You have probably heard somebody say something like:

people are our most valuable/most important/greatest asset.

Whenever I hear this, my eyes involuntarily roll around my head, and I have to bite my tongue to prevent it auto-responding:

actions speak louder than words.

Think about it. An asset is as valuable as the money that can be made from it. That is why it is an asset, not a liability. So if people were the business’ greatest asset, the business would be better off with more. Lots more. Businesses would endlessly and relentlessly compete for every joiner, there would be no unemployment and every new recruit would be greeted with an initiation ceremony so splendid that it would rival the coronation of Napoleon Bonaparte. In my experience, new recruits are more often told to sit at the desk of somebody who is on holiday, to keep quiet, to not interfere with anyone else doing their work, and to wait a couple of weeks for their desk phone to be hooked up. No, people are not treated like valuable assets. They are treated like costs to be ruthlessly excised, as pointed out by Tony Poulos in this recent blog.

In an era of data analytics, it should be possible to calculate how much an asset is worth. That is the kind of thing that analytics is good for. “How profitable are customers aged between 25 and 29?”, “what revenues were generated from ADSL lines in the North West of the country?”, “what is the average margin on international calls to Botswana?”. Analytics is supposed to answer questions like those. However, analytics is not used to tell us how much our people are worth, except for the simplest cases like answering how many calls were answered by the contact centre staff. Why not extend analytics further into the realm of human performance? Perhaps it is because we are not sure what our people are for. To know what an asset is worth, you must know how to use it. That way you can tell whether you should make the most of keeping the asset, or whether you should sell it to somebody who can make better use of the asset. This analytical view of staff value is belied by real practice. Staff cuts are usually made using a machete, with an overall target in mind. They are not made using a scalpel, distinguishing the vital organs from the fatty mass. As Tony points out, one common result of downsizing is that some of the vacant positions will soon be refilled because the business realizes it needs them after all. If we knew precisely what each employee was worth, there would be no need for deep and sudden cuts; we would always know exactly who adds value and who is a drain on the business, and hence the net cost-benefit of making any individual redundant. But then, if we knew that, we could calculate the value at c-level at least as well as at any other…

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5 Responses to “The Most Valuable Asset?”

  1. Wendy Wollard says:

    I liked this post. Made me smile and nod my head. Visibility into (human) asset performance could be a scary thing (for the less valuable assets).

  2. Dan Baker says:

    Eric,

    You’ve really hit on something big. Professional sports may be the only industry where the value of the human asset to the enterprise is monitored to the Nth degree. Call it Superstar Assurance.

    The whole basis of justifying new software is productivity gains, i.e. lower people costs. Ah, but that’s not true! Software will allow your employees to be “redeployed”.

    The HR department of Oracle knows something about human asset management. Every time Oracle acquires a new company, it has a rule that the company must shed a certain number of workers across all of Oracle. It’s sounds ruthless, but the alternative is to shed thousands of jobs when a business downturn hits. The 7,000 people at Nokia that Tony Poulos talks about were shed when it was a politically expedient time to do the firings.

    Here’s an answer to this problem. What if a certain percentage of an employee’s pay – say 10% — was tied directly to company performance. Wouldn’t that moderate the ups and downs of its balance sheet. What if that policy was practiced throughout a nation? It would lead to less severe recessions because a safety valve would be there to preserve job stability.

    The underlying problem, of course, is that every worker expects a raise, but no one takes a voluntary cut if his employer is not doing well this year.

    Then there are the self-employed guys like me who love the thrill of roller coaster rides :- )

  3. Eric says:

    @Wendy, thanks for your comment. What Tony Poulos wrote really got me thinking. Redundancy programs involve big numbers (the number of people who get the boot) and even bigger numbers (the amount spent on compensation in the short run, the cost savings in the long run). Telcos keep talking about being intelligent – so where is the data to support decision-making when embarking on these redundancy programs, or to monitor staff performance in general? And if there is no data of that sort, then how can we have intelligent data about the machines the company owns and uses, and about the customers and their behaviour, and about all sorts of things, but not about the staff who use the machines, who interact with customers, and so on? If we don’t know what makes a person effective or ineffective in their job, it raises questions about the usefulness of other kinds of data analysis.

