Download 2 Detailed Reading of When Information is NOT

Reading McAfee
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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
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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
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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
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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
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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.
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