Posts tagged ‘Measure’
Succeeding with workforce analytics is difficult. It requires a mix of skills not found in one person only, and you should not assume, that you can do it on your own. We are all decent at most things but really only good in a few. You should therefore assemble a team, which has a multiple of superheroes each with a superpower of their own.
I described this in a previous post, where I suggested six competencies a superhero analytics team should have:
- Strong data management skills
- Captivating storyteller
- Understand the business
- Ability to visualize your results
- Strong psychological skills
- Excellent statistics and numbers skills
But what happens if just one of those skills are not present? Can’t we manage anyway? My answer is no. If just one of the skills is missing from the team, six outcomes are possible – each with a disastrous outcome – as shown in the figure below:
In essence, if you:
- have no good data, you will not be able to perform analytics. It is as the old saying goes: crap in – crap out. If you do not have good data, it is sometimes better not to do analytics.
- lack of storytelling abilities, the message will nog. As Tom Davenport describes: “Narrative is the way we simplify and make sense of a complex world” and it is the way messages are most effectively conveyed and the best way to get people to change (which is the ultimate goal of analytics).
- have no business acumen will mean that your team will perform excellent analytics on the wrong issues. Workforce Analytics should help decision making on vital must win battles for your organization. Understanding the business is vital to understand what those must win battles are.
- are not able to do visualizations you will bore your audience. Data and numbers are boring (and I am a numbers guy), but data and numbers effectively conveyed through visualization
- lack psychological skills you will misunderstand your findings, be unable to convert your information to knowledge and be subject to important challenges such as bias, cognitive dissonance, imposter syndrome etc.
- have poor numbers and statistics abilities, your analysis will just be plain poor. You can get really far with simple regression-, factor- and t-test analysis skills but at other times, you will need skills in more advanced statistics when the data set become really big or you are looking for more predictive analysis.
Analytics require a lot of skills and abilities – superpowers if you like. The best way to ensure that you have the right ones to deliver on your task is to assemble the best team. An analytics superhero team.
Lets face it – most companies don’t measure ROI on their Talent Management programs. Perhaps this is because they don’t know how to, that they know the result will be scary (very negative) or just because they don’t believe in measuring HR. For whatever reason, they are starting from way behind the starting line.
ROI is a simple tool – and also a tool to be used carefully as it has many pitfalls. However, at is core it has two components; benefits and costs. To improve ROI you need to focus on both. These five suggestions will improve your ROI by looking at how to improve the benefits (4 & 5) and how to lower the costs (1, 2 & 3).
- Improve your development program. You can create value by finding ways to lower the cost of your development program associated with the talent program without affecting the benefit of the program. This can successfully be done using e-learning, coaching and action learning which all have significant lower costs than big classroom-based learning programs. While no program should be based solely on any of the above mentioned, the cost of most programs can be lowered without compromising the benefits using these types of components.
- Shorten your program. Going back in the 60’s it was not unusual to find talent programs which had a duration of three years or even more. This has proved to be wasteful for two reasons; firstly, the uncertainty of forecast of talent needs are too high over such a period. You end up with more talent or competencies you don’t need – and that is a serious waste of money. The second reason is that the added benefits of the final year has proved to be lower than its costs. It is simply not worth it. Best to keep programs at a length of 1½ years instead.
- Create more effective assessment centers (AC’s). AC’s are used to select and develop talent. AC has been under a lot of pressure for two reasons; the validity is often very low and they cost a lot. While both issues are real it is possible to make AC’s valid and cost effective. The difference in ROI between a standard AC and a best practice AC is significant. Make the effort to make a good one.
- Add external candidates to your program. Fact is that you will not have enough talent in-house to meet your need for growth and innovation. Instead of spending good money on people who will not be able to develop at the required speed or achieving the right level of competencies you should acquire them from outside. This is cheaper and earns a better return.
- Have a plan for what happens after the program. The single biggest reason for why talents leave after having been through a talent program is that they are frustrated of not getting moved up in the organization or being offered better projects to work on after the program. This must be addressed up front. Studies suggest that the talent turnover can be halved post the program if proper post-program plans are in place.
The first step is however to measure your return on your Talent Management program. This is not difficult, but requires a solid process based upon best practice. Once this has been done then you can find ways to improve your return. The above five categories will get you a long way.
Job Satisfaction is one of the most researched concepts in Industrial Psychology and in HR in general. And one of the most robust findings about it is that it correlates highly with productivity. To which degree varies quite a lot. A large meta-survey by Judge et al. suggests that the correlation is about 0.3, but I have seen it as high as 0.5. That is quite a lot.
I don’t dispute that ‘Job Satisfaction’ and ‘Productivity’ correlates highly. There is so much evidence to suggest that. I just wonder about the causality. I can think about three ways to explain the correlation:
The more satisfied you, the more productive you are in your job
The more productive you are, the happier you are with your job
A third element drives both e.g. if the match between job and employee is high then this employee will experience both a higher job satisfaction and be more productive.
In the end I believe that all three of the above are true. Which one of them is ‘the most true’, well I don’t know. However it matters a lot for HR practitioners.
If you believe the first explanation is more true then you would work hard on getting your employees to enjoy their work by increase autonomy, skill variety or give more feedback. If you believe the second to be true you would work on things which can increase productivity such as process optimisation. If you believe in the third explanation then you would work on your recruitment processes to optimize job-fit.
Before you measure job satisfaction in your organization, you must decide which of the three explanations you believe in and therefore how you should use the results.
It is tempting to measure retrospectively – but try to stay away from it.
You may have just held a course or completed a successful leadership training program. Or you are finding that your talent management program is being well received. Now you want to show that it added value to the business by measuring retrospectively. Don’t.
I can think of 3 reasons why you should always start your measurement before the program:
1. True evaluation requires a before measurement. Paul Kearns (http://www.paulkearns.co.uk) highlights that pre-training evaluation work – establishing how the activity is going to add value to the organisation and obtain performance measures for each trainee before the training starts – is the most important in any evalution process. I couldn’t agree more.
2. It is too easy to ‘adjust reality’ when doing it retrospectively. To reconstruct an original intend is always difficult – when you are trying to evaluate it is even harder. You must be able accurately to reconstruct the true context, behaviour and results from the time before the activity in order to assess the progress. You may have data going back in time but it is more difficult to reconstruct the original intend. (see this White Paper from Kirkpatric Partners: here)
3. Measuring is also about assessing if the activity should be done or not. Measuring is not an end in itself – it is a mean to create better HR activities. An important benefit of doing all the hard work before the program is to make adjustments so the outcome is strategically focused and create the most shareholder value. If you only do post-activity-measures then you don’t get all those benefits.
In short, measuring and evaluating is great but do it right – true evaluation starts before the activity is launched.