Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Reading McAfee 1 Reading McAfee Notes for teaching by Bruce Spear In these notes you will find two parts: 1 What are the five steps and the two lessons to be learned? 2 Detailed Reading of When Information is NOT the Answer 1 What are the five steps and the two lessons to be learned? Who is Sull? = Step #1: Identify the author and basic issue. The writer met him at HBS doctoral program mid 90’s He is currently working to help companies in a turbulent environment. The writer said that he’s thrilled to see a general management scholar to pay attention to technologic issues. He is encouraged to take it serious. Sull’s Position = Step #2: Explain the issue Sull writers that: what type of supporting date do we need to make sense of rapidly changing market. Sull also cities works of the writer himself: Zara and other fast fashion retailers succeeds or fails on the quality of their market data. McAfee’s Argument With Sull = Step #3: Suggent an alternative finding The writer doesn’t agree that for 100 %. The writer claimed that Zara succeeds because the comparatively light use of market date sakes information. The techniques is broadly similar they analyze date and make then a prediction to determine shipment to shop. The Zara Business = Step #4: Explain your finding Zara operates in a turbulent environment. Zara ships clothes to shops after advice of the store manager. Store managers use qualitative tools like walking of the working floor and talking to customers and employees. = Step #5: The Moral of this Story: IT is not good for everything Zara used also IT but it support the decision only the decision is not based on the IT data. The good information is information from the floor and employees and it is not easy to make data out of it. Zara believes that forecasting for fast fashion must be based on relevant knowledge that can’t be digitalized. The 2nd Moral of the Story: Decentralized Decision-Making The decision making at Zara happens low in the organization structure because there is the most knowledge The 3rd Moral of the Story: Respect Different Kinds of IT/Knowledge Reading McAfee 2 The writer ad any questions to Sulls: What knowledge is interesting and what mix of specific knowledge and general knowledge is required? 2 Detailed Reading of When Information is NOT the Answer Andrew McAfee http://andrewmcafee.org/2009/07/information-is-not-the-answer/ A conversation among friends/colleagues Before we criticize, we set up a professional, research-based framework My friend Don Sull and I met in HBS’s doctoral program, which we both slogged through in the mid 1990s. He’s now cranking out mounds of good work at London Business School, and also blogging for the Financial Times. Establishes solidarity with the person, before going on to criticize his argument We make sure we are speaking about published information, over which reasonable people can disagree His current work concentrates on helping companies navigate their increasingly turbulent competitive environments, and his most recent blog posts discuss how IT can and should help with this task. I’ve written about this topic in an Harvard Business Review article last summer and a couple blog posts. Establishes an image of business, “turbulent seas”, leading me to collect an image of a typhoon heading to Japan Establishes the role of IT, to serve business (and not the other way around) I’m thrilled to see a general management scholar of Sull’s caliber pay serious attention to technology issues. He’s encouraging his executive readers to take IT seriously, and offering excellent them advice. Please keep it up, Don. What role data in business? Compare and contrast diffferent business models and different corporate cultures, data flows, and managerial strategies In his recent posts on IT for execution, Sull concentrates on IT’s ability to provide needed information to business decision makers. Role of IT: for execution Establishes audience: top managers, deciding where and when to use data Get the alternatives right, describe them accurately As he writes: Chief Information Officers, Finance Directors, CEOs and outside directors should be asking, and answering, a… fundamental set of questions: What type of supporting data do we need to make sense of a rapidly changing market? What other information is required to support execution? What organizational, behavioral, and cultural changes will we need to capture the benefits of improved information? Define data, develop a working model for how it might be used: establish the basic terms of the argument before we explore differences Reading McAfee 3 Clarifies audience; DIO, FD, DEO’s, directors = the top managers Where I learn what (“fundamentals”) top managers ought to be thinking about Data to understand dynamic, competitive (typhoon) markets: “rapidly changing” What other (non-data?) information do we need? = opens the door for a critical view of data, what are data’s limits? Knowledge management: how to use data is a larger question of organization, corporate cultures, communication He also kindly cites my work on the incredibly agile (and hence wildly successful) Spanish retailer Inditex, parent company of the worldwide clothing chain Zara. Repeats his claim of solidarity with the speaker, before going on to criticize his argument Define the speaker’s position in an accurate, convincing way, but also in a way that sets up a contrasting point-of-view Sull writes: When companies start with the question of what data do we need to execute effectively, they can achieve a great deal without massive investments in IT. Consider Zara. The Spanish retailer surpassed the Gap in 2008 as the world’s largest fashion retailer. Zara leads the world in “fast fashion” a retail category pioneered by European companies including Sweden’s H&M and Britain’s Topshop. These companies track fashion globally, spot emerging trends, and translate them into new products. Zara can move a product from design table to store rack in three weeks. Zara, like other fast fashion retailers, succeeds or fails based on the quality of their market data. Takes care to summarize the speaker’s argument accurately and clearly while introducing the case study, how these businesses work McAfee’s argument, where he differs from Sull McAfee insures that the arguments are professionald and research-based, that you can look them up, among highly qualified researchers Sull stresses that “Zara’s business model demands good information,” which is certainly true. But my work with the company (see this Sloan Management Review article and this case study) revealed something I found fascinating: Zara succeeds in large part because the company makes comparatively light use of market data and sales information, at least as these terms are commonly understood in the retailing industry. Takes issue with the speaker’s argument (and not with his person) Asserts that this different way of looking at it is based on research The argument: data is good for some things, but not all Why this is important: data costs money, and when used improperly leads to bad decision making: we don't want to over-estimate data, but use it when it makes good business sense McAfee’s different view of how data is being used A general model, lots of data flows and decisions to get the right goods to the right customers The decisions about which