ever to finding a real and data-based measurement of productivity. This requires a combination of understanding application transactions, tracking the individual user experiences of each employee on a click-by-click level, and translating employee actions into specific employee deliverables and actions. This end-to-end view of labor is still just starting to be developed and DataHive looks forward to being an HR data whisperer in translating Social Big Data into the next generation of HR.