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“The ability to bring data-driven insights into decision making is extremely powerful, all the more so given all the companies that can’t hire enough people who have these capabilities. It’s the way the world is going,” Hammond said in an interview.
As she prepares to teach Data-Driven Operations and Supply Chain Management in the second term, we spoke with her about the competitive advantage of data expertise across industries.
Your current research focuses on supply chain and manufacturing agility. With the rise of e-commerce, what are some key ways retailers are responding to changes in how customers shop?
The reality is that it’s critical for brick-and-mortar retail as well as e-commerce to have a fast and flexible supply chain. One of the reasons is the extraordinary product proliferation that has taken place in recent years. Customer expectations have escalated considerably over the last decade or so as companies have offered more products and refined their selections so that everybody can find exactly what they want. But that puts a lot of emphasis on the systems that support that — data, manufacturing and supply chain management.
In e-commerce, probably the best example is a case I just finished writing on Wayfair, the online furniture retailer. This is a case I plan to teach in the program. Wayfair offers 8 million different stock keeping units, or SKUs, sourced from 10,000 suppliers. In a context like this there’s no way you could forecast which customer wants which product and hold it in inventory.
What you have to do instead is have very good data analytic capabilities that allow you to better understand which customers want which types of things. We’re really at a micro level trying to understand customers: What does this person want, rather than that person, and why? Online you get all sorts of data just from the clicks that people make, so their behavior online is captured and can then be analyzed. Then the question becomes, How do you analyze that data to help make better managerial decisions?
Furniture brands are not strong, so people don’t say, “Oh, I want an Ashley sofa.” They don’t know the brands. That puts completely different requirements on Wayfair for trying to design a user interface that customers can browse, as opposed to search. What I like about this case is it allows people to see that there’s no one-size-fits-all business model for e-commerce. Amazon has extraordinary data analytical skills, but it’s not the only business model out there that can be successful. The key points to me are that data capture, data analysis, and the learning and understanding that come from that can drive how you design your site’s user interface, as well as the supply chain that delivers your products.
What are some particularly competitive skills that you think would set a data analyst apart in the job market right now?
First of all, you need to learn the analytical skills, so get the best education you can. The next piece that sets somebody apart is his or her ability to understand the problems in the context in which they are placed.
Every time you do an analysis, you don’t just say, “Oh, the answer is 17. I’m done.” You need to ask, “What can I learn from the results of this analysis, about the underlying context, about competition, about customers, about suppliers?” Managers should ask things like, “How do the results of this analysis validate or reinforce hypotheses I had before I did the analysis?” It is equally important to ask, “What did I learn that negates or calls into question the assumptions that I made going into the analysis?” Every analysis should be a feedback loop that deepens your learning.
A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing.
Women are still a minority in data fields, filling only 26 percent of the data jobs in the U.S. What impact do you think a stronger representation of women would have on the data analytics field and on the industries it serves?
A colleague of mine at HBS, Robin Ely, has done extensive research on gender and race, and she has told me that if you can make an organization more equal for women, make it a place where women can thrive and advance based on their merit, you tend to raise the culture for everybody. People start evaluating others differently and questioning some of their previous assumptions.
One of the things that’s driven me throughout my career is to try to educate women and help make them both more competent and more confident. I run a couple of Executive Education Programs at HBS for senior women leaders from all over the world — the impact they’re having is unbelievable. A lot of these senior women really had to forge the path themselves. And when they get together and meet the other senior women, they often say, “Somebody understands; these women understand what I’ve been through.”
How do you think data expertise could help women advance as leaders in their careers?
I think that using data analytics is a very effective way to have influence in an organization. If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential. You’ll be respected. What I love about women with mathematical capabilities or quantitative or data analytical capabilities is that that capability is more immediately visible and measurable than some of the softer skills people bring to the table.
Much more data are going to be available; we are only seeing the beginning now. If you don’t use the data, you’re going to fall behind. So, people that have those capabilities as well as an understanding of business contexts are going to be the ones that will add the most value and have the greatest impact.