April 24, 2019
6 min

6 things to consider when setting up a research practice

With UX research so closely tied to product success, setting up a dedicated research practice is fast becoming important for many organizations. It’s not an easy process, especially for organizations that have had little to do with research, but the end goal is worth the effort.

But where exactly are you supposed to start? This article provides 6 key things to keep in mind when setting up a research practice, and should hopefully ensure you’ve considered all of the relevant factors.

1) Work out what your organization needs

The first and most simple step is to take stock of the current user research situation within the organization. How much research is currently being done? Which teams or individuals are talking to customers on an ongoing basis? Consider if there are any major pain points with the current way research is being carried out or bottlenecks in getting research insights to the people that need them. If research isn't being practiced, identify teams or individuals that don't currently have access to the resources they need, and consider ways to make insights available to the people that need them.

2) Consolidate your insights

UX research should be communicating with nearly every part of an organization, from design teams to customer support, engineering departments and C-level management. The insights that stem from user research are valuable everywhere. Of course, the opposite is also true: insights from support and sales are useful for understanding customers and how the current product is meeting people's needs.

When setting up a research practice, identify which teams you should align with, and then reach out. Sit down with these teams and explore how you can help each other. For your part, you’ll probably need to explain the what and why of user research within the context of your organization, and possibly even explain at a basic level some of the techniques you use and the data you can obtain.

Then, get in touch with other teams with the goal of learning from them. A good research practice needs a strong connection to other parts of the business with the express purpose of learning. For example, by working with your organization’s customer support team, you’ll have a direct line to some of the issues that customers deal with on a regular basis. A good working relationship here means they’ll likely feed these insights back to you, in order to help you frame your research projects.

By working with your sales team, they’ll be able to share issues prospective customers are dealing with. You can follow up on this information with research, the results of which can be fed into the development of your organization’s products.

It can also be fruitful to develop an insights repository, where researchers can store any useful insights and log research activities. This means that sales, customer support and other interested parties can access the results of your research whenever they need to.

When your research practice is tightly integrated other key areas of the business, the organization is likely to see innumerable benefits from the insights>product loop.

3) Figure out which tools you will use

By now you’ve hopefully got an idea of how your research practice will fit into the wider organization – now it’s time to look at the ways in which you’ll do your research. We’re talking, of course, about research methods and testing tools.

We won’t get into every different type of method here (there are plenty of other articles and guides for that), but we will touch on the importance of qualitative and quantitative methods. If you haven’t come across these terms before, here’s a quick breakdown:

  • Qualitative research – Focused on exploration. It’s about discovering things we cannot measure with numbers, and often involves speaking with users through observation or user interviews.
  • Quantitative research – Focused on measurement. It’s all about gathering data and then turning this data into usable statistics.

All user research methods are designed to deliver either qualitative or quantitative data, and as part of your research practice, you should ensure that you always try to gather both types. By using this approach, you’re able to generate a clearer overall picture of whatever it is you’re researching.

Next comes the software. A solid stack of user research testing tools will help you to put research methods into practice, whether for the purposes of card sorting, carrying out more effective user interviews or running a tree test.

There are myriad tools available now, and it can be difficult to separate the useful software from the chaff. Here’s a list of research and productivity tools that we recommend.

Tools for research

Here’s a collection of research tools that can help you gather qualitative and quantitative data, using a number of methods.

  • Treejack – Tree testing can show you where people get lost on your website, and help you take the guesswork out of information architecture decisions. Like OptimalSort, Treejack makes it easy to sort through information and pairs this with in-depth analysis features.
  • dScout – Imagine being able to get video snippets of your users as they answer questions about your product. That’s dScout. It’s a video research platform that collects in-context “moments” from a network of global participants, who answer your questions either by video or through photos.
  • Ethnio – Like dScout, this is another tool designed to capture information directly from your users. It works by showing an intercept pop-up to people who land on your website. Then, once they agree, it runs through some form of research.
  • OptimalSort – Card sorting allows you to get perspective on whatever it is you’re sorting and understand how people organize information. OptimalSort makes it easier and faster to sort through information, and you can access powerful analysis features.
  • Reframer – Taking notes during user interviews and usability tests can be quite time-consuming, especially when it comes to analyze the data. Reframer gives individuals and teams a single tool to store all of their notes, along with a set of powerful analysis features to make sense of their data.
  • Chalkmark – First-click testing can show you what people click on first in a user interface when they’re asked to complete a task. This is useful, as when people get their first click correct, they’re much more likely to complete their task. Chalkmark makes the process of setting up and running a first-click test easy. What’s more, you’re given comprehensive analysis tools, including a click heatmap.

