Louis DiCesari, global head of data, analytics and AI, Levi Strauss & Co

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

• Continue to deliver tangible benefits: Revenues, gross margin, cost savings, and EBIT, while showing uplift;
• Scale the products we’ve developed across the globe while adding new and modular features;
• Automate and digitise everything we do; spend as much time on tooling as analytics;
• Invest time to “de-mystify” AI at all areas of the organisation: everything from up-skilling c30 people to become junior data scientists through an eight-week intensive bootcamp to executive training;
• Promote diversity in everything we do, with the goal to have the most diverse AI team in the industry.

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

Sustainability is our key focus area on using “data for good”, in particular:

• Use of AI-developed lasers in the manufacturing process to reduce chemicals;
• Improved forecast accuracy that leads to lower waste, excess and obsolete product;
• Virtualising physical processes, eg, digital showrooms.

What has been your path to power?

In the words of Winston Churchill, “never let a good crisis go to waste.” I started work just before 9/11; I worked in financial services in New York City during the financial crisis; and now I can add retail in the age of Covid-19 to the list of crises I’ve faced. As I reflect on 2020, I think each of those crises were instrumental on my “path to power.”

In my first job, I was the last person hired into a boutique consulting firm before a 9/11-related hiring freeze. When traditional business started to dry up, I learned to take on anything and everything - working on random projects including forecasting sales of train car windows or hydraulic hoses, in addition to some of the biggest consumer brands. Lesson 1: Squeeze value out of everything.

During the financial crisis, I was working in a marketing analytics role when budgets were slashed. When we had no money to purchase data, conduct surveys or use vendors, we had to be creative in using and generating our own data. This forced me and others to take a better look at the tangled “bowl of spaghetti” that was our own data. Starting to rely on our own data, spaghetti strand by spaghetti strand, was the dawn of the “big data” initiative I would co-create just a few years later. Lesson 2: Necessity breeds invention.

Now I am 15 months into my role at Levi Strauss & Co, which had to close most of its stores during the Covid-19 outbreak just six months into my tenure. Roadmaps became directional, but we didn’t abandon them entirely. We figured out new and different ways to get to our ultimate goals, which were value delivery and capability building for company. Lesson 3: Embrace the art of the pivot - resilience and flexibility are not mutually exclusive.

What is the proudest achievement of your career to date?

My answer is always the same - building high-performing, diverse teams who exceed expectations. But this year added new dimensions to it. During the worst of the Covid-19 trauma, I realised that there was no group of people I’d rather be in a (virtual) room with than the team I have. One, because they’re generally great, kind, empathetic, resilient people who maintained energy and balance despite working around the clock for a period of time. Two, because together as a global team, we cobbled together all kinds of random past experiences - from disaster response modelling to epidemiological training to NLP experience - to produce analysis, tools and product that none of us could have done individually and in record time.

During the pandemic, a few of my former colleagues with solid jobs in far less affected industries reached out about opportunities on my team. At a time when I was feeling so uncertain, their confidence in me and my ability to manage through crisis was a morale boost and badge of honour. (And, yes, they joined Levi’s! A couple people have been crazy enough to work for me three times now.)

Tell us about a career goal or a purpose for your organisation that you are pursuing?

One goal I have is to make as many people data professionals as possible! At a previous company, I trained 100 people throughout the world to be data scientists. I’m starting on this at Levi’s as well, up-skilling 30 analysts – proficient in advanced Excel coding or SQL - to become junior data scientists and join the data and AI community. This has a triple benefit of being a great employee appreciation and retention tool, a great sourcing tool, and further helping to integrate a new capability into a 167-year-old company.

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

At LS&Co, data and analytics are closely aligned to the business with a strong commercial focus. Some of the techniques we use to achieve this are:

• C-level engagement and commitment;
• Close partnership with the strategy function, including dotted-line reporting;
• Joint planning of the roadmap with quarterly reviews with the commercial leaders;
• Collaborative approach to initiatives, requiring commercial scope of “size of the prize,” a business product owner, and implementation and measurement plans plan prior to work starting;
• Shared value delivery targets with the analytics/AI team owning the uplift for the desired metric.

In my view, data and analytics functions best operate as a cross-functional strategic enabler to drive desired outcomes.

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

1. Start with the “why?” - the commercial value.
2. Ruthlessly prioritise - Use a simple 2x2 matrix of value and speed to value. Don’t get too hung up on data quality or governance - start where the data is “good enough.”
3. Embrace agile processes to deliver value quickl, with a clear “before and after” in business and financial language.
4. Communicate the story with a business champion to all levels of the organisation from interns to the C-suite.
5. Rinse and repeat, incrementally improving data, building new features, tackling new problems, and up-skilling others from within the organisation.