The actions you take in the first 30 to 100 days can set the course for organizational analytics for the next three to five years.
That’s the guidance from Dr Nigel Guenole, research at the IBM Smarter Workforce Institute and a lecturer at Goldsmiths, University of London.
While Guenole observes that there’s “no one perfect model”, for implementing HR analytics, there are some general principles and stages companies should follow. These are set out in IBM’s recent report Starting the workforce analytics journey: the first 100 days, co-authored by Guenole.
The report focuses on the organizational development and change management required for HR to establish an enduring workforce analytics program and addresses the issues in phases.
The first phase is all about establishing a clear vision of what you want to achieve. Guenole says:
It’s not about opening up a statistics program, if you want to do this in an enduring, systematic way it’s about setting a vision for analytics and your organization.
This involves more than setting a vision for HR alone; it’s about tuning into the business challenges and the part HR can play in helping the business meet those challenges.
While it’s key to establish a vision at the outset, that doesn’t mean it’s carved in stone from day one; it should be an iterative process. HR needs to share it with the HR business partners and business line managers and tweak that original idea until everyone is happy.
It’s also important in this phase to define the governance model and establish what legislation they should be aware of, taking into account country differences in legislation, for example.
During those early weeks, HR needs to find out what line of business executives really think about HR, as this will impact how they should approach rolling out analytics. Guenole explains:
If it is viewed as an operational function and you want to do something strategic with analytics, then perhaps you will need to engage with the line of business directly, because they might not be used to hearing from HR in this capacity.
If, however, it turns out that HR is seen as a strategic partner, then it might be better to make use of the relationships the HR business partners already have in the organization. HR will have to “flex the model” according to their standing.
Those interviews with the business will also help identify some of the challenges the business is facing and influence where HR chooses to target its analytics. It’s very important to have a “quick win” project that will instil confidence in the business and act as a proof of concept.
Finding suitable business challenges where HR can help probably won’t be a problem; the challenge will be to prioritize which order to tackle them. To do this, HR must weigh up the ease of implementation against the level of impact.
The golden ticket is to find a project that is both easy to implement and has significant long or medium term impact. But Guenole concedes that, in the first 100 days, this might be quite difficult to achieve:
So what you need to do is focus on something that is already being done – in compensation, say, or recruitment – so the process is already being done, but with analytics you can do it faster.
Such projects don’t involve largescale changes in the way people work, but the impact of using analytics can be easily felt and have what Guenole refers to as a “direct cost impact” – clear results and realized quickly.
The second phase, covering the first 60 days, is about being smart with data quality and smart with technology, such as using the cloud.
According to Kieran Colville, a consultant for IBM Smarter Workforce, an important element of this is defining the approach to data. While it’s right for HR people to be concerned about data quality, there’s a danger they can take the obsession too far, warns Colville:
The nirvana of data is to collect all the data sources we need in HR and you need to get everything clean, trustworthy and accurate. Actually, for us, that is a little bit of an unrealistic aim.
Instead, HR should go back to that priority list and hone in on specific areas to spring clean the data. By examining the business’s key KPIs and the key people issues associated with those KPIs, HR can get the data right for those job families to impact them.
While the data has to be trustworthy, Colville says that HR is often guilty of striving for unrealistic levels of perfection. As Colville points out: You don’t have marketing going to the CEO and saying we’ve done a 10-year study of our customer behavior,” and neither should HR. The data needs to be trustworthy but not perfect.
Hand in hand with data trustworthiness there needs to be discipline about how the data is inputted and how the processes are defined.
Understanding the technology options is another key element of this phase. The old-style data infrastructure of ERP systems and data warehouses are off the menu. Cloud enables companies to speed things up massively and have simplified data management and storage.
The other key benefit cloud brings to HR, notes Colville, is that:
You don’t have to write a big business case built around CapEx and have the CFO shoot you down and give you 10 reasons why can’t do that right now.
But, according to Colville, neither pristine data nor analytics acumen is as important as the ability to communicate what workforce analytics can do:
If you can’t tell a good story, if you can’t visualize it so that the line of business executive gets it intuitively and emotionally, this thing isn’t going to fly no matter how good the rational argument is.
Phase three is about growing analytics capabilities. A key element of that is identifying the roles and skills you will need longer term as you build the analytics department.
On the one hand you need mathematicians and people who are comfortable in the land of data science and statistics. But to complement the technologists, Colville says psychologists able to understand and communicate the “so what” factor about what they are doing are also an asset to a team.
Different skills are needed again when it comes to head of the analytics team. The leader needs to have the business and HR acumen and an ability to focus on the business outcomes. Technical skills are not required.
Phase three is also the time to complete the business plan, looking at the metrics the CEO cares about and how HR can make a difference to them with workforce analytics. To deliver results, the report suggests running the workforce analytics team as if it were a small consulting business, taking a project-based approach, with start and end dates, deliverables, resource allocations and client agreements.
With this business focus in place, the workforce analytics team will be well placed to identify projects that go beyond traditional HR boundaries and have an impact on the wider business.
With the firm foundations put in place during those first all-important few months, HR will be well placed to make a big contribution. As to what kind of contribution that will be long-term, well, the sky’s the limit. As Guenole observes:
There was a consensus among the people we interviewed that we were only scratching the surface of the possibilities of what HR can do with analytics.