Tools for productivity

These tools aren’t necessarily designed for user research, but can provide vital links in the process.

  • Whimsical – A fantastic tool for user journeys, flow charts and any other sort of diagram. It also solves one of the biggest problems with online whiteboards – finicky object placement.
  • Descript – Easily transcribe your interview and usability test audio recordings into text.
  • Google Slides – When it inevitably comes time to present your research findings to stakeholders, use Google Slides to create readable, clear presentations.

4) Figure out how you’ll track findings over time

With some idea of the research methods and testing tools you’ll be using to collect data, now it’s time to think about how you’ll manage all of this information. A carefully ordered spreadsheet and folder system can work – but only to an extent. Dedicated software is a much better choice, especially given that you can scale these systems much more easily.

A dedicated home for your research data serves a few distinct purposes. There’s the obvious benefit of being able to access all of your findings whenever you need them, which means it’s much easier to create personas if the need arises. A dedicated home also means your findings will remain accessible and useful well into the future.

When it comes to software, Reframer stands as one of the better options for creating a detailed customer insights repository as you’re able to capture your sessions directly in the tool and then apply tags afterwards. You can then easily review all of your observations and findings using the filtering options. Oh, and there’s obviously the analysis side of the tool as well.

If you’re looking for a way to store high-level findings – perhaps if you’re intending to share this data with other parts of your organization – then a tool like Confluence or Notion is a good option. These tools are basically wikis, and include capable search and navigation options too.

5) Where will you get participants from?

A pool of participants you can draw from for your user research is another important part of setting up a research practice. Whenever you need to run a study, you’ll have real people you can call on to test, ask questions and get feedback from.

This is where you’ll need to partner other teams, likely sales and customer support. They’ll have direct access to your customers, so make sure to build a strong relationship with these teams. If you haven’t made introductions, it can helpful to put together a one-page sheet of information explaining what UX research is and the benefits of working with your team.

You may also want to consider getting in some external help. Participant recruitment services are a great way to offload the heavy lifting of sourcing quality participants – often one of the hardest parts of the research process.

6) Work out how you'll communicate your research

Perhaps one of the most important parts of being a user researcher is taking the findings you uncover and communicating them back to the wider organization. By feeding insights back to product, sales and customer support teams, you’ll form an effective link between your organization’s customers and your organization. The benefits here are obvious. Product teams can build products that actually address customer pain points, and sales and support teams will better understand the needs and expectations of customers.

Of course, it isn’t easy to communicate findings. Here are a few tips:

  • Document your research activities: With a clear record of your research, you’ll find it easier to pull out relevant findings and communicate these to the right teams.
  • Decide who needs what: You’ll probably find that certain roles (like managers) will be best served by a high-level overview of your research activities (think a one-page summary), while engineers, developers and designers will want more detailed research findings.

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How to create a UX research plan

Summary: A detailed UX research plan helps you keep your overarching research goals in mind as you work through the logistics of a research project.

There’s nothing quite like the feeling of sitting down to interview one of your users, steering the conversation in interesting directions and taking note of valuable comments and insights. But, as every researcher knows, it’s also easy to get carried away. Sometimes, the very process of user research can be so engrossing that you forget the reason you’re there in the first place, or unexpected things that come up that can force you to change course or focus.

This is where a UX research plan comes into play. Taking the time to set up a detailed overview of your high-level research goals, team, budget and timeframe will give your research the best chance of succeeding. It's also a good tool for fostering alignment - it can make sure everyone working on the project is clear on the objectives and timeframes. Over the course of your project, you can refer back to your plan – a single source of truth. After all, as Benjamin Franklin famously said: “By failing to prepare, you are preparing to fail”.

