A very interesting book by MIT Media Lab Professor Cesar Hidalgo, titled Why information grows: the evolution of order, from atoms to economies[i] talks about why and how information shapes up our world and its economies, in a lot more subtle ways than we can imagine. People, companies and economies form complex networks that exchange information and use knowledge and know-how to create our business world. It offers a fresh way to look at how information gets disseminated and how it helps economies grow.
Information is inherently embedded into every product, process and customer interaction kept by the company. Products and processes contain “information crystals” that determine how they work and how they are put together, intentionally (through knowledge and know-how) or unintentionally (modified by unintended complexity). Information grows as knowledge and complexity grows and complexity grows as the business grows and matures. Inevitably, as the business evolves, complexity modifies some (or most) of the original purpose or intent of the company and gradually rewires the portfolio, processes and other critical functions of the enterprise. In other words, complexity transforms the foundational information base that constitutes product portfolios and customer interactions. This is the very reason why complexity leads to non-intentional activities, non-value-added tasks, unprofitable products and low-value customers. Complexity rearranges the information crystals in unpredictable and most of the time, in undesirable ways. Data analytics is a very powerful tool to read and interpret the information crystals and uncover clues on how complexity has rewired the business, changing the way the company operates versus its purposeful intent. I find data analytics equivalent to taking an X-ray of you portfolio. 80/20 data analytics (8020dA) is a tool for examining interrelated sets of data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can lead to improvement actions. If nothing else, it unveils the very “center of gravity” of the business, i.e. the vital few customers and products. In almost every analytics exercise you will deal with large sets of raw data (big data), in order to draw useful conclusions. Fortunately, with the ever-increasing power of information-technology resources, 8020dA has become highly practical and desirable. The abundance and the easy access to data, the processing speeds and the analytical tools make it easier to mine for meaningful information nuggets to exhaustion. The most sought after 8020dA is related to data at the intersection of customers and products, as managers search for new insights into buying patterns and marketing strategies. However, it’s important to note that 80/20 data analytics is not exclusive to customers and products; it can be applied to any two sets of interconnected data to understand how activities are wired in order to reach actionable conclusions. Different sets of data are linked throughout the company, such as suppliers and purchasing transactions, people and fixed-costs, procedures and reports, and so on. There is great value in analyzing different data sets for simplification of business processes. Data analytics for customers and products contains the two most valuable interrelated sets of data about the business and is a powerful way to bring your company’s most important activities into focus. It allows you to visualize complexity and extract essential conclusions that are not so intuitive or easy to interpret when you look at individual bits and pieces of the puzzle. The outcome is much more meaningful and revealing than the raw picture we draw in our minds when we start the process. It also gives you the ability to begin adjusting your portfolio right away. 8020dA can be divided in three different types of analyses: correlational, descriptive and inferential. Correlational analysis describes how the raw data is interrelated, using tools such as quartile, customer-products matrix and quadrant analysis. You are trying to answer the question of how much complexity and distortion has entered the business. The sheer shape and size of the data set is very revealing in itself. How dense or sparse is your offering? Do you have patches, uneven distribution or gradients in the data? All of these characteristics can help the experienced observer understand how complexity has rewired your company’s portfolio. The descriptive analysis depicts threshold properties and unique characteristics about the data, using common statistical metrics (mean, median, sparsity factor, etc.) as well as experienced observation of unique characteristics amongst customer and products. Once you characterize the data, then you turn to the inferential portion to derive actionable meaning from the information and optimize the portfolio. In order to do that, we first need to transform raw data into information. In summary, 8020dA is an essential tool to uncover the intentional and the unintentional “crystals of information” in your portfolio. To use a medical analogy, before you decide to take on surgery to rearrange your portfolio, it is advisable that you use all the X-ray and imaging capabilities available to you, to fully understand the situation and avoid surprises when you cut open the patient. [i] Why information grows: the evolution of order, from atoms to economies / Cesar Hidalgo – Published by Basic Books, 2015
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AuthorPedro Ferro Archives
August 2016
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