Experiencing Data w/ Brian T. O’Neill 121 - How Sainsbury’s Head of Data Products for Analytics and ML Designs for User Adoption with Peter Everill
39:40
8-Step Methodology
- Define an end-to-end product management methodology.
- Focus on the discovery phase (understanding user problems) and the final steps (build, release, adoption, impact).
User Involvement
- Users are aware of their problems but not how to solve them with analytics.
- Involve users in validating designs and UAT, but not in the technical build process.
Importance of Discovery
- Don't jump into implementation too early; invest in discovery.
- Discovery saves time and cost by preventing rework later.
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Introduction
00:00 • 2min
Sainsbury's: A Supermarket in the UK
01:37 • 2min
The Role of Chief Data Product Officer at Sainsbury's
03:57 • 1min
The Eight Steps to Analytics Delivery
05:17 • 2min
How to Interact With the End Users of Solutions
06:59 • 2min
The Importance of Early Design in an Agile Process
08:30 • 2min
The Importance of Continuous Education in Data Product Development
10:48 • 2min
The Importance of Discovery in Analytics
13:00 • 3min
The Importance of Core User Research Journey Mapping
15:38 • 2min
How to Be a Successful Data Product Manager
17:55 • 4min
The Role of Data Product Management
22:13 • 2min
The Nuance of Data Analytics in the User Experience
24:23 • 4min
The Problem With Analytics and Data Organizations
28:34 • 2min
The Importance of Reach in Analytics
30:27 • 3min
The Importance of User Engagement in Product Discovery
33:30 • 2min
How to Tell When a Product Needs to Be Paused
35:51 • 2min
How to Hire a Product Manager
37:41 • 3min
The Benefits of Having a Team of 10
40:23 • 2min
Designing for Analytics: A Practical Approach to Product Management
42:04 • 2min
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)