In this article, we’re going to take a look at the best way to put together a research plan.

Your research recipe for success

Any project needs a plan to be successful, and user research is no different. As we pointed out above, a solid plan will help to keep you focused and on track during your research – something that can understandably become quite tricky as you dive further down the research rabbit hole, pursuing interesting conversations during user interviews and running usability tests. Thought of another way, it’s really about accountability. Even if your initial goal is something quite broad like “find out what’s wrong with our website”, it’s important to have a plan that will help you to identify when you’ve actually discovered what’s wrong.

So what does a UX research plan look like? It’s basically a document that outlines the where, why, who, how and what of your research project.

It’s time to create your research plan! Here’s everything you need to consider when putting this plan together.

Make a list of your stakeholders

The first thing you need to do is work out who the stakeholders are on your project. These are the people who have a stake in your research and stand to benefit from the results. In those instances where you’ve been directed to carry out a piece of research you’ll likely know who these people are, but sometimes it can be a little tricky. Stakeholders could be C-level executives, your customer support team, sales people or product teams. If you’re working in an agency or you’re freelancing, these could be your clients.

Make a list of everyone you think needs to be consulted and then start setting up catch-up sessions to get their input. Having a list of stakeholders also makes it easy to deliver insights back to these people at the end of your research project, as well as identify any possible avenues for further research. This also helps you identify who to involve in your research (not just report findings back to).

Action: Make a list of all of your stakeholders.

Write your research questions

Before we get into timeframes and budgets you first need to determine your research questions, also known as your research objectives. These are the ‘why’ of your research. Why are you carrying out this research? What do you hope to achieve by doing all of this work? Your objectives should be informed by discussions with your stakeholders, as well as any other previous learnings you can uncover. Think of past customer support discussions and sales conversations with potential customers.

Here are a few examples of basic research questions to get you thinking. These questions should be actionable and specific, like the examples we’ve listed here:

  • “How do people currently use the wishlist feature on our website?”
  • “How do our current customers go about tracking their orders?”
  • “How do people make a decision on which power company to use?”
  • “What actions do our customers take when they’re thinking about buying a new TV?”

A good research question should be actionable in the sense that you can identify a clear way to attempt to answer it, and specific in that you’ll know when you’ve found the answer you’re looking for. It's also important to keep in mind that your research questions are not the questions you ask during your research sessions - they should be broad enough that they allow you to formulate a list of tasks or questions to help understand the problem space.

Action: Create a list of possible research questions, then prioritize them after speaking with stakeholders.

What is your budget?

Your budget will play a role in how you conduct your research, and possibly the amount of data you're able to gather.

Having a large budget will give you flexibility. You’ll be able to attract large numbers of participants, either by running paid recruitment campaigns on social media or using a dedicated participant recruitment service. A larger budget helps you target more people, but also target more specific people through dedicated participant services as well as recruitment agencies.

Note that more money doesn't always equal better access to tools - e.g. if I work for a company that is super strict on security, I might not be able to use any tools at all. But it does make it easier to choose appropriate methods and that allow you to deliver quality insights. E.g. a big budget might allow you to travel, or do more in-person research which is otherwise quite expensive.

With a small budget, you’ll have to think carefully about how you’ll reward participants, as well as the number of participants you can test. You may also find that your budget limits the tools you can use for your testing. That said, you shouldn’t let your budget dictate your research. You just have to get creative!

Action: Work out what the budget is for your research project. It’s also good to map out several cheaper alternatives that you can pursue if required.

How long will your project take?

How long do you think your user research project will take? This is a necessary consideration, especially if you’ve got people who are expecting to see the results of your research. For example, your organization’s marketing team may be waiting for some of your exploratory research in order to build customer personas. Or, a product team may be waiting to see the results of your first-click test before developing a new signup page on your website.

It’s true that qualitative research often doesn’t have a clear end in the way that quantitative research does, for example as you identify new things to test and research. In this case, you may want to break up your research into different sub-projects and attach deadlines to each of them.

