Posts tagged ‘Human Capital Management’
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.
My second take-away from the workforce analytics case-studies and conferences I have heard, attended and experience over the last year is what I call the confusion of cost savings and value creation. While the good news is that we are starting to deliver, my warning would be, that we should be careful not to deliver on the wrong things – or more important; on the least value added things. Let me elaborate.
At most conferences and in most reports by leading consultants, we are being presented with a maturity model, which illustrates activities from the least mature to the most. It goes something like this; first there is some descriptive methods, such as reporting and trend analysis, then maturity increases and the methods goes on to being predictive and prescriptive and finally the maturity goes on to machine learning or something like this. One example of such as maturity model is from IBM shown below (but frankly they all look very similar).
I fully agree with the idea of maturity and that prescriptive analysis is better than descriptive. It is also a good way to illustrate this maturity journey albeit they could be a little more operational in terms of assessing level of maturity and suggested next step depending upon current level. However there is one dimension missing from this picture: the focus of the analysis itself. All good at being mature of the methods but we must also assess maturity on the object of our analysis.
In rough terms: If it the focus is on cost savings elements then the potential shareholder creation will always be limited (it will be the net present value of the cost savings minus the investment). If the focus is directly on creating customer value/business value then the potential shareholder creation will be great.
In fact, I will propose, that there is more value added in doing predictive analysis on a business matters than doing prescriptive analysis on an HR matter.
To be clear, let me come with a few examples. If you are analyzing sickness, employee turnover, recruitment effectiveness or training effectiveness, you are really at the cost savings end of the spectrum. There is no harm (at all) in coming up with evidence based suggestions to reducing employee turnover. Indeed for many companies there are significant money to save in doing that. It is however still cost savings and it won’t get you a seat at the table. So do some of that, but don’t put all your efforts there.
At the other end of the spectrum, you are adding workforce data to customer/profit/sales/other business data. Here the examples are less generic as they are (should be) tailored to each company’s specific strategy and situation. A few I have witnessed/been part of: Finding which service behavior adds the most impact to customer experience/satisfaction, and which training programs are most effective in embedding this behavior. In this example, the workforce data leads straight on to more sales and higher profits. Another example; how does change load (employees’ load of change relative to ability to handle change) impact strategy execution.
These two specific examples had a heavy use of non-workforce data as part of the analysis. In fact, you can test your value maturity on the cost/value axis by testing how much business data you have compared with how much workforce data. If you only work with workforce data, you are probably focusing on cost savings rather than value creation.
Some will sometime argue that “Attracting talent is always business critical and therefore what we do is value creating”. That may be true in some cases but they are missing the point. Indirect value creation is important but less straight forward to prove. In most cases they misunderstand HR processes with business matter.
I therefore suggest that we add a dimension to our maturity models. Perhaps some large consultancy company can show how this may look?
Question: What is the difference between good and bad analytics?
The answer is probably best illustrated like this;
This is bad analytics:
You have received the latest performance management data from all five divisions. You know from experience that the data is questionable – in two of the divisions many of the inputs are the default settings. You also hear that the managers have a somewhat relaxed attitude towards the accuracy of the data (to say the least). Your report show that absenteeism is flat at a reasonable level. You report this with satisfaction.
This is ok analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You contact the relevant HR partner and suggest that they implement the usual retention initiatives.
This is great analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average and 2 %-point higher than sector-adjusted benchmark. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You then decide to interview relevant people in those divisions including leaders and employees as well as the HR partners. You read relevant academic research and case studies on effective measures against voluntary turnover in your particular sector and discuss this with experienced managers and HR partners within your company. You suggest three actions; coaching for the leaders, competency profiling to match job demands and team building. Your data suggest that these three initiatives will reduce the voluntary employee turnover from the current rate to 1 %-point below the benchmark at a ROI of 55%
So what is the difference? Great analytics
- are made with reliable data
- are combined with quantitative data
- use an evidence-based approach
- is predictive
Any other suggestions to differences between bad and great analytics?
