145 - Data Product Success: Adopting a Customer-Centric Approach With Malcolm Hawker, Head of Data Management at Profisee
Jun 11, 2024
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Malcolm Hawker, Head of Data Management at Profisee, discusses the importance of a customer-centric approach in data products. He emphasizes empathy, understanding customer needs, and developing business skills for data experts. Malcolm also highlights the benefits of a product-oriented approach to ML and analytics, addressing the UX question for adoption and business value, and the concept of data culture.
Prioritizing empathy in UX design for data products drives adoption and business value.
Data experts need to develop business skills to be viewed as equals by customers.
Taking a customer-centric and product-oriented approach enhances data product success.
Deep dives
Transitioning from Data to Customer-Centric Focus in Data Products
Shifting towards a product and customer-centric perspective in the data industry is crucial for driving value and relevance. By moving away from isolated experiments towards solutions driven by customer needs, data scientists can ensure that their work aligns with real-world applications and problems. Adopting a mindset that values customer intentions and targets specific problems enables a more effective approach to data product development.
Navigating the Iterative Nature of Data Science Models in Production
The iterative process of developing and refining machine learning models is a fundamental aspect of data science. While external views may interpret multiple model attempts negatively, understanding and iterating based on customer needs and real-world value can transform these iterations into essential steps towards predictive accuracy. Focus on customer problems rather than experimentation for experimentation's sake can lead to more impactful and practical model deployment.
Data Mesh and Raw Material Orientation in Data Product Development
The concept of data products varies along a spectrum, with some viewing data products as raw materials that enable scalability and flexibility across different applications. While this approach emphasizes discoverability and curation of data elements, shifting towards a product-centric focus can help align data product development with end-user value and specific problem-solving contexts. Balancing raw material utility with customer-centric product design leads to more effective and relevant data product outcomes.
Challenges in Data Adoption and Mindset Shifts Needed
Overcoming barriers to data adoption involves a transformative mindset shift towards a service-based, customer-centric approach. Striving to humanize customers and view them as partners rather than stakeholders enhances understanding of business needs and drives value creation. Embracing a service-oriented mindset fosters collaboration, problem-solving, and empathetic design, essential for bridging the gap between data solutions and actual customer requirements.
Driving Value through Product Management Mindset in Data Science
Encouraging a product management mindset within the data science field promotes a shift towards delivering tangible value and benefits to end users. By focusing on outcomes, benefits creation, and user experience, data scientists can align their work with customer needs and business objectives, enhancing the relevance and impact of data products. Prioritizing user-centric design, problem-solving, and value delivery shapes data science initiatives towards targeted and impactful solutions.
Wait, I’m talking to a head of data management at a tech company? Why!? Well, today I'm joined by Malcolm Hawker to get his perspective around data products and what he’s seeing out in the wild as Head of Data Management at Profisee. Why Malcolm? Malcolm was a former head of product in prior roles, and for several years, I’ve enjoyed Malcolm’s musings on LinkedIn about the value of a product-oriented approach to ML and analytics. We had a chance to meet at CDOIQ in 2023 as well and he went on my “need to do an episode” list!
According to Malcom, empathy is the secret to addressing key UX questions that ensure adoption and business value. He also emphasizes the need for data experts to develop business skills so that they're seen as equals by their customers. During our chat, Malcolm stresses the benefits of a product- and customer-centric approach to data products and what data professionals can learn approaching problem solving with a product orientation.
Highlights/ Skip to:
Malcolm’s definition of a data product (2:10)
Understanding your customers’ needs is the first step toward quantifying the benefits of your data product (6:34)
How product makers can gain access to users to build more successful products (11:36)
Answering the UX question to get past the adoption stage and provide business value (16:03)
Data experts must develop business expertise if they want to be seen as equals by potential customers (20:07)
What people really mean by “data culture" (23:02)
Malcolm’s data product journey and his changing perspective (32:05)
Using empathy to provide a better UX in design and data (39:24)
Avoiding the death of data science by becoming more product-driven (46:23)
Where the majority of data professionals currently land on their view of product management for data products (48:15)
Quotes from Today’s Episode
“My definition of a data product is something that is built by a data and analytics team that solves a specific customer problem that the customer would otherwise be willing to pay for. That’s it.” - Malcolm Hawker (3:42)
“You need to observe how your customer uses data to make better decisions, optimize a business process, or to mitigate business risk. You need to know how your customers operate at a very, very intimate level, arguably, as well as they know how their business processes operate.” - Malcolm Hawker (7:36)
“So, be a problem solver. Be collaborative. Be somebody who is eager to help make your customers’ lives easier. You hear "no" when people think that you’re a burden. You start to hear more “yeses” when people think that you are actually invested in helping make their lives easier.” - Malcolm Hawker (12:42)
“We [data professionals] put data on a pedestal. We develop this mindset that the data matters more—as much or maybe even more than the business processes, and that is not true. We would not exist if it were not for the business. Hard stop.” - Malcolm Hawker (17:07)
“I hate to say it, I think a lot of this data stuff should kind of feel invisible in that way, too. It’s like this invisible ally that you’re not thinking about the dashboard; you just access the information as part of your natural workflow when you need insights on making a decision, or a status check that you’re on track with whatever your goal was. You’re not really going out of mode.” - Brian O’Neill (24:59)
“But you know, data people are basically librarians. We want to put things into classifications that are logical and work forwards and backwards, right? And in the product world, sometimes they just don’t, where you can have something be a product and be a material to a subsequent product.” - Malcolm Hawker (37:57)
“So, the broader point here is just more of a mindset shift. And you know, maybe these things aren’t necessarily a bad thing, but how do we become a little more product- and customer-driven so that we avoid situations where everybody thinks what we’re doing is a time waster?” - Malcolm Hawker (48:00)