People often debate the greatest impediments to building an people analytics function. Typical answers include lack of technical skills, lack of data, lack of senior management support, an organisational culture that doesn’t view employee behaviours as quantifiable, lack of funding, and so on.
While all of these contain some truth in my experience, the list lacks the number one barrier, so well expressed by Chris as his key takeaway after this week’s CIPD analytics workshop: “The obvious yet easily overlooked concept of finding the business problem first then working back from there”
So what do we mean by first finding a business problem and starting from there? The first thing most analysts learn is that analysing without first proposing a model is usually a waste of time because without a model, you may end up finding relationships that exist by chance but are in fact false. What is a model? Here’s an example:
Programme £ -> Competencies (how) -> Performance (what) -> Organisational objectives
This model says that:
1. You spend money on a people programme e.g. development, compensation, recruitment, employee relations, and so on
2. The only reason you spend this money is to increase the available pool of organisational competencies (otherwise known as human capital i.e. human capital is about competencies; human resources is about headcounts)
3. The only reason you want to increase the organisational competencies is to improve employee performance
4. The only reason you want to increase employee performance is to increase the achievement of organisational objectives
In other words, the model says we only spend money on people programmes to increase achievement of organisational objectives. (Side implication: So any programme that doesn’t result in the competencies which contribute to organisational objectives is not helping the organisation to achieve its objectives).
Now this model, like any model, has limitations e.g.
1. Some people argue we spend money on people programmes for reasons other than achieving organisational objectives e.g. we spend it as part of a corporate social responsibility to our employees (but try explaining that one to investors in public companies)
2. This model includes factors like “employee engagement” and “manager behaviour” as “competencies” (but this is just semantics – you come up with a better name than “competencies” for that box: some people refer to it as the “how” we get things done)
3. It’s difficult to prove directional causality e.g. how do we know that performance doesn’t “cause” a “competency” like engagement?
4. How do you measure competencies? Presumably via your competency framework; if you don’t have one, then use a Repertory Grid to develop one
5. How do you measure performance? Chances are you have a performance management framework so you could use that as a starter. If you don’t trust it, then this model gives you a good reason to fix it.
6. How do you measure achievement of organisational objectives for employees without P&L/budget responsibility? Answer: you’ll get tons of value by focusing on competencies and performance management for the first year or three; you can always come back to this afterwards.
But despite limitations like these, the model has two major benefits:
1. It translates directly from model to Excel spreadsheet (one row per employee, one column for each block in the model)
2. More importantly, it forces people to think about how every penny of HR money will result in the achievement of some organisational objective. As Doug Bailey, Unilever HRD said at a Valuing Your Talent event last month, “Whenever someone requests funding for an HR programme, I ask them how this will help us sell another box of washing powder”.
So how does all this link to Chris’s original comment about finding the business problem first? It means that unless an analytics initiative is helping to fix some unachieved organisational objective, it is worthless. Thus the rule is:
The only way to start any analytics programme is by first finding an important organisational objective that is not being achieved (preferably one that most of the board agree is a problem – that way you’re more likely to get a budget for analytics)
So the bottom line is that if you’re achieving all your organisational objectives, you don’t need analytics. For the rest of us, chances are there’ll be some organisational objective not being achieved.
Let me hasten to add that organisational objectives are people-related objectives that appear in your corporate/organisational business strategy like “reduce the number of people in the population with disease X” or “achieve revenues of £X”. But more importantly, organisational objectives are not HR objectives like “employee engagement” or “employee churn” or “absence rates”. In the above model, those would count only as Competency or Performance measures. (In other words, don’t confuse organisational objectives with HR objectives).
So after reading this, you might say, this is all so obvious – I mean how else could you start an analytics project? I’d say that 90% of the calls I get start with (and I’m sure other analytics consultants agree with me): “Our HR function has collected a lot of data over the past few years. Can you please help us do something with it?”. I say “Like what?” The reply is usually: “Help us to make HR look good”. I suggest that the best way to make HR look good is by enabling (helping) the business to achieve its organisational objectives. And the only way it will do that is by starting with the business problem and not with the data.
In fact starting with the data is like saying “We’ve got all these motor spares lying around; can you help us build a car with them?”. What are the chances you’ll have all the right spares to build a car? If your “organisational objective” or problem is to build a car, you’d surely first determine what parts you need. Then by all means do an audit of what parts you have lying around. Chances are you’ll find you only have 5% of the parts you need to achieve your objective; for the rest, you’ll have to go out and obtain the other 95% of parts you need. It’s exactly the same with data for analytics: for any given problem with achieving an organisational objective, you probably only have 5% of the data you need to solve it.
So there you have it: that’s why Chris felt that his biggest insight from the workshop was: “The obvious yet easily overlooked concept of finding the business problem first then working back from there”. And not gaining that insight is really the biggest impediment to creating an effective people analytics function.