In this article I share the experience of my first three months at Stuart as a Data Product Manager for our internal data platform; what it means to sit at this powerful intersection between data and product; and why it's critical for a modern data platform team like ours to "think product" in truly delivering business value. I'll draw on the challenges faced when looking at a data platform through a product lens, redefining it from simply a "tech stack" to what it really is: a product that delivers value to hundreds of business users day in day out. My hope is that this will serve as a guide to data platform teams at any stage of becoming more product-led while contributing to the emerging space of data product more generally.

The uncomfortable questions

Over recent years most companies have adopted a data platform as a way to manage how data is ingested, stored, transformed and accessed at scale. The end goal is typically to support the business in making fast and reliable data-driven decisions, and here at Stuart we're no different.

In the face of exploding volume and complexity of data which needs no further explanation, the focus of data platform teams have understandably been concentrated on stability and reliability at scale. For example, ensuring the smooth running of business-critical pipelines, minimising downtime or optimising query efficiency.

While companies large and small have made considerable gains in building a scalable and sustainable architecture, we're left with the uncomfortable questions: is what we're doing truly providing value? Do we really know who our users are and understand their needs? If so, can they generate insights in a fast and reliable way? As long as users don't complain and pipelines don't fail, does that mean all is well? For all our investment in data, are we seeing the return? These questions that touch on the concept of value are particularly difficult for an internal platform that doesn't sell to or make money from our users.

Moreover, the added complexity of quantifying the value that data brings forces us to consider alternative definitions of value:

  • Top-down: How does the data platform align with and contribute to company goals?
  • Bottom-up: Would users choose to use the data platform, and is it easy to use?

While the importance of the former cannot be underestimated and should play a critical part in goal setting and prioritisation, this is often a question of modifying process. Meanwhile, the latter is a more complex topic, and especially so for a data platform intended for business (i.e. human) decision-making. To better understand users requires more than a change in process —we need a change of mindset.

When the data platform is viewed purely from a technical lens, we put ourselves at risk of forgetting our users and their needs, building solutions before we've understood the problem and further increasing the data/value gap.

These questions are particularly important to ask now, when the growing discussion around data mesh suggests we're on the verge of a radical shift in how business interacts with and governs data. This only further increases the need to proactively and continuously understand our users and their evolving needs.

The data platform: More than a tech stack

Much in the same way that experimentation platforms, algorithms, and even the data itself are being increasingly considered products, we turn our attention to the data platform. For simplicity, consider the data platform to refer to all the tech and tools that collectively serve the business to make data-driven decisions.

If the previous section wasn't enough to convince us, there is one concept that helps sum up the need for product thinking for a data platform more than anything:

Accessing and working with data is a user experience.

In the same way we use applications for almost everything in our personal lives from measuring our heart rates to finding a new house, the experience of accessing data to make decisions should be no different. But the concept of UX is rarely heard among data teams. This is hardly surprising if the data platform is viewed entirely as a technical problem and resourced accordingly; but perhaps added to this is the idea that as user-facing data tools are frequently bought and often come with a UI, that these UIs automatically "take care" of our user needs, i.e our job is done.

But a good UI cannot replace the need for a good UX. To think that in most modern organisations, hundreds or even thousands of users are interacting with data on a daily basis, the idea of not continuously investing in discovering users' needs, pain points and desires, of not using this user context to shape our roadmap for success, in other words, not thinking of our data platform as a product should concern anyone investing in it in the first place.

Here at Stuart the Data team is fortunate to sit alongside a strong Product team whose philosophy is centred on continuous discovery: identifying opportunities in the form of user needs, pain points and desires and using this to guide our vision, strategy and goals. This means that as well as building products that make sense both from a technical standpoint and aligned with company goals, we're also strongly led by our users. This approach means we're more likely to deliver a data platform that users love, and less likely to jump into solutions that ultimately only we in the platform teams love.

Data Platform as a Product: First Principles

Until now we've mostly discussed the why. It's now time to move from theory to reality and put this into practice: the how. The complex make-up of the data platform doesn't make it easy to define as a product by any means. Here we present our "first principles" approach to this, which focused on three broad areas:

  • First, at the end of almost all of our data efforts is a user experience. But users are not one and the same; they are diverse and evolving, fast. For this reason we should start by considering our different user personas ("who are our users?").
  • Second, we also need to consider the motives and reasons behind needing a data platform in the first place. We should at least give consideration to user intents ("what do our users want to do with data?"), broadly similar to use cases but at a much higher level.
  • Finally, for the multifaceted data platform we need to make life easy for both our users and ourselves by breaking it down into easily identifiable product components ("how do we present our data platform to our users?"). These components are accessed by users through interfaces, typically representing third party or internal tools.

Together with other data leads and interviews with key business stakeholders we investigated each of these areas one by one. To ensure it didn't become a one-off exercise we made them as visible as possible internally, e.g as part of onboarding a new Data or BI Engineer. Doing this also incentivises us to keep them relevant and up to date.

User Personas

If we want to shift to a more user-centric approach, understanding who our different users are feels like a logical starting point.

