A few weeks ago, I had the opportunity to interview Sharon Kratochvil, Global Head of Data and Analytics at Capri Holdings Limited. Capri Holdings is a global luxury fashion group, comprised of iconic brands (Michael Kors, Jimmy Choo, Versace) spanning all of fashion’s luxury categories, including women’s and men’s accessories, footwear and apparel, as well as wearable technology, watches, jewelry, eyewear and a full line of scented products.
Kratochvil has an interesting past. Not only is she a chief data officer (CDO) with a PhD, but she is an econometrician by training (someone who uses statistics and mathematics to model and predict economic outcomes). I was impressed by her expertise when she spoke at a recent CDO Club event, where she championed two important new concepts: there are data product managers and data products. She is truly at the forefront of this trend and provides a valuable role model for CDOs to follow. According to Kratochvil, the rise of data products and product managers happened because traditional business intelligence projects were too siled.
What is a data product?
Kratochvil’s view of data products contrasts with analyst firms. She argues that companies should only have a small number of data products. The focus of these products should be to support a strategic ecosystem, and each data product should include and integrate multiple data sources, with data enhancements (i.e. designed features, algorithms, scores ) and the platforms associated with the data product. A data product as an ecosystem is used across multiple functional areas to drive a set of business outcomes.
The move to data products makes data strategic. At Capri, their companies view data products as strategic data assets. And that led to a natural distinction between master data and data products. Master data is a single source. Whereas data products are always multi-source and designed for a specific business function.
Capri has several data products. Capri’s first data product creates a single customer view. It combines CRM, purchase, SMS, and email data with loyalty information, as well as other customer interaction data. The goal is to integrate customer data sources. They call this data asset a Single View of Customer Data Asset. Kratochvil points out that this is first-party data and that individual instances exist for each brand; for example, Michael Kors.
Their second data product is digital data. It combines website data, mobile app data, visitor identity, visitor segments, third-party platform integrations, and consent management. This product combines data from digital channels, selectively tapping into their digital ecosystem. Compliance and customer experience are at the heart of this data product, as they must both protect and effectively leverage this data.
On the protection side, they must comply with various digital privacy restrictions such as Apple’s App Tracking Consent and Browser Tracking Prevention. Here it is important to keep relationships at the center – merchants, finance, technology platform providers, etc. It’s also important that they produce this data for other teams, so they can use it to power digital offerings in partnership with CX-focused business groups.
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What role do Data Product Managers play?
Data product managers play a key role, says Kratochvil. She thinks this is an emerging role for people with data talent.
In terms of skills, she says data product managers should have business knowledge, technical knowledge and, in her case, domain data knowledge at least equal to the volumes of data the team is working with.
It’s also important for data product managers to understand their company’s business model and how it works. They must understand the problems that need to be solved with data and be familiar with the analyzes and insights to be produced. Data Product Managers must be excellent collaborators and communicators and be comfortable playing the role of gatekeeper and marketer for their business unit.
Become familiar with AI and machine learning
When Kratochvil described the role, I realized that my role as a product manager for HP Software’s data products was very similar. Data product managers must first be responsible for the quality of the data and the methods to address it. They need to know how to examine data and detect problems, understand bad data, and know how to validate data from a source. And then, take responsibility for determining the best way to correct the erroneous data.
Data product managers do not implement data models, but need to understand how machine learning and AI work. That’s what I did at HP Software with our advanced analytical models. Data product managers are also responsible for ensuring that data is fit for purpose and is readily available in a usable form. This allows them to focus on vital tasks, such as data strategy, data roadmap, and enhancements to their data product based on business needs and priorities.
To do all of this, data product managers must have technical skills and an understanding of the source systems alongside the business model of the company. They must also be able to manage suppliers and analyze the effectiveness and efficiency of the data collected. Organizations should have “a dedicated team focused on data governance and products,” says CIO Pedro Martinez Puig. They also need a “transparent portfolio of data initiatives and the level of maturity to expand geographically and pursue cross-company opportunities.”
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Qualities of a Data Product Manager
Kratochvil asserts that successful data product managers have several qualities together. First, they are curious about data. What does it say? What does it mean? Critical thinking is also essential; beyond that, they must understand the domain. This includes big data and big data management. The job requires them to work with terabytes of data. Data product managers need to have a visceral sense of the data and what it means; they need to analyze and make decisions.
So, once the qualities were defined, I asked who could be a data product manager. She suggests that anyone can grow into this kind of role. But she was clear: it’s different from a data scientist role. They are not data scientists; the more technical work is done outside.
Given this, legacy business analysts can become data product managers. They need to understand data science and the role it should play. This includes engineering or feature extraction. Here domain knowledge is essential to extract features (features, properties, attributes) from raw data. The motivation is to use these features to improve the quality of results from a machine learning process.
Former CIO Isaac Sacolick agrees with Kratochvil: “Data has always needed an agile model with product management and delivery leaders, but their skills must include data management, DataOps and data governance + UX/CX, and must dig deep into customer analytics and integration needs.”
Parting Words: Data is at a Crossroads
Without a doubt, data is at a crossroads. It is time for him and his processes to mature. A key part of this is to no longer view data as siled and separated in transaction systems and instead as a mix of data compiled into data products. And no one is better placed to manage this than someone skilled in data and product management.
Myles Suer is the top CIO influencer, according to Leadtail. He is the Director of Solutions Marketing at Alation and also the host of the #CIOChat.