ROI is used more and more in HR when justifying or evaluating HR projects. But it has at the same time come under a lot of criticism for being too difficult to use in HR. The first alternative I suggested was another financial ratio – CROCI – which may be better due to its focus on cash and the balance sheet. But while it is a much better ratio than ROI, it is more complicated to use. The second alternative was friction & flow, which is highlighting that HR should create flow and remove friction allowing employees to get on with their day-to-day things. This alternative is very common sense (its strength) but very vague (its disadvantage).
My third alternative is trying to measure HR’s strategic relevance. To understand why, let’s take a step back and ask “what is the purpose of HR?”. What is the ultimate outcome of the services HR provides? I am not thinking about HR’s activities (recruitment, annual appraisals, talent management etc.). So answers like “To hire the best talent” or “To retain our best people” are not good answers. They are ‘means’ not ‘ends’ purposes/goals. I am thinking about HR’s true and ultimate purpose.
For me, there is only one purpose for HR, which is to: “support the vision and strategy of the company”. This can be followed up by “…by hiring, developing, deploying and retraining the best talent and creating an environment for high performance “. In other words, an HR activity has value if brings the company closer to its strategic targets.
So my third alternative to ROI is what I will call the “Strategic Value Index” (made up for the occasion). It goes something like this. Any HR initiative will get a number of points – from 0 to 100 – based on how strategic and impactful it is.
Two examples may illustrate the index:
- Imagine a succession management program. It is designed to identify emergency and long term successors for top 100 managers, identify skills gaps for the long term successors and create individual development programs to fill these gaps. The design is excellent but in reality it does not work. The long term successors are not used with a position becomes available and the development programs are seldom effective. This program will get a Strategic Value Score of, say, 40. It will get a lot of points for a great design, being strategic in its set-up, but very little for execution. In addition, the program has an annual total cost of $1m, which gives a Strategic Value Index score of 40 (40/1.0).
- The second example is an upgrade to the annual assessment days for graduates. Each year, 100 graduates are invited to the annual assessment days with a prospect of a job. 25 young hopefuls are offered a job. The current selection process is not good (close to random). An upgrade will align the company’s strategic competency needs with the exercises and selection criteria of the assessment centre. The Strategic Value Score is, say, 20. The program gets a lot of points for being strategic, its ability to align competency needs with recruitment criteria but does not get many point for impact. The upgrade will cost $100k, which gives a Strategic Value Index score of 200 (20/0.1).
A program should only be approved if it is above the dotted line on the graph to the right. If there are more programs to choose between, the program which is furthest above the line should be approved (best value for money). In the above example, of the two programs HR should choose the upgrade to the assessment centre.
The advantage of this tool is, that it rewards strategic impact, which is really important. Better to do a smaller program which gets the company in the right direction according to its strategy than creating a monster of a program which has no strategic value. A second advantage is that it is relatively easy to measure AND you can use it in case you want to evaluate to investment decisions. ALSO it is an excellent communication tool with you C-suite.
This index does not really exist. I just made it up. But perhaps it should be used?
HR represents something of a paradox. On the one hand, management gurus suggest that HR should be central to the strategic thinking in most organisations. Jack Welsh – former CEO of GE – suggests that the HR executive should be hierarchically second only to the CEO, and at least on a level with the CFO. Jim Collins writes that the best companies understand that they must get the right people on the bus, get the right people in the right seats and only then find out where the bus should drive i.e. the people stuff comes before strategy.
And survey after survey show that people and talent related issues are top priorities and concerns among top executives across the western world. Also, more and more evidence show that the companies with the best people processes and ability to attract the best talent consistently outperform the rest across all industries and countries. It is also near impossible to open a management book or a magazine today without reading that “people is the most important asset” for companies today. This all suggests that HR should be the most important department in any organisation.
On the other hand, most people – including most top executives – often perceive HR to be an administrative function whose purpose is to make sure that people are paid on time, that employment contracts are signed and that relations with unions are good. Stuff that does not add much strategic value. When asked, CEO’s and CFO’s reply that they do not believe that their current HR function is delivering or is even able to deliver the value which is expected of them. HR is still not the strategic partner it wants to be.
So on one hand CEO’s say people stuff is important and on the other they don’t regard HR as important. This is a paradox.
