Overcoming HR’s Decision Paralysis: Making Sense of Data and Connecting the Dots to Business Outcomes

Jacquelyn Soh
September 2, 2020

Trusting data to inform our decisions can be daunting. This is especially so  for HR professionals who have not been expected to be data fluent beyond the occasional Excel manipulation – until now. 

Increasingly, business leaders are turning to their colleagues in HR to help them understand where the gaps in the companies’ talent force are, what skills and capabilities will be needed as they transform into a fast-changing digital business, and most recently, how can they enable their teams to be productive and future-ready through the tumult of the present moment. 

Adrian Tan, Practice Leader of Future of Work Tech at People Strong, recently partnered with Michelle Hancic, pymetrics’ Global Head of Consulting Psychology, to untangle some of the myths around HR analytics. In unpacking the notion of ‘perfect data’, Michelle and Adrian delineate a path from how HR professionals should approach data collection, to what kind of meaningful action we can spur with people data.

Myth 1: “I do not have the data I need.”

In the first poll of the webinar, 60 percent of the audience indicated that a lack of quality data was one of their biggest challenges with people analytics. HR collects an abundance of data today. From employee count to compensation and benefits, annual engagement surveys to managerial reviews, it is unlikely that we don’t already have the data we need. A more pertinent question is, how do we assure that the data is accurate, synchronous, and goal-driven so that we can  derive value from it?

Technology holds much promise to resolve data quality on all three fronts. As Adrian calls out, technology is particularly effective at minimizing human errors. It can nudge us towards cleaner data entry, such as when forms limit the type of characters you can enter (letters vs numbers). It can also capture information in the background without the need for conscious human input, such as data for the purposes of organizational network or sentiment analysis. 

On data synchrony, investing in a cloud platform as your core system with which other applications can integrate can help streamline your data sources and  give you complete visibility of your data.  Urgently, HR leaders need to tear down the old divides within their function to maximize the full potential of technology.  Who is maintaining the data? How do we know what data has already been captured? Is interpretation consistent? These are questions that have clear answers only when HR operates as an agile, unified function instead of within rigid silos. 

To illustrate, Michelle cites how Talent Acquisition collects a huge amount of information about candidates during recruitment through their CVs and the multiple stages of pre-hire assessments. Unfortunately, these insights are rarely accessible to their Talent Management counterparts to inform L&D planning or individual career development.  Aside from likely duplicating data collection efforts, it also reduces the organization’s ability to redeploy people efficiently and effectively during workforce disruptions. 

Above all, we need to recognize that there is no perfect dataset, nor does more data always equate to better insights. Taking a step back, HR leaders should first define clearly the business problem or objective that they are hoping to address. Start small if necessary. Then, set parameters for the time and scope of your data collection. Finally, apply your experience and knowledge of the broader context to dig deeper and  guide your analysis. 

This brings us to the second myth. 

Myth 2: “I should replace intuitive decision making with data.” 

On the one hand, yes. A recent HR Digital Leadership Workshop organized by Thrive HR Exchange, in partnership with pymetrics and the Centre for Creative Leadership, confirms that HR Leaders skew towards intuition when making decisions. This default creates vulnerability to experience bias, whereas data might aid to surface blindspots and challenge assumptions about a situation.

Good data can offer legitimacy to our decisions as objective, supporting evidence. Outside of HR, departments across Finance, Sales, and Marketing have already proven the power of data to optimize and drive strategy.  As Adrian firmly puts it, “You cannot improve what you don’t measure.” The necessity is amplified in today’s turbulent market where organizations are forced to respond to situations that are, as the experts describe, unprecedented. Past experience is limited under these conditions.

On the other hand, Michelle acknowledges that HR’s vast prior knowledge can come into play when framing and prioritizing questions. Without that background, organizations risk wasting resources on collecting and analyzing  data that isn't aligned with their objectives. 

Instincts borne out of experience can help HR professionals discern if a data trend is due to correlative or causative factors, or if it's anomalous or indicative of a bigger issue that demands further investigation.  For example, a 99% satisfaction rate in your pulse survey might indicate that the company is doing a phenomenal job at employee engagement. Or, it might be a reflection of the office culture’s aversion to criticism. Without a robust understanding of the cultural context, accepting the results at face value might hamper your strategies down the line. 

The key is to keep a curious mind and iterate between data and intuition.

Myth 3:  “I need strong statistical knowledge to apply data successfully.”

Of course, having a strong grasp of statistics is helpful when scaling a people analytics project. Data fluency affords you greater freedom and flexibility to develop and pursue hypotheses. It also allows you to progress from reporting the situation as is, to prescribing solutions to the business.  Up-skilling HR functions to be more comfortable with managing, validating, and auditing data is therefore vital as we  grapple with increasing flows of human capital data. Bearing in mind that it typically takes 30-40 months for functions to be capable of producing predictive and prescriptive values, the time to start training is now.

It is a misconception that data fluency is all about crunching numbers. Being able to shape data into compelling stories is essential to strengthen buy-in from your stakeholders.

Nevertheless, this does not mean less data-literate leaders are incapacitated in the short term. Platforms like PeopleStrong’s Alt Analytics dashboard or pymetrics’ Workforce Insights can provide you with useful analysis with just a few easy clicks. Importantly, you need to ensure that your solution provider can account for the validity and fairness of their data. pymetrics’ white-box AI approach for instance means that you have full transparency of the data that goes into our algorithms and the outcomes.

Lastly, it is a misconception that data fluency is all about crunching numbers. Being able to shape data into compelling stories is essential to strengthen buy-in from your stakeholders. Adept HR Leaders understand the need to connect employee contributions to broader business goals because the cultivated sense of purpose improves productivity. Those experiences of fostering connections and meaning-making can be applied to communicate insights and urge actions more effectively.

In closing, Michelle reminds us that there is no need to be overwhelmed. To start, there are plenty of resources available online, both paid and free. Seek out colleagues in other departments who have gone through the process, and if possible, leverage the expertise of your internal analytics function for guidance. If there’s one thing to take away from this webinar (and the ten others that you have attended), people are willing to help.  

Click here to watch the webinar replay, or learn more about how pymetrics is redefining talent data for better predictions about fit and potential here.