Action: Figure out how long your research project is likely to take. If you’re mixing qualitative and quantitative research, split your project timeframe into sub-projects to make assigning deadlines easier.

Understanding participant recruitment

Who you recruit for your research comes from your research questions. Who can best give you the answers you need? While you can often find participants by working with your customer support, sales and marketing teams, certain research questions may require you to look further afield.

The methods you use to carry out your research will also have a part to play in your participants, specifically in terms of the numbers required. For qualitative research methods like interviews and usability tests, you may find you’re able to gather enough useful data after speaking with 5 people. For quantitative methods like card sorts and tree tests, it’s best to have at least 30 participants. You can read more about participant numbers in this Nielsen Norman article.

At this stage of the research plan process, you’ll also want to write some screening questions. These are what you’ll use to identify potential participants by asking about their characteristics and experience.

Action: Define the participants you’ll need to include in your research project, and where you plan to source them. This may require going outside of your existing user base.

Which research methods will you use?

The research methods you use should be informed by your research questions. Some questions are best answered by quantitative research methods like surveys or A/B tests, with others by qualitative methods like contextual inquiries, user interviews and usability tests. You’ll also find that some questions are best answered by multiple methods, in what’s known as mixed methods research.

If you’re not sure which method to use, carefully consider your question. If we go back to one of our earlier research question examples: “How do our current customers go about tracking their orders?”, we’d want to test the navigation pathways.

If you’re not sure which method to use, it helps to carefully consider your research question. Let’s use one of our earlier examples: “Is it easy for users to check their order history in our iPhone app?” as en example. In this case, because we want to see how users move through our app, we need a method that’s suited to testing navigation pathways – like tree testing.

For the question: “What actions do our customers take when they’re thinking about buying a new TV?”, we’d want to take a different approach. Because this is more of an exploratory question, we’re probably best to carry out a round of user interviews and ask questions about their process for buying a TV.

Action: Before diving in and setting up a card sort, consider which method is best suited to answer your research question.

Develop your research protocol

A protocol is essentially a script for your user research. For the most part, it’s a list of the tasks and questions you want to cover in your in-person sessions. But, it doesn’t apply to all research types. For example, for a tree test, you might write your tasks, but this isn't really a script or protocol.

Writing your protocol should start with actually thinking about what these questions will be and getting feedback on them, as well as:

  • The tasks you want your participants to do (usability testing)
  • How much time you’ve set aside for the session
  • A script or description that you can use for every session
  • Your process for recording the interviews, including how you’ll look after participant data.

Action: This is essentially a research plan within a research plan – it’s what you’d take to every session.

Happy researching!

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The Evolution of UX Research: Digital Twins and the Future of User Insight

Introduction

User Experience (UX) research has always been about people. How they think, how they behave, what they need, and—just as importantly—what they don’t yet realise they need. Traditional UX methodologies have long relied on direct human input: interviews, usability testing, surveys, and behavioral observation. The assumption was clear—if you want to understand people, you have to engage with real humans.

But in 2025, that assumption is being challenged.

The emergence of digital twins and synthetic users—AI-powered simulations of human behavior—is changing how researchers approach user insights. These technologies claim to solve persistent UX research problems: slow participant recruitment, small sample sizes, high costs, and research timelines that struggle to keep pace with product development. The promise is enticing: instantly accessible, infinitely scalable users who can test, interact, and generate feedback without the logistical headaches of working with real participants.

Yet, as with any new technology, there are trade-offs. While digital twins may unlock efficiencies, they also raise important questions: Can they truly replicate human complexity? Where do they fit within existing research practices? What risks do they introduce?

This article explores the evolving role of digital twins in UX research—where they excel, where they fall short, and what their rise means for the future of human-centered design.

The Traditional UX Research Model: Why Change?

For decades, UX research has been grounded in methodologies that involve direct human participation. The core methods—usability testing, user interviews, ethnographic research, and behavioral analytics—have been refined to account for the unpredictability of human nature.