This gap between the theoretical added value of HR and the perception that HR is not adding much value is in large part down to HR practices. Perhaps the best way to describe the current state of HR is to say that it is an area where practice lags behind knowledge quite a lot. There is a lot of evidence that shows that HR can add customer and shareholder value, but it is also fair to say that that practice is different.
I am not sure why. From my chair, I see a lot of improvement; improved practices, new mind-set and focus on value creation. That is why I am surprised that the latest surveys have not improved the picture of HR. Perhaps there is a fixed bias against HR, perhaps it is true that HR really does add no value, perhaps this is changing or perhaps HR is adding a ton of value but it is just not possible to prove it (and therefore other people take the credit). I don’t know. I am just a bit sick of hearing all the time how poor HR is. It is not what I am seeing.
HR analytics promise to give HR better data so they can make better decisions. And who would disagree? If you don’t know your levels of, say, talent turnover, how do you know if you have a problem, which of your current programs work and which don’t etc. In other words, with no or poor data you cannot make good decisions. I totally agree.
The incorrect assumption, which is sometimes made by some in the HR analytics community is, however, that people automatically will (or is even likely to) base their decisions upon good objective data. I would argue this is wrong.
I have previously argued that the concept of cognitive dissonance will actually make people make irrational and poor decisions even faced with good and objective facts. Elsewhere it has been argued that information overload (too much good data) will make people to make emotional or irrational decisions. Studies with placebo effects have also proved that people will act and make decisions based upon (irrational) beliefs rather than facts and data.
To understand why, I think it is important to remind ourselves of the difference between data, information and knowledge (see Davenport and Prusak for more).
1. Data is the fact of the world which can be subject to graphs, tables, statistics etc. They often reside in company’s archives, computers or records. Data do not carry any inherent meaning and more data is not always better as it can be difficult to make sense of raw data.
Examples: We have 5,000 employees, our annual talent turnover is 8.2%, profit per FTE (Full Time Equivalents) is $100,000.
Implication: Human beings cannot base their actions or change behavior on data alone!
2. Information is inferred from data and essentially means that the data has been processed by a human being (or increasing by an intelligent computer) and conclusions are drawn on the basis of the data. Peter Drucker said that information is “data endowed with meaning and relevance”. Some call them “value-added data” – it is sent from sender to receiver indented to change the receiver’s perception of something.
Examples: The talent turnover rate is too high and should be at 4%, our talent turnover is higher than our competitors’ and have been trending upwards
Implication: Evidence from diverse areas such as behavioral finance, social psychology and neuroscience show that people rarely act on the basis of information. Concepts such as cognitive dissonance (and many other) simply override information. More habit-breaking actions are impossible to make on the basis of information whereas more routine and simple actions can be done.
3. Knowledge is inferred from information and is produced by taking information and adding experience, evidence (research, case-studies, theory), contextual information, consequence etc. To produce knowledge requires human beings – it cannot be produced by intelligent software only. It represents our ‘map of the world’.
Examples: Increasing pay levels will not reduce talent turnover in our company but a 2-year talent management program will.
Implication: Knowledge is the only level which will produce ‘real decisions’ and therefore impact behavior in individuals and organizations. HR must take the best possible information and turn it into knowledge before it has the impact it was meant to have. An approach which looks at a mix of experience, values, context and not least (a high degree of) evidence-based research will produce knowledge.
So when we say that HR analytics will enable HR to make better HR decisions we need to understand that it has the potential but it will not necessarily do so. If the data is presented as, well, data then it will have little or no effect. If it is produced as information it may have some but not much impact. If HR analytics can produce real knowledge it will have a profound impact.
The solution is therefore not to remove HR analytics away from HR as has been suggested by some. No doubt that the actual skills of setting up and running an effective analytics department requires skills not present in any HR department today. But removing it too far from HR makes it difficult to use this data and information to produce knowledge. Knowledge is produced by human beings with experience in the subject matter. At least a (very) strong connection between the analytics department and HR must be established.
I freely admit that I believe HR analytics can add a lot of value in any organization. I also strongly suggest that it is only by converting information to knowledge that it can fulfill its promise of being an enabler of better HR decisions.