Take the Guesswork Out of Talent Decisions With Big Data

by   |   October 1, 2015 5:30 am   |   0 Comments

Dave Weisbeck, Chief Strategy Officer, Visier

Dave Weisbeck, Chief Strategy Officer, Visier

Even the best, most experienced managers are prone to bias when making decisions about people. It’s only human nature, after all: Our natural propensity for bias can explain why a job applicant is less likely to be admitted to medical school when interviewed on a rainy day or why taller presidential candidates win.

Every day, managers make costly, time-sensitive people decisions: A team member has been offered a higher-paying position at another company – should I counteroffer? What should I counteroffer? I have a new leadership position opening – who is my best internal candidate? An employee has asked for a promotion – should I give one?

When these decisions are made based on intuition alone, we risk engagement, employee, and business performance, as well as incurring unwanted costs. With human capital costs typically well over half of the operating expenses of most organizations, the stakes are simply too high to allow these decisions to be influenced by cognitive bias.

Supporting and educating managers to make decisions based on data solves only part of the problem in reducing cognitive bias. The first challenge is acquiring the employee data, which often is locked in HR management systems that were never designed for analysis. This is partially because of the way that systems that store employee data have evolved into multiple silos (each function, such as compensation, learning and development, recruiting, and performance management has its own set of transactional data that typically can’t be linked with other systems).

To make sound talent decisions based on a more complete analysis of the situation at hand, this disparate employee information needs to be connected. Determining when and what to counteroffer, judging if a succession candidate is ready for a leadership opening, and responding to a promotion request are three examples of common talent decisions that can benefit from this unified-data approach.

Evaluating Counteroffers

When a team member has been offered a higher-paying position at another company, it can create a big dilemma for HR business partners and the managers they support: Should the manager counteroffer, what is the best counteroffer? Or do they let the talent walk away?

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The first instinct often is to look at the individual’s pay in relation to her assigned salary band. Making the right decision, however, requires going beyond this single question to look at data from multiple angles (multidimensional analysis) and making comparisons to relevant peer groups (cohort analysis). For example, the individual is high in her salary band, but when was the last time her salary changed? Or when was the last time she was promoted? Is she due to be promoted to a higher salary band? Does her performance, potential, time in role, and time since promotion compared to her peers and others who have been in a similar situation justify a promotion? Would a promotion be a more powerful technique to retain this person, or does the analysis show that we should let her continue her career elsewhere?

For most companies, the employee data required for this type of analysis resides not only in the HR management system, but also in many other HR systems, such as recognition, payroll, performance management, and more. Performing this analysis using a standalone, embedded analytics solution (a capability delivered within transactional business applications, such as payroll administration systems) will tell only half the story. Many of these types of solutions cannot link recognition and compensation data, for example.

Here, analytical processing and data visualization technologies that pull together multiple systems and are delivered independently of the individual transactional systems can be a significant boon: HR business partners and managers can quickly make employee comparisons in real time using different dimensions. They also can compare across the organization to peers in different locations or departments, rather than being limited to the individual’s immediate team.

Filling a Leadership Opening

When a new leadership position opens up, it may be tempting to begin shopping around immediately for new talent outside of the organization. But the first step is to determine who from within the organization would best fill the role: According to a study at the University of Pennsylvania’s Wharton School, external hires not only get paid more, but also receive significantly lower marks in performance reviews during their first two years on the job.

Recruiting departments are set up with processes ranging from résumé capture to behavioral assessments that provide insight on candidates, but internal candidates are often assumed to be already known. From sourcing to evaluation, the data to help identify and decide on the best internal candidates is often locked away in multiple systems. Especially when making decisions on the most senior roles, many stakeholders who are involved need to be able to compare and contrast potential candidates. This requires a more sophisticated cohort analysis, which involves examining different employee attributes of each candidate and comparing across candidates to understand how they differ in relevant experience, potential, training, performance, and many other talent review criteria. This information lives in recognition, performance, learning management, and other systems.

When these systems are linked via data normalization and delivered through an in-memory platform (which processes data 250,000 times faster than a traditional database), HR business partners and the managers they support can have a more productive, real-time conversation about the pros and cons of each candidate.

Responding to a Promotion Request

Promotions are a very public form of recognition, and when an employee comes forth with a request to be promoted, the consequences of making the wrong decision go beyond the individual in question. An unjustified promotion may undermine the engagement of other employees or lead to a tidal wave of promotion requests. Declining a promotion request, however, may make the employee a risk for voluntary turnover. With costs to fill the person’s vacant position typically 1.5 times that person’s annual base pay, this also is a scenario that is best avoided.

When an employee requests a promotion, the manager’s inclination may be to accommodate the request in an attempt to keep that person from leaving. The employee’s manager needs to ask some tough questions: Is this person ready for the new responsibilities? How do time in role, related experience, performance, and other measures of career progression compare to others in his current position and in the new position? Is he at risk of leaving? Is that risk significantly increased without a promotion? Has he demonstrated the level of long-term potential that would warrant an exception to our promotion practices?

This is a complex judgment call that requires a thorough review of performance history, including data from recognition applications and the learning management solution (LMS). It also requires insight, drawn from the talent management system, into the person’s potential rating and how this has evolved over time. The third element to consider is how easy it would be to replace this individual, either with an external hire or with someone from inside the organization, by considering the data from the applicant tracking system.

In-memory analytics solutions can bring all of these data silos together in one place, enabling business users to address this type of complex and critical people management issue.

Painting One, Clear Picture

Ultimately, talent decisions are grounded in human judgment. That is – and will always be – the nature of business and people management. But all too often, HR relies on isolated metrics delivered via standalone applications when helping managers address important questions about individual employees. This does not paint a clear picture of an employee’s past or his or her future impact.

These decisions become far more sound when considered more holistically. With recent advancements in big data technologies, organizations can tap into a unified workforce data ecosystem, uncovering exciting, objective and actionable insights.

A seasoned software executive, Dave Weisbeck’s experience ranges from building development teams to growing multi-billion dollar businesses as a General Manager. With 20 years in the information management and analytics industry, Dave’s prior roles include developing Crystal Decisions and Business Objects products and product strategy. Most recently, Dave was the Senior Vice President and General Manager responsible for Business Intelligence, Enterprise Information Management, and Data Warehousing at SAP. Dave holds a position on the HR.com Advisory Board.

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