This approach works well, but it has challenges:

  1. Participant recruitment is time-consuming. Finding the right users—especially niche audiences—can be a logistical hurdle, often requiring specialised panels, incentives, and scheduling gymnastics.
  2. Research is expensive. Incentives, moderation, analysis, and recruitment all add to the cost. A single usability study can run into tens of thousands of dollars.
  3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
  4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

In short: traditional UX research provides depth and authenticity, but it’s not always fast or scalable.

Digital twins and synthetic users aim to change that.

What Are Digital Twins and Synthetic Users?

While the terms digital twins and synthetic users are sometimes used interchangeably, they are distinct concepts.

Digital Twins: Simulating Real-World Behavior

A digital twin is a data-driven virtual representation of a real-world entity. Originally developed for industrial applications, digital twins replicate machines, environments, and human behavior in a digital space. They can be updated in real time using live data, allowing organisations to analyse scenarios, predict outcomes, and optimise performance.

In UX research, human digital twins attempt to replicate real users' behavioral patterns, decision-making processes, and interactions. They draw on existing datasets to mirror real-world users dynamically, adapting based on real-time inputs.

Synthetic Users: AI-Generated Research Participants

While a digital twin is a mirror of a real entity, a synthetic user is a fabricated research participant—a simulation that mimics human decision-making, behaviors, and responses. These AI-generated personas can be used in research scenarios to interact with products, answer questions, and simulate user journeys.

Unlike traditional user personas (which are static profiles based on aggregated research), synthetic users are interactive and capable of generating dynamic feedback. They aren’t modeled after a specific real-world person, but rather a combination of user behaviors drawn from large datasets.

Think of it this way:

  • A digital twin is a highly detailed, data-driven clone of a specific person, customer segment, or process.
  • A synthetic user is a fictional but realistic simulation of a potential user, generated based on behavioral patterns and demographic characteristics.

Both approaches are still evolving, but their potential applications in UX research are already taking shape.

Where Digital Twins and Synthetic Users Fit into UX Research

The appeal of AI-generated users is undeniable. They can:

  • Scale instantly – Test designs with thousands of simulated users, rather than just a handful of real participants.
  • Eliminate recruitment bottlenecks – No need to chase down participants or schedule interviews.
  • Reduce costs – No incentives, no travel, no last-minute no-shows.
  • Enable rapid iteration – Get user insights in real time and adjust designs on the fly.
  • Generate insights on sensitive topics – Synthetic users can explore scenarios that real participants might find too personal or intrusive.

These capabilities make digital twins particularly useful for:

  • Early-stage concept validation – Rapidly test ideas before committing to development.
  • Edge case identification – Run simulations to explore rare but critical user scenarios.
  • Pre-testing before live usability sessions – Identify glaring issues before investing in human research.

However, digital twins and synthetic users are not a replacement for human research. Their effectiveness is limited in areas where emotional, cultural, and contextual factors play a major role.

The Risks and Limitations of AI-Driven UX Research

For all their promise, digital twins and synthetic users introduce new challenges.

  1. They lack genuine emotional responses.
    AI can analyse sentiment, but it doesn’t feel frustration, delight, or confusion the way a human does. UX is often about unexpected moments—the frustrations, workarounds, and “aha” realisations that define real-world use.
  2. Bias is a real problem.
    AI models are trained on existing datasets, meaning they inherit and amplify biases in those datasets. If synthetic users are based on an incomplete or non-diverse dataset, the research insights they generate will be skewed.
  3. They struggle with novelty.
    Humans are unpredictable. They find unexpected uses for products, misunderstand instructions, and behave irrationally. AI models, no matter how advanced, can only predict behavior based on past patterns—not the unexpected ways real users might engage with a product.
  4. They require careful validation.
    How do we know that insights from digital twins align with real-world user behavior? Without rigorous validation against human data, there’s a risk of over-reliance on synthetic feedback that doesn’t reflect reality.

A Hybrid Future: AI + Human UX Research

Rather than viewing digital twins as a replacement for human research, the best UX teams will integrate them as a complementary tool.

