Analysing Global Markets and the Intelligent Company
They say a little knowledge is a dangerous thing, but it’s not half so bad as a lot of ignorance.
Sir Terry Pratchett (1948-2015).
Humorist, satirist, fantasy novel author.
In this chapter, we broadly balance the content between discussions of academic theories and debates with the laundry-list information requirements which we mentioned in the ‘Style Guide’ section of the Preface. As promised there, we will also provide models and frameworks designed to harness this quality information to improve company performance. By way of another contrast that we have frequently discussed up to this point, that between behavioural and economic theories, this chapter leans heavily towards the latter. We also compare the nature of established versus emerging markets alongside a consideration of the strategic challenges associated with each. In the first instance, we need to address some definitional issues which suggest that the universe of global markets (and therefore market analysis) has developed layers of complexity previously unknown and which therefore require clarification.
The Characteristics of Quality Information
As we draw this chapter to a close, it seems appropriate to summarise the characteristics of quality information regardless of the rationale for its collection and processing. The following (final) listing draws on a theme that a colleague – a Professor of Information Systems and Management – uses as the first slide of her first lecture to introduce the subject of data analytics as a core Executive MBA course. She subsequently shows it again as the last slide of the last lecture to review the lessons learned with the class participants throughout the course. It is a highly effective teaching technique that provides structure and meaning to a complex five-day intensive module and demonstrates that there is not much which is ‘new’ in data processing and management.
The constant theme throughout the professor’s course relates to an acronym: GI-GO. This is Garbage In-Garbage Out: it doesn’t matter how sophisticated the algorithms underpinning big data, data warehousing, data mining, artificial intelligence, robotics and machine learning become, if the data which feeds them is garbage, the assumptions, decisions and actions based upon it will be fundamentally flawed, i.e. garbage. And the great reveal in the last minute of the professor’s last lecture? The original source for this 21st Century leading-edge data-analytics course was a textbook first published in 1948 with the provocative title: Data Processing. Yawn!
For the new ‘Big Data’ world, however, here are some old-world truths: Beware GI-GO! Quality information should be:
- Readily available.
- Easily accessible.
A large amount of information has been presented in this chapter, from a critical assessment of a cross-section of theories of finance and economics to a ‘laundry list’ of laundry-lists, each of these with multiple line-items suggesting that engagement in business, let alone global business strategy, is perhaps too risky to contemplate. But we should consider what the American playwright and humourist Neil Simon observed: “If no one ever took risks, Michelangelo would have painted the Sistine floor”.
In essence, the discussion that we have presented in this chapter relates to risk mitigation, not in the sense of risk spreading (as in portfolio management and financial risk diversification) but, rather, the more mundane but powerful sense of out-smarting rival interpretations of the same data-sets. And, of greater importance, asking the right questions of them. As French Enlightenment philosopher Voltaire observed, “Judge a man by the questions he asks rather than by his answers”.
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All content © Colin Edward Egan, 2021