Understanding your workforce through data is far more than the simple approach of "scientific management" as famously prescribed by Frederick Taylor at the start of the last century.
Many now appreciate that people are not machines, and an unsophisticated 'one size fits all' approach would not recognise that your employees are full of complex and differing motivations and circumstances.
However, this basic concept of measuring and reacting to data can still be seen as the first seedling that grounds many subsequent management theories and practices. Many years later, and often attributed to Peter Drucker, the phrase “What gets measured gets managed” has been widely propagated.
Others have expanded on this quote, and this longer version profoundly changes the context of what is being said, “What gets measured gets managed – even if it’s pointless to measure and manage it, and even if it harms the purpose of the organisation to do so”
This now emphasises that policy or actioned consequence of data should often not simply lead to an automatic reaction. It may be of great importance and have many implications to delve deeper and understand the reasons behind the data before reacting. The data itself may be a headline but we need to read the full article.
There have been many historical instances where simply designing policy on data alone has led to inadvertently incentivised unwanted actions - this has become known as
the cobra effect, such called from its origins in an anecdote about the British rule in India. The Government was concerned about the number of venomous cobra snakes seen in Delhi, so concerned that it offered a reward for every dead cobra presented to it. This worked well, with large numbers being submitted and reward distributed – so far, so good. As time went by, some more unscrupulous entrepreneurial businessmen decided that the best way to cash in on this deal was to breed snakes! This was done on an industrial scale and for a while everyone was happy with the data showing that many snakes were being destroyed and people becoming financially better off. If only they had dug deeper and understood what was happening! When eventually the scam came to light the scheme was abruptly stopped, the entrepreneurs moved on and released their now worthless cobra into the environment. This now left the British Government out of pocket and with a bigger venomous snake problem to deal with.
A further example, with the aim of environmental improvement, was seen in a major South American city. In an attempt at traffic reduction, vehicles containing a number plate with an even number were allowed on the streets on certain days and those with an odd number on the plate on others. The consequence of this was over time, there was some reduction in traffic but on further investigation it was found that it incentivised many people to buy more than one, single expensive “green” modern car and had in fact led to families buying two, cheaper, older, more polluting cars – one with a number plate containing an odd number and one containing an even number!
These examples show the danger of unexpected consequences of seemingly accurate data.
Moving on, there are many other examples where the data was inaccurate or misinterpreted which led to unfortunate results. A famous example was seen by NASA in 1999 when the Mars Climate Orbiter crashed due to errors in the interpretation of data when performing a manoeuvre above the red planet. Unfortunately, parts of the software had been supplied by a US company and other parts from Europe. One had calculations based on pound-force seconds and the other in the SI unit, newton- seconds. The result was estimated as a costly $327 million accident.
Even more recently, in October 2020 Public Health England (PHE) reported that almost 16,000 Covid-19 cases went unreported for a specific week. Who knows what consequences for action this had! The reason behind this misreporting was inaccurate data from their seemingly antiquated reporting methods. PHE used an automated process to transfer positive lab results via CSV files and into Excel templates. However, when the number of cases overreached the 16,384 columns in the spreadsheet the 15,841 results at the end of the list were simply lost. The data issue had not led to those being tested losing their results but had meant that close contacts were not immediately followed up by Test and Trace. PHE put rapid mitigation in place to split larger files to prevent this in future and began a review to ensure similar data based issues would be prevented in the future.
All of the issues and poor implications above do not negate the importance and requirement of data but do emphasise the requirement for it to be good, accurate and appropriate data. There has never been more data available in the world. It has been estimated that 90% of the worlds’ data was generated in the last two years and this generation is only getting faster so this accuracy and interpretation driving subsequent actions becomes ever more important!
A dashboard showing base data can be useful as it highlights an issue, but actions and understanding need to be taken before managing the issue and seeing its consequence. This starts with examining the data and how it is made up. A simple example in workforce management would be seen by purely looking at overtime levels without delving deeper at cost, productivity, quality and even in many cases, examining down to the individual levels of working hours – are levels low but for a certain few people excessively high? Is this about lack of required skills, poor management or something else?
Absence levels may seem alright as an average but again delving deeper you may see specific shifts or days or again individuals that make up the lions-share of time off. This ability to dig down means that the reasons behind data can be better understood, and consequently dealt with in an appropriate way. A blanket ban on additional hours working could be implementing a “cobra effect” situation if you had not understood that productivity was still good, and orders were up!
Integration with Microsoft Power BI® presents huge amounts of time and attendance data, captured from every level of an organisation, in simple graphical form
Create dashboards showing the top-level insights you need with just one click, allow these dashboards to be analysed simply by driving down into lower levels
Transform your data into easy-to-understand business facts
Dashboards (with the ability to dig down into the data) created by the leadership team can be shared across the entire organisation
Combine multiple data sources into one true database
No matter where your data flows from (including third party ERP and HR systems), we can take everything into account
Automatically “roll up” data into daily, weekly, monthly, quarterly or annual views, with year-on-year trending
‘Slice-and-dice’ data by many useful attributes without needing knowledge of complex data structure
Bringing all of this accurate and relevant data will help you to manage and make better decisions that boost efficiency and keep you in control of workforce costs.
To find out more about Crown's Workforce Management system contact one of our specialists today!
