OREANDA-NEWS. March 28, 2016. Can you quantify what drives employee performance, how people learn on the job, and what it takes to keep employees engaged?

These are just a few of the questions human resources professionals grapple with daily. Increasingly, talent analytics – harnessing “big data” about employees to gain insights that can more tightly link HR and business strategy – is resulting in more meaningful answers.

If you feel you’re still learning about talent analytics, you’re not alone. In 2014, Deloitte noted in its Global Human Capital Trends report that only 7% of large companies rated themselves as having “strong” HR data analytics capabilities. Indeed, Deloitte found that only 14% of companies had any form of talent analytics program in place, though more than 60% were intent on building one.

As we’ve built our talent analytics capabilities at BNY Mellon, I’ve followed some simple guiding principles grounded in learnings from peers and best practices.

· Bust the myths and use data to drive management decisions: As your organization shifts to a more data-driven mindset, you’ll find some deeply rooted myths that developed based on instinct and perceptions. There will likely be surprises in the data you collect on retention, promotion, diversity and inclusion, and other talent-related topics. For instance, our data showed that we are filling a high percentage of our open positions internally, but the perception was that the internal fill rate was much lower. The data should be shared with key decision makers to dispel any myths and ensure that future decisions are grounded in an accurate understanding of your talent. One way we’re doing this is by piloting a Global HR Scorecard that enables managers to access and drill down on data in real-time.

· Internal data is not enough. Paint a full picture: Mastering internal data quality is critical. You have to know and understand your own people data first. But effective data-driven decision making shouldn’t be based solely on the data available within your organization’s four walls. Do you have a data-supported view of what’s happening in the talent marketplace? For example, if your business strategy calls for a ramp up in specific capabilities, you want data that shows the best markets for those skills so you can search in the right locations. The external context can help you make smarter decisions.

· Seek to find the data that matters most: Companies have moved beyond being creative about data capture. The challenge now is using that data to answer the questions that matter most for your organization. HR is rich with data and it’s tempting to look at a broad range of issues, but focus is critical. Conversations around talent analytics have tended to focus on predicting and minimizing turnover. But is that even an issue for your organization? If it’s not, focus on the issues that will allow you to move beyond an operational workforce plan to a strategic one that drives your desired business outcomes. For us, that means using talent analytics to understand the effectiveness of our existing employee engagement, and learning/development programs on our employees’ success and satisfaction. Talent analytics should firmly link your talent strategy to the business strategy.

· Soft skills still matter: The “human” touch in human resources – such as empathy, listening, communication, and conflict resolution – remains critical in analyzing and applying talent analytics learnings to your organization’s strategy. With myth busting in particular, soft skills are important in helping us understand why people have certain perceptions and how we can address them. Many talent analytics teams will stop with “here are the facts,” and that is where many fail. Successful teams are good at aligning their work with strategic goals, and then making sure they address perceptions with both the hard data and soft skills to truly drive change and long-term success.

I have challenged our organization to use all of the current tools and processes we have to mine data and make it mobile so we can manage in a more evidence-based way – and we already see it starting to pay off by changing our mindset around certain talent investments.
What lessons have you learned about talent analytics?