121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)
00:00
How to Tell When a Product Needs to Be Paused
Mock-ups and prototypes can be used to measure adoption of a product. If users are not using it at that stage then there's nothing to say they're going to use it in production so you need to stop, he says. "You don't pull the black drape off and production say here it is for the first time" - He adds. 'The champion has been created'
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Today I’m chatting with Peter Everill, who is the Head of Data Products for Analytics and ML Designs at the UK grocery brand, Sainsbury’s. Peter is also a founding member of the Data Product Leadership Community. Peter shares insights on why his team spends so much time conducting discovery work with users, and how that leads to higher adoption and in turn, business value. Peter also gives us his in-depth definition of a data product, including the three components of a data product and the four types of data products he’s encountered. He also shares the 8-step product management methodology that his team uses to develop data products that truly deliver value to end users. Pete also shares the #1 resource he would invest in right now to make things better for his team and their work.
Highlights/ Skip to:
- I introduce Peter, who I met through the Data Product Leadership Community (00:37)
- What the data team structure at Sainsbury’s looks like and how Peter wound up working there (01:54)
- Peter shares the 8-step product management methodology that has been developed by his team and where in that process he spends most of his time (04:54)
- How involved the users are in Peter’s process when it comes to developing data products (06:13)
- How Peter was able to ensure that enough time is taken on discovery throughout the design process (10:03)
- Who on Peter’s team is doing the core user research for product development (14:52)
- Peter shares the three things that he feels make data product teams successful (17:09)
- How Peter defines a data product, including the three components of a data product and the four types of data products (18:34)
- Peter and I discuss the importance of spending time in discovery (24:25)
- Peter explains why he measures reach and impact as metrics of success when looking at implementation (26:18)
- How Peter solves for the gap when handing off a product to the end users to implement and adopt (29:20)
- How Peter hires for data product management roles and what he looks for in a candidate (33:31)
- Peter talks about what roles or skills he’d be looking for if he was to add a new person to his team (37:26)
- “I’m a big believer that the majority of analytics in its simplest form is improving business processes and decisions. A big part of our discovery work is that we align to business areas, business divisions, or business processes, and we spend time in that discovery space actually mapping the business process. What is the goal of this process? Ultimately, how does it support the P&L?” — Peter Everill (12:29)
- “There’s three things that are successful for any organization that will make this work and make it stick. The first is defining what you mean by a data product. The second is the role of a data product manager in the organization and really being clear what it is that they do and what they don’t do. … And the third thing is their methodology, from discovery through to delivery. The more work you put upfront defining those and getting everyone trained and clear on that, I think the quicker you’ll get to an organization that’s really clear about what it’s delivering, how it delivers, and who does what.” – Peter Everill (17:31)
- “The important way that data and analytics can help an organization firstly is, understanding how that organization is performing. And essentially, performance is how well processes and decisions within the organization are being executed, and the impact that has on the P&L.” – Peter Everill (20:24)
- “The great majority of organizations don’t allocate that percentage [20-25%] of time to discovery; they are jumping straight into solution. And also, this is where organizations typically then actually just migrate what already exists from, maybe, legacy service into a shiny new cloud platform, which might be good from a defensive data strategy point of view, but doesn’t offer new net value—apart from speed, security and et cetera of the cloud. Ultimately, this is why analytics organizations aren’t generally delivering value to organizations.” – Peter Everill (25:37)
- “The only time that value is delivered, is from a user taking action. So, the two metrics that we really focus on with all four data products [are] reach [and impact].” – Peter Everill (27:44)
- “In terms of benefits realization, that is owned by the business unit. Because ultimately, you’re asking them to take the action. And if they do, it’s their part of the P&L that’s improving because they own the business, they own the performance. So, you really need to get them engaged on the release, and for them to have the superusers, the champions of the product, and be driving voice of the release just as much as the product team.” – Peter Everill (30:30)
- On hiring DPMs: “Are [candidates] showing the aptitude, do they understand what the role is, rather than the experience? I think data and analytics and machine learning product management is a relatively new role. You can’t go on LinkedIn necessarily, and be exhausted with a number of candidates that have got years and years of data and analytics product management.” – Peter Everill (36:40)
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