Today’s HR landscape is more complex than ever before: globalization is taking the competition for talent worldwide, four generations are in the workplace at the same time, and contract workers account for at least one-third of the workforce. As a result, an organization’s people strategy is critical – and is a top concern of CEOs.
Analytics is the difference between guessing and making fact-based decisions. There are many examples in life where we would never leave decisions to chance: will the bridge support the vehicle’s weight or is the accused innocent?
Yet, although decisions about hiring, developing, and engaging the workforce are critical to organizations today, too many have been left to ‘I think,’ rather than ‘the data tells me.’ This contrasts with finance, sales, marketing, and other functions, where fact-based decisions are the norm.
HR has been slower to adopt an analytics approach than other parts of the business, partly because HR is traditionally a discipline about empathy, not fact; but even more so because people data is a uniquely challenging form of big data.
People data is sensitive – it’s data with feelings. It’s also messy, constantly changing, and housed in many disparate systems. Moreover, connecting decisions about people to how they impact customer satisfaction, same-store sales results, patient readmission rates, or other business KPIs, is far from trivial.
Diversity can be a very powerful thing for businesses. Studies have shown that diverse workforces are more innovative, perform better, and can help expand a company’s pool of prospective customers. Yet setting diversity goals and implementing policies is not enough.
One of the most famous quotes in business management is, if you can’t measure it, you can’t improve it. This is certainly true in the case of ensuring diversity.
Analytics plays a key role here: from answering questions about the state of diversity throughout an organization and across its employee lifecycle, to identifying areas where intentional or unintentional bias may be occurring, to helping companies understand how to effectively address problem areas.
As an example, one client – despite efforts to hire a more diverse workforce – found it was struggling to achieve its goals. With further insights, the company’s HR team discovered the underlying cause was a high turnover of diverse workers on three specific teams. They wouldn’t have been able to uncover this insight without analytics. In the past, the client said they would have looked at their organization’s overall diversity and increased goals for hiring more diverse candidates. The analytics let them focus specifically on where and how help was needed (such as working to retain specific workers at risk of resigning), creating a significant improvement – much more than they otherwise would have achieved.
As mentioned, HR data is inherently messy and difficult to integrate. But it would be a mistake to let that stop you from moving forward with analytics. One of the biggest mistakes an organization can make is thinking they need to clean and warehouse their data before doing analytics.
Companies starting down the path of analytics should aim for accuracy instead of perfection. Good business decisions require accurate data, but the data does not need to be perfect all the time. Think of Finance: financial decisions are changed because of adjusted costs, restated data, or realignments – and those perpetual adjustments are widely accepted.
Not all decisions are equally important. With this in mind, organizations should map data accuracy to the impact of the decisions being made based on that data. There are times when you need to have near-pristine data, such as determining changes to compensation. On the other hand, ‘good enough’ data can already tell you an action is necessary. If your turnover is between 25% to 30%, it doesn’t matter that you don’t know precisely your turnover, you know you need to act.
The best way to clean data is to start with a business question, such as: ‘Are we retaining the right people?’ Then you can bring all your relevant data to the light and work with it while improving it. This approach drives two favorable results: you won’t let good data go unused and the people who are responsible for the data and accountable for the decisions that are based on that data will be given a more compelling reason to get the data in the system, get it right, and get it done.
For organizations that go down the path of trying to build their own analytics systems (using BI toolsets like Tableau or Qlik, or trying to integrate data into their transactional HR systems for more comprehensive reporting), having access to data scientists or other analytics experts is critical to their success.
However, there are other approaches, which can make analytics easier to gain traction with HR practitioners. A platform like Visier can act as a sort of ‘easy button’ for analytics – reducing the skillset needed to leverage analytics and to deliver insights in an intuitive way.
The road ahead
Looking back over the last 10 years, the workforce and workplace have been massively disrupted. Jobs have gone from posts in newspapers, to online job boards, to today where recruiters contact you through your LinkedIn profile. Smartphones, outsourcing, contingent workers, the gig economy, and other social, economic, and technical changes are disrupting how we work. Even larger changes are on the horizon as we start to look at automation and its impact on the workforce. This challenges the very nature of the role of HR.
Over the next five years, we will move to a far more strategic role for human resources. Where data takes the forefront to help the business make decisions. Great HR functions today are strategic and play a vocal role in critical business decisions. They don’t just translate the business strategy, but drive it.
In the future, the CHRO alongside the CEO and CFO will form a triumvirate at the top of the corporation, where the people strategy and decisions related to it are rooted firmly in data-driven insights.