This might be relevant to senior managers or leaders who have a STAFF function at their disposal (besides of course a “LINE army” obviously). Line functions have a clear revenue-linked deliverable and hence are output-oriented. Getting the staff functions to work effectively is a tougher job. Whether they are working too little or too much unfortunately ends up being invariably gauged from the number of hours spent at work.
Hence, converting tasks of staff function into a set of Output KPIs is a must. These output KPIs might most probably be the inputs for the line army. Also, since these belong to staff functions, these KPIs could be tough to quantify and mostly might not be coming out from some elaborate (IT) system. Hence, it requires extra work to define these first, set up a system to extract these on a periodic basis and finally, to review these diligently month-on-month. That these may not be KPIs which your people northwards may be asking for, further robs them of a sense of urgency or immediacy. Hence, pushing yourself to diligently pursue this becomes more important.
A reverse corollary of the above is “how not to use such staff teams ineffectively ?” One way to send them scurrying around unnecessarily is to keep on asking for ad hoc data from them. It takes efforts to get out data from the corporate system and except for a few analysts, this is not the core job for them. Hence, asking for any such ad hoc data comes at the cost of their daily operations. Which means that you have a responsibility to ask for such data in a responsible manner – that is, data that you have a business use for. This act of asking data further harms the working if the culture is to revert to the big boss in the next 5 minutes and hence, leave other important tasks de-prioritised. You need to give them reasonable time for getting such data. A side point here is working with “enough data” rather than “highly accurate data” to shape our business decisions. Unless you are a 100bn $ company and have a large team of analysts, chances are that your business decisions can live with a “better-than-rough” idea of the historical data, and that you do not have to be correct to the second decimal for getting such data. The cost of getting the second decimal might outweigh the risks of going ahead with “enough data”. This cost is not only of the time spent in making the data to be accurate, but can also result in systems, processes (and sometimes, additional people) getting built to ensure that data is super-accurate. You won’t even realize this.
The 3rd decimal should be a C-category item for CXOs. They need to focus on the big picture. Details should be in asking Managers “if they have asked”.