Dispelling the Big Data Myth: A Practical Take on Talent Analytics
It has become a truism that organizational talent is the biggest competitive advantage in the modern marketplace—and the biggest threat. As digital technology revolutionizes what can be known about employees and how that data can be leveraged by companies and competitors alike, massive generational workforce shifts put critical business intelligence and management capability at risk.
Securing meaningful and actionable talent data is imperative to mitigating these risks and maintaining, if not gaining market share. But many companies ‘don’t know what they don’t know’ about their talent and are stymied about where and how to start. In some cases, they lack the technology and capabilities to capture and apply talent data in meaningful ways. In others, they lack the leadership needed to change these factors and an effective decision framework to act on the data they have.
Securing meaningful and actionable talent data is imperative to mitigating these risks and maintaining, if not gaining market share.
This lack of readiness comes at a high cost. Companies that don’t leverage talent analytics are at risk of losing relevance to their customers and employees. Talent analytics may be defined simply as finding meaningful data elements and relationships to make fact-based people decisions. In day-to-day practice, however, what does that mean?
It's not about the numbers
Gathering and using data for modeling and forecasting are just part of effective talent analytics. Elegant visualization and story-telling—creating “beautiful evidence,” per Edward Tufte—are also critical. In fact, the value of talent analytics may lie less in data itself and more in how data is interpreted and presented to create the intended awareness and impact. Once a niche specialty, impactful data visualization is now equal access thanks to tools like Tableau and Power BI.
The business benefits are clear. Multiple studies point to the value of using talent analytics to help drive critical measures like best-in-class business performance and higher revenue per employee. In Competing on Talent Analytics, for example, the Harvard Business Review describes net increases in employee engagement, operating income, and high potential performance and retention at companies like Starbucks, The Limited Brands, Best Buy, AT&T, Google, and Sprint—all of whom have dedicated talent analytics functions and use talent data to drive people processes and practices.
One size does not fit all
It is a common assumption that business-critical talent metrics are standard across companies and that, once those metrics are defined, capturing employee data is the primary work. As a result, many companies devote more effort to data capture than to data analysis.
Talent metrics are not one-size-fits-all, however. Data that is essential for one company may not make sense to another depending on several factors:
- Strategic business objectives and success measures: What a company is trying to achieve and how it defines success
- Stakeholder needs and engagement: End-user access to data, involvement in data management, and constraints
- Data systems and visualization maturity, capacity, and intent: Ability to effectively visualize and manage data
Peter Drucker is attributed with saying “culture eats strategy for breakfast.” The same might be said for cloud maturity. While an intentional approach to digital technology is imperative in the modern marketplace, organizational leadership and culture may still counter-balance technical sophistication in driving business results.
Best-in-class leaders create cultures that are responsive to all types of data and value evidence-based decision-making. They demand hard and soft people data and drive operations that are agile in identifying and aligning people metrics to key business results.
Finding the right balance
Understanding what drives a company is key to identifying which metrics should guide strategic decisions. A business decision framework is one way to map top strategic differentiators and risk tolerance alongside the talent metrics and people processes that drive them. Another key tool is a change management plan that addresses analytics findings.
An “all or nothing” mentality can hold companies back from investing in talent analytics. Talent analytics maturity may range, though, from simple metrics and dashboards to predictive modeling and data-driven decision-making. Taking a modular approach and scaling capabilities—maintaining legacy platforms as needed while adopting agile analytics processes for developing tools and delivering data—is a good way to start.
Following your employees’ lead can also help set talent analytics priorities. Modern employees expect companies to use data to connect them more closely with the products, services, programs, and experiences they provide. Leading companies leverage data from inside and outside the corporate environment to anticipate and address employee needs.
The end result? Active engagement that drives long-term interest, productivity, and loyalty. Competitive advantage at its best.