    @Dan, forgive the cultural stereotype, but you Americans certainly have a lot of statistics on the performance of sports stars :) Whilst I’m teasing, it’s also a relevant example of how even ordinary people with no special training can make use of, and even enjoy analysing, statistical data on human performance. How’s that for challenging two default assumptions about statistics and their usefulness for evaluating human performance! Of course, we English have cricket, a sport which meticulously records the detail of every player’s performance. We still have data from matches played in 1744. There is a serious point here – whilst a coach cannot see what 11 players are doing at once, he can read a summary of how far and how fast everyone ran in a game of football, and observe correlations between speed and stamina and overall effectiveness, or detect the early signs that a player’s performance levels are falling due to age. Of course, if such an approach was adopted in the workplace, it would smack of Taylorism and probably would be roundly criticized by all who were subject to it (me included). But that is because the sportsman’s objectives are usually so clear, and the way to achieve it is so well understood. Imagine going into work and having as clear an idea of what you are meant to do and how your performance is measured. It can happen: self-employed guys get very straightforward feedback! But big companies are like enormous sporting teams, except that many of the players either disagree or don’t know what the objective is and how they are meant to contribute towards it.

    As for software and productivity, many years ago I seem to recall that Microsoft was so fed up about the lack of research to show that software had any economic benefit, that they started funding the academic research themselves. At the time, I asked an economist friend to explain what evidence there was that the IT revolution had lead to any increase in global productivity. His argument was pretty straightforward: global productivity had gone up, such-and-such % of the rise was explained, the unexplained % must hence be due to software. How’s that for circular data analysis!

    Then step back and remember JM Keynes, perhaps the most name-checked economist of all time (and seemingly back in fashion since the global financial meltdown). At the height of the great depression he predicted it was “only a temporary period of adjustment” and that standards of living would increase greatly by the start of the 21st century. It was a brave prediction, and he was right. But he also predicted that the rise in the standard of living would result in most people working a 15-hour week, and enjoying far more leisure. Not so – those with shorter hours tend to be low-paid and are insecure about their income, not enjoying their leisure. Meanwhile, the 15-hour week is an unattainable dream for most middle class workers. Quite a few explanations have been offered for why Keynes’ second prediction was so very wrong. Some say we want more because there is more to want. However, I’m more inclined to think it comes down to what suits the employer, not the employee. Why deal with the organizational hassle of two people who each work a 20-hour week, when it is simpler to employ one person to do a 40-hour week? All else being equal, the one person will work out cheaper, as they won’t need training twice, create half as much admin, and won’t need to spend time co-ordinating in the way two part-time workers would. On the other hand, the very good reason to employ two instead of one is that it is less risky. One leaves the company – but their information is not lost. One is run over by a bus – the other can step up to being full-time during the time it takes to recruit a replacement. One wants to go on holiday – let them go and relax whilst the other covers, instead of giving them blackberries so they never stop working.

    I think that the real danger of employing two people where one would do is that you have double the number of people who don’t know what their objective is and how to achieve it, so double the number of people working against each other instead of pulling in the same direction.

  4. Wendy Wollard says:

    I suspect that often decision making when embarking on big redundancy programmes has much less to do with being intelligent and much more to do with the perception of action … most specifically directed at shareholders or potential investors. IMHO too many decisions are made out of a fear of the market response.

  5. ml says:

    The biggest problem is: ” Perhaps it is because we are not sure what our people are for. To know what an asset is worth, you must know how to use it. ”

    Companies hire people and set them afloat in the sea, with no map, compass – sometimes without even sails for the boat. If there are expectations, the resources also have to be there, the strategy and goals have to also be defined.

    Measuring performance – even in the more objective manner of analytics – requires some subjectivity/judgement about the complete package one brings to the table and an analysis about why their potential is not realized.

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