clothes should to go which stores at what time(s) are probably the most important decisions made by any large apparel retailer. Most chains make them by collecting large amounts of daily sales data from stores, combining it with other hopefully relevant information, then applying a variety of statistical techniques to generate a forecast — a quantitative prediction about what will sell. This forecast is used to push the ‘right’ items — the ones predicted to sell — over time to each store. Reading McAfee 4 We learn how data is being used, a different, more detailed view In this model, data helps with forecasting, because managers here are in the business of prediction: what will sell Each retailer forecasts differently, of course, but I find their techniques broadly similar: they all gather lots of data, analyze it centrally, then use the resulting predictions to determine shipments to stores. In this model, the stores themselves have fairly limited roles: they are expected to record data accurately and send it promptly, then do their best to sell whatever headquarters decides to send them. The data is one thing, but governance – how data is organized by a company – is the larger issue In “this model,” McAfee will offer a different one, individual stores play a limited role: McAfee will advance a more active role for managers, one based on “non-data” observation, analysis and evaluation Taking into account changes in business, increasingly competitive businesses This seems sensible enough, and it also seems logical that as the business world gets more and more turbulent more and more supporting data will be required. This data will need to be acquired, analyzed, shared, and interpreted with ever-greater velocity, requiring ever-bigger computers, ever-faster networks, and ever-more-quantitative decision makers. Justification: the world is more turbulent (the image of a cyclone threatening to overwhelm a business Using an exception that proves/improves the rule: on closer examination, Zara does something very different But Zara, operating in an intensely turbulent environment, does something totally different. The company doesn’t really generate a store-level sales forecast at all. Instead, it relies on its store managers to tell headquarters what they think they could sell immediately at their locations. Headquarters then gets as many of these clothes as possible to the stores as quickly as possible. McAfee says his research shows Zara using data differently – “totally different” – than Sull says: the managers establish priorities, including purchase orders Zara’s example: observation, conversation, intuition and experience can, in some instances, count for more What’s more, the store managers are given very few quantitative or analytical tools to help them make their short-term predictions. They rely largely on intuition and experience, on walking the floor and talking to customers and employees. The managers, he claims, are kept away from statistics and instead directed to gain first-hand experience, including interviews with customers and staff Distinguish between strategy and execution: data is great for execution, including the monitoring of goods, services, money, etc., but for strategy the manager does well to rely on a much broader perspective Information technology is still critically important at Zara. The company uses technology to present store managers with a multimedia digital order form, and to transmit completed forms back to headquarters. IT is used heavily to support execution, in short, but not at all to assist with data-based analysis or decision making about getting the right clothes into stores at the right time. Reading McAfee 5 Summarizing his argument: data in its place, other kinds of information and a different organization of managers, communication, information flows, makes for Zara better business sense Competive advantage might depend on understanding the particular features of a competitive niche, especially as others rely on one kind of information and emerging advantages might depend on other kinds of information Zara is obsessed with making good decisions about what clothes to stock, but has configured itself so that people making these decisions operate in what looks like a ‘data vacuum’ – a lack of aggregated, filtered, and massaged information from throughout the corporation. This is because the good information that Zara’s business model requires is not the kind that’s easy to digitally encode, transmit, aggregate, and analyze. Instead, it’s information that comes from watching, talking, and listening, then using the computer between our ears to pattern match, draw conclusions, and peer just a little bit into the future. Stepping back to generalize on what this example means: valuable information comes in different forms, business models are different, data and its analysis might be understood in different ways Summarize the distinguishing features of Zara’s data problem As I wrote here, Zara believes that the relevant knowledge for fast fashion forecasting isn’t general knowledge (the kind that can be digitized), it’s specific knowledge (the kind that can’t). Three critical business design considerations flow from this belief. First, Zara spends almost no time on store-level sales forecasting and other similar kinds of data analysis. Second, it has moved decision making down very low in the organization, because this is where the relevant knowledge is. And third, it gives these decision makers very little market data or other forms of general knowledge. Each of these points is perhaps best put on a line on their own, highlighted, and examined in detail to understand of data might best be analyzed, evaluated, and put to better use Departing from the traditional model, McAfee claims that “Zara spends almost no time on store-level sales forecasting”. This involves assigning managers a different role and for good reason: “moved decision making down very low in the organization, because this is where the relevant knowledge is. And so data is used differently, the traditional model no longer applies: “it gives these decision makers very little market data or other forms of general knowledge.” So in addition to the IT-related questions Sull lists in the quote at the top of this post, I’d add one more: McAfee’s refinement of data: “relevant knowledge”, and better, make this big as it stands for his key insight: RELEVANT KNOWLEDGE For this decision, what’s the relevant knowledge? What’s the mix of specific knowledge and general knowledge required to make this decision well? If this question is not asked, the danger is that executives will assume that all or most of the relevant knowledge will be general knowledge, and will therefore get to work digitizing, analyzing, and sharing it. Zara’s success shows how beneficial it can be to question this assumption. I want to be clear: I believe that in many if not most situations business decision makers are well-served by the kinds of information served up by computers. But in at least some cases they’re not. Companies that do a good job of figuring out which cases these are will Reading McAfee have an edge over those making the blanket assumption that more turbulent times call for greater reliance on data. Have you seen other circumstances where classic market data is just not that useful? Leave a comment, please, and tell us about them. 6