Where AI Can Lead:

  • Large-scale pattern identification
  • Early-stage usability evaluations
  • Speeding up research cycles
  • Automating repetitive testing

Where Humans Remain Essential:

  • Understanding emotion, frustration, and delight
  • Detecting unexpected behaviors
  • Validating insights with real-world context
  • Ethical considerations and cultural nuance

The future of UX research is not about choosing between AI and human research—it’s about blending the strengths of both.

Final Thoughts: Proceeding With Caution and Curiosity

Digital twins and synthetic users are exciting, but they are not a magic bullet. They cannot fully replace human users, and relying on them exclusively could lead to false confidence in flawed insights.

Instead, UX researchers should view these technologies as powerful, but imperfect tools—best used in combination with traditional research methods.

As with any new technology, thoughtful implementation is key. The real opportunity lies in designing research methodologies that harness the speed and scale of AI without losing the depth, nuance, and humanity that make UX research truly valuable.

The challenge ahead isn’t about choosing between human or synthetic research. It’s about finding the right balance—one that keeps user experience truly human-centered, even in an AI-driven world.

This article was researched with the help of Perplexity.ai. 

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Meera Pankhania: From funding to delivery - Ensuring alignment from start to finish

It’s a chicken and egg situation when it comes to securing funding for a large transformation program in government. On one hand, you need to submit a business case and, as part of that, you need to make early decisions about how you might approach and deliver the program of work. On the other hand, you need to know enough about the problem you are going to solve to ensure you have sufficient funding to understand the problem better, hire the right people, design the right service, and build it the right way. 

Now imagine securing hundreds of millions of dollars to design and build a service, but not feeling confident about what the user needs are. What if you had the opportunity to change this common predicament and influence your leadership team to carry out alignment activities, all while successfully delivering within the committed time frames?

Meera Pankhania, Design Director and Co-founder of Propel Design, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, on traceability and her learnings from delivering a $300 million Government program.

In her talk, Meera helps us understand how to use service traceability techniques in our work and apply them to any environment - ensuring we design and build the best service possible, no matter the funding model.

Background on Meera Pankhania

As a design leader, Meera is all about working on complex, purpose-driven challenges. She helps organizations take a human-centric approach to service transformation and helps deliver impactful, pragmatic outcomes while building capability and leading teams through growth and change.

Meera co-founded Propel Design, a strategic research, design, and delivery consultancy in late 2020. She has 15 years of experience in service design, inclusive design, and product management across the private, non-profit, and public sectors in both the UK and Australia. 

Meera is particularly interested in policy and social design. After a stint in the Australian Public Service, Meera was appointed as a senior policy adviser to the NSW Minister for Customer Service, Hon. Victor Dominello MP. In this role, she played a part in NSW’s response to the COVID pandemic, flexing her design leadership skills in a new, challenging, and important context.

Contact Details:

Email address: meera@propeldesign.com.au

Find Meera on LinkedIn  

From funding to delivery: ensuring alignment from start to finish 🏁🎉👏

Meera’s talk explores a fascinating case study within the Department of Employment Services (Australia) where a substantial funding investment of around $300 million set the stage for a transformative journey. This funding supported the delivery of a revamped Employment Services Model, which had the goal of delivering better services to job seekers and employers, and a better system for providers within this system. The project had a focus on aligning teams prior to delivery, which resulted in a huge amount of groundwork for Meera.

Her journey involved engaging various stakeholders within the department, including executives, to understand the program as a whole and what exactly needed to be delivered. “Traceability” became the watchword for this project, which is laid out in three phases.

  • Phase 1: Aligning key deliverables
  • Phase 2: Ensuring delivery readiness
  • Phase 3: Building sustainable work practices

Phase 1: Aligning key deliverables 🧮

Research and discovery (pre-delivery)

Meera’s work initially meant conducting extensive research and engagement with executives, product managers, researchers, designers, and policymakers. Through this process, a common theme was identified – the urgent (and perhaps misguided) need to start delivering! Often, organizations focus on obtaining funding without adequately understanding the complexities involved in delivering the right services to the right users, leading to half-baked delivery.

