All companies are good, and some are excellent. How to grasp the difference?
Companies active in particular sector are basically similar. Similar products, machines, technology, and people are doing similar things. Why some are falling, some simply exist stagnating, and some excel and are climbing – for example – to the top of World Class Manufacturing?
Excellent companies think differently. It is cultural difference, and many studies discovered the relationship between culture of the enterprise and its business results. Some 10 years ago I also made a study of this relationship, and discovered evolution of the values’ perception and hierarchy during transformation from centrally planned economy to the market economy. The essay on the “cultural revolution in business”, published in the press and in a book, won some renown.
The discourse of the company reflects its culture. There are, for example, some differences between the discourse of excellent and good companies:
- in the average company people think and speak in the terms of results, whereas in excellent companies people think more about processes (the flow, the quality, control etc.); in between are the companies in which the people of Board are focused on results only and the other managers are concerned with processes, and this discrepancy sometimes induce conflicts;
- in the average company, during break, people talk about sport, children, politics, last weekend etc, whereas in excellent companies lunchtime is full of the business chats – the sign that they are interested in business;
- the average company is quiet and politically correct and the wrangles are rare and full of accusation, whereas in excellent companies wrangles are almost permanent and focused on the problems not people – they are not wars;
- people in the average company take problems personally, whereas in excellent company problems are treated simply as tasks and expressed in the terms of figures and facts (objectivism);
- excellent company has the common language dramatically facilitating communication; in the companies with KAIZEN culture this language is replete with the terms of organisational techniques (“Pareto language”);
The discourse differ also in enterprising or innovating and average companies. For example, the attitudes to uncertainty: entrepreneurs are often “taking risk”, innovating companies are “looking for opportunities”, and average companies rather “protect themselves” against threat.
The difference means the opportunity to measure and analyse. The question is, of course, whether the analytics of the discourse of excellence could be useful and make sense. For example: could we monitor the entrepreneur climate more cheaply or more accurate or faster using discourse analytics instead of conventional methods? Or could we rate the companies more efficiently using analysis of the “discourse of excellence” instead of (or in addition to) traditional rating? Could we measure competitive capacity of particular company and identify the space for improvement using the analysis of its discourse? Could we monitor its culture and alarm if something goes wrong? Simply speaking – could we develop intangible capital analytics?
Above questions are inspired by the short discussion of Nicholas.Carbis’ post on AnalyticBridge. He develops “human capital analytics” (as he says). I think that broader idea of intellectual (or intangible) capital analytics is worth considering.
As far as I know there is no empirical evidence up to now of the relationship between Intellectual Capital (IC) and company productivity. Perhaps IC analytics could help?
There are so many questions about practical implementation of IC analytics. First: Data source. Second: Sometimes discurse reflects company’s culture indirectly, esp. when official language prevails and is used to hide rather than to reveal the issues…
Thrilling question whether “All Predictive Models Are Wrong?” has already second page of proposed answers in the LinkedIn, and probably will be continued for a long time. However, the other question seems to be equally important: “What to do if the predictions are not sufficiently accurate?”
Standard answer is seductive and well known in the security management – it is necessary to built a strategy to cope with uncertainty and following risk. For example, contemporary military strategies for such cases fulfil the goal “to preserve the ability to continue operations”. Narrow-minded business often develops strategy “to minimize loss”, and open-minded business develops organisation able “to benefit most of opportunities and optimize the measures against threat”.
Discussed is the case of hurricanes. No one can predict the loss due to the hurricanes with satisfactory accuracy. Uncertainty is costly. If the risk is overestimated, people and companies bear excessive cost to protect or insure themselves. If the risk is underestimated, the insurers bankrupt…
It is not possible to predict loss of disasters accurately. It does not mean, however, that data analytics has not to do much in this field. Probably instead of tilt with windmills better is to analyse strategies of the response to uncertainty, and to build the models of optimum strategies. Moreover, the variance of strategies can explain a part of the variance of total loss, and contribute to the accuracy of total loss prediction.
The idea of modelling the strategies of reinsurance is not new. It requires some knowledge and models of the behaviour of open dynamic systems. It seems to be reasonable to build a general model of the reinsurance strategy securing insurers against bankruptcy – the strategy “to preserve the ability to continue operations”.
For many years in hundreds of articles, lectures and discussions I urged managers and IT professionals to apply best practices, humane science and economy to their work. As consultant, I demonstrated for many times the practical usefulness of such attitude. Today, after many achievements in the past, I see that bringing knowledge and practice together is never ending necessity. Post-industrial society and knowledge-based economy is still to be built on the always fresh layers of ignorance-based economy. Therefore I open this blog with two purposes in mind.
The first purpose of this blog is to bring humane sciences and technology together. IT and computer science could benefit much, and develop much more efficiently if closely collaborating with the experts in the fields they enter. For example, why not to learn process management from industrial engineers, when developing Business Process Management applications? Why not to develop ontologies together with philosophers who are experts in ontology? Why to develop semantic technologies without the experts of linguistics?
As generalist, I managed to base my inventions in semantic technologies on strong philosophical grounds, and applied experiences of the dicourse analysis. The results are promising, and I will use them in this blog as examples of the benefits of such integral approach.
The second purpose of this blog is to promote knowledge and expertise as the resources of economic and human growth. This is quite difficult task, as a variety of hype and multitude of sham pretend in the field. Probably more difficult is to make knowledge and expertise comprehensible without losing their profundity and pertinence.
I invite everybody interested in these two tasks to join this blog.