by Kon Leong, CEO of ZL Technologies
So you’ve hired someone. Now what? It’s time to maximize their potential. With "Big Data" analytics, this is increasingly becoming reality, yet it’s often being ignored in favor of analytics that attempt to screen individuals prior to the initial offer.
Departments are hunting for someone who is perfect on paper, perfect in person, and perfect in predictive analysis of a vast constellation of traits related to aptitude and personality. Corporations are searching for the right combination of hiring metrics that correlates with long-term performance, yet recent high-profile examples have shown that these attempts are often in vain.
Even more troublesome is that the trend to scour personal data for hiring purposes has been steadily treading into murky waters; with questions of ethics and privacy being raised, many states are pushing forward with legislation that prevents unwarranted mining of personal information. Companies are in an arms race to collect and use data, but up in arms on how to use it effectively with minimal legal risk.
Perhaps it’s time to focus on data’s value in assigning relatively new workers to their best fit once they’re already hired, using data that’s generated right within the walls of the corporation. This "downstream" data, already managed for other reasons such as regulatory compliance, offers a powerful glimpse into the human metrics of the business process… while largely sidestepping the ethical issues of personal data.
One size doesn’t fit all: Big Data for a better employee fit
Analyzing the business communication and work products of individuals can reveal strengths in a tangible way that is often not possible at the hiring stage. The reality of recruitment is that businesses often end up with talented individuals that aren’t exactly what was expected. At that point, the key is finding a role that benefits them AND the company.
With the high investment and commitment associated with a new team member, it’s in the best interest of business to see that the individuals are assigned to situations which take advantage of their aptitudes while keeping them satisfied and engaged. After all, with the hiring process typically creating a strong filter for company culture and quirks, there’s usually a reason the person made the final cut. There’s no reason to leave talent untapped.
Besides, people can also change over time. Honeymoon phases are inevitably human, with the employer-employee relationship no exception. Analytics tools to prescreen candidates are slowly improving, but they’re not mature… and increasingly present thorny privacy issues. Instead, we often find those who are hired settle into a profile different from what we originally expected.
Previously unstated skills can surface, and gleaming rapport that shone brightly in an interview can fade. Adapting to individual changes can be the difference between jamming a square peg into a round hole and finding a snug fit that keeps a team interlocked and strong.
Big Data goldmine: Mining what you already have
Fortunately, the enterprise is already rich with company-owned data about the way people work. Business emails, documents, instant messages, collaboration suites, and other "unstructured" data generated by humans can offer insight into skills, productivity, strengths, attitudes, and bottlenecks. The data already belongs to the corporation, and doesn’t violate legal bounds.
Ability to harness this information is in relative infancy; however, there are steps to take that can maximize its future value to the HR department and employees alike:
- Connect the dots between departments. Unstructured data management has many stakeholders, and it is critical to be on good terms with them. HR cannot operate alone, especially if data is needed for analytics. Communication with IT, records managers, compliance officers, and legal teams will strengthen the likelihood of working towards common goals that benefit everyone without needless overlap of effort.
- Centralize all unstructured data for common use. Most firms try to manage unstructured data. Few manage it in a centralized system where multiple business units can simultaneously access it for real-time global searches and content-based analysis. In order for the big picture to be seen in communication trends, workflows, and efficiency, all unstructured data types need to be aggregated.
But most IT systems don’t work this way. By partitioning data based on function, department, or type, the business creates isolated "silos"–islands of data that have little ability to exchange information with each other. That restricted flow of data makes it very difficult to look at the large batches of information that reveal worker trends at the enterprise-wide level.
- Move analytics engines–NOT mountains of data. Another problem facing data analysis is that most companies try to bring limited sample sets–which can be biased–to stand-alone analytics engines or to companies offering online analytics services. Data centralization enables unstructured data tools to be built into or brought to the complete data universe once they are available, so nothing has to be left out of analysis.
If all data is stored in a single repository or system, there is no data to be moved or exported. Because information is ingested directly into the environment as it is generated by workers, it can potentially be sifted in real time.
- Stay tuned in to social channels (without being creepy). Everyone wants happy employees, and sentiment analysis of business social data such as enterprise instant messaging platforms can make it easier to achieve. Emotional tone and changes in communication can pinpoint frustration, problems, or unhappiness that individuals may be hesitant to admit to management.
Ultimately, HR should stay true to its core function: allocation of the human resource. Yet, like any resource, it is finite. Analytics can interpret data to optimize talent in ways that benefit everyone, but it takes planning. To leverage Big Data, HR needs to think big and work outside the department bubble, for the benefit of all.
Kon Leong is CEO/Co-founder of ZL Technologies. For two decades, he has been immersed in large-scale information technologies to solve “big data” issues for enterprises. His focus for the last 13 years has been on massively scalable archiving technology to solve records management and eDiscovery challenges for the government and private sectors. He speaks frequently at records management and eDiscovery conferences on cutting edge trends and solutions. A serial entrepreneur, Mr. Leong earned a BS degree from Loyola (Concordia U) and an MBA from Wharton (U of Penn).