When Information is NOT the Answer

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.

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.

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.

In his recent posts on IT for execution, Sull concentrates on IT’s ability to provide needed information to business decision makers. 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?

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. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

So in addition to the IT-related questions Sull lists in the quote at the top of this post, I’d add one more:

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 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.