Getting into development

I wrote recently about understanding what employer’s really mean when they ask for particular skills. It reminded me of a conversation I had with a recruiter from a management consultancy about ‘analytical skills’. While this doesn’t relate much to international development – who don’t tend to ask for this skill it does illustrate the point about needing to get a nuanced understanding of what is meant by particular skills and why the employers is asking for them.

In this instance I asked a consultant from Diamond Management & Technology what was meant by ‘analytical skills’ and how were they used. What he told me was an interesting illustration of my point.

In a recent project Diamond had to help a company establish why they had such a high staff turnover. The company was very profitable and bonuses were being paid but at certain layers in the firm there was dissatisfaction which was leading to high staff turnover

In the final presentation to the client Diamond demonstrated that the root of the dissatisfaction lay in the fact that bonuses were being distributed unfairly. Less on performance and in fact more on how physically close people’s offices were to the offices of those who were making the bonus decisions.
Initial interviews with various staff members across all levels uncovered the initial suspicions or hypothesis. But care was required with such a sensitive issue, if you give certain people a platform to “whinge” then they do just that regardless how well justified their complaint is. Without significant analytical skills this issue could not have been uncovered with a sufficient degree of confidence to confront the IT Director and CEO of the multi-national. The consultancy team had to read and analyse a significant amount of official and quasi official documentation.- “sometimes it might be one email out of the stack that provides the key to understanding the problem”. Lots of financial salary and personal performance evaluations had to be analysed. They segmented the analysis by level as different results per segment would help to explain why the team leaders (managers) were happy but the staff were generally demotivated.  With all the data, they started looking for correlation and trend analysis between data sets. For example, in a meritocracy we would normally expect a strong positive correlation between between bonus and performance, i.e the better you perform the more you are rewarded. Whilst the manager segment demonstrated a positive, but weak, correlation. The gradient dropped by level until reaching the support department – which had a negative correlation. This meant statistically speaking the better you performed in your job the less likely you were to get a good bonus.