Approach

  • If our goal is to provide constant reminders to our data platform teams that there's a user at the end of our efforts, we need to reflect reality as much as possible. We used names and photos from real users.
  • We avoided creating personas directly along job titles or business departments, as it often obfuscates the nuances between teams and is less flexible to change. e.g. a data analyst in one team could have a very different profile to a data analyst in another.
  • We tried to at least get a rough number of the size of each persona through looking at usage logs etc. This helps give more meaning and context to a persona.

Outcome

User personas schema
Our data platform user personas. Note: user photos were hidden from this view.

User Intents

Why would anyone make use of a data platform in the first place? What are the key actions our combined user personas should expect to be able to perform autonomously? These were the questions we asked during this step.

Approach

  • We placed the word autonomously front and centre of this discussion. As we scale we must strive to reduce the dependency on the data platform teams so users can act fast, albeit in an aligned and secure manner. 
  • With this in mind, we took an aspirational approach in considering both what the user can and cannot do with data today by themselves.
  • We avoided listing all the specific use cases at this early stage due to the sheer volume of these, which could easily run into the several hundreds. Instead, we focused on summarising high-level intents as described by a single action (verb). As we mature and evolve we can add (and remove) nodes to this "tree" of user intents, possibly then going down to specific use cases.
  • As soon as broad categories of user needs emerged we began to group these. For example, the "need right now" vs. "need for future". The first represents ad-hoc needs for data answers immediately; while the latter represents intents to build or automate in order to avoid repetition in future.

Outcome

User intents schema
Our data platform user intents, summarising the key actions our combined user personas expect to be able to perform autonomously

Product Components

With a better overview of the users and motives for using a data platform, we turn our focus inwards and ask what our data platform offers. Similar to how market-facing products sit on a shelf or under the "Our Products" dropdown on a website, how do we represent our data platform as a portfolio of products? 

Approach

  • We only considered business user-facing aspects of the platform as product components. The goal here is for users to get an easy answer to "what's in it for me?"
  • We divided the platform into components that make sense to our combined user personas. This is an important point, because the purpose of this exercise is not to end with another technical architecture diagram. Components should be named in a way that clearly communicates their purpose.
  • Just as for user intents, we shouldn't be bound only by the current or "live" platform components. Even if there are some that are planned or still under consideration there is no reason not to show them among the components.

Outcome

Data platform components schema
Our data platform as a portfolio of user-facing products. No technical architecture diagrams were harmed in the making.

One of the advantages of dividing our platform in this way is that we can build a set of KPIs for each product component, which would then form the basis of future OKRs:

Component KPIs schema
Components help us to treat, and therefore measure, the various parts of the platform differently

A Note about Interfaces

At a later stage, we mapped the more familiar user interfaces (usually third party tools) to each component, so that users can clearly navigate to the specific tool(s) for accessing each component. We felt it important to distinguish between components and interfaces for these reasons:

  • Interfaces change. If we were to replace our primary visualisation interface from Tool A to Tool B, the interface has changed but the component remains unchanged.
  • Interfaces are increasingly multi-purpose. Many third party tools are expanding beyond their core offering and some even considered platforms in themselves. This means their purpose isn't always obvious to users; components help to clarify this. This also means there isn't a one-to-one mapping of interface to component. For this reason, we introduce the idea of primary and secondary interfaces. The primary interface is where the user should typically be directed. Failing that, the secondary interface may be partially able to serve as that product component.
Component interfaces schema
Mapping the interfaces to our product components helps users navigate the data platform between its various technologies and tools

Joining the dots

By taking a step back and linking the use cases, user personas, components this enables us to:

  • Look at our data platform as a manageable set of product components we can measure more easily and build OKRs around.
  • Identify gaps in our offering, where important user intents are not linked to a live product component. This is particularly useful as a starting point for building a longer-term strategy.
  • Decide how we best organise our teams around common KPIs.
  • How we structure our roadmap and communications to the business on what we're delivering, to who and why.
Data platform as product schema
Our data platform: still a tech stack, but also a product that delivers value to hundreds of different users for different motives, every day

Where we're heading next

We've introduced the concept of a data platform as a product, moving beyond simply viewing it as a tech stack. We did this by identifying who our users are, their intents and what our product offering is that can meet those needs.

As we've witnessed at Stuart most modern data platforms are complex and multifaceted, so the simple act of putting these on paper and visualising our platform as a collection of products that deliver value to our users is already a meaningful first step.

Our work is by no means done here —we are only at the start of our journey to becoming more product-led. These practices and principles will form a solid foundation for what comes next: discovering opportunities on an ongoing basis. Opportunities that will play a key role in building our strategy and OKRs for the coming year, becoming a data platform that continuously brings value to our users and to our business.


Main reading sources:

  • Continuous Discovery Habits, Teresa Torres
  • Inspired: How to Create Tech Products Customers Love, Marty Cagan
  • How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh, Zhamak Dehghani
  • How to Build your Data Platform like a Product, Barr Moses
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