After this initial research, some general themes started to emerge:

  1. Assumptions were made that still needed validation
  2. Teams weren’t entirely sure that they understood the user’s needs
  3. A lack of holistic understanding of how much research and design was needed

The conclusion of this phase was that “what” needed to be delivered wasn’t clearly defined. The same was true for “how” it would be delivered.

Traceability

Meera’s journey heavily revolved around the concept of "traceability” and sought to ensure that every step taken within the department was aligned with the ultimate goal of improving employment services. Traceability meant having a clear origin and development path for every decision and action taken. This is particularly important when spending taxpayer dollars!

So, over the course of eight weeks (which turned out to be much longer), the team went through a process of combing through documents in an effort to bring everything together to make sense of the program as a whole. This involved some planning, user journey mapping, and testing and refinement. 

Documenting Key Artifacts

Numerous artifacts and documents played a crucial role in shaping decisions. Meera and her team gathered and organized these artifacts, including policy requirements, legislation, business cases, product and program roadmaps, service maps, and blueprints. The team also included prior research insights and vision documents which helped to shape a holistic view of the required output.

After an effort of combing through the program documents and laying everything out, it became clear that there were a lot of gaps and a LOT to do.

Prioritising tasks

As a result of these gaps, a process of task prioritization was necessary. Tasks were categorized based on a series of factors and then mapped out based on things like user touch points, pain points, features, business policy, and technical capabilities.

This then enabled Meera and the team to create Product Summary Tiles. These tiles meant that each product team had its own summary ahead of a series of planning sessions. It gave them as much context (provided by the traceability exercise) as possible to help with planning. Essentially, these tiles provided teams with a comprehensive overview of their projects i.e. what their user needs, what certain policies require them to deliver, etc.  

Phase 2: Ensuring delivery readiness 🙌🏻

Meera wanted every team to feel confident that we weren’t doing too much or too little in order to design and build the right service, the right way.

Standard design and research check-ins were well adopted, which was a great start, but Meera and the team also built a Delivery Readiness Tool. It was used to assess a team's readiness to move forward with a project. This tool includes questions related to the development phase, user research, alignment with the business case, consideration of policy requirements, and more. Ultimately, it ensures that teams have considered all necessary factors before progressing further. 

Phase 3: Building sustainable work practices 🍃

As the program progressed, several sustainable work practices emerged which Government executives were keen to retain going forward.

Some of these included:

  • ResearchOps Practice: The team established a research operations practice, streamlining research efforts and ensuring that ongoing research was conducted efficiently and effectively.
  • Consistent Design Artifacts: Templates and consistent design artifacts were created, reducing friction and ensuring that teams going forward started from a common baseline.
  • Design Authority and Ways of Working: A design authority was established to elevate and share best practices across the program.
  • Centralized and Decentralized Team Models: The program showcased the effectiveness of a combination of centralized and decentralized team models. A central design team provided guidance and support, while service design leads within specific service lines ensured alignment and consistency.

Why it matters 🔥

Meera's journey serves as a valuable resource for those working on complex design programs, emphasizing the significance of aligning diverse stakeholders and maintaining traceability. Alignment and traceability are critical to ensuring that programs never lose sight of the problem they’re trying to solve, both from the user and organization’s perspective. They’re also critical to delivering on time and within budget!

Traceability key takeaways 🥡

  • Early Alignment Matters: While early alignment is ideal, it's never too late to embark on a traceability journey. It can uncover gaps, increase confidence in decision-making, and ensure that the right services are delivered.
  • Identify and audit: You never know what artifacts will shape your journey. Identify everything early, and don’t be afraid to get clarity on things you’re not sure about.
  • Conducting traceability is always worthwhile: Even if you don’t find many gaps in your program, you will at least gain a high level of confidence that your delivery is focused on the right things.

Delivery readiness key takeaways 🥡

  • Skills Mix is Vital: Assess and adapt team member roles to match their skills and experiences, ensuring they are positioned optimally.
  • Not Everyone Shares the Same Passion: Recognize that not everyone will share the same level of passion for design and research. Make the relevance of these practices clear to all team members.

Sustainability key takeaways 🥡

  • One Size Doesn't Fit All: Tailor methodologies, templates, and practices to the specific needs of your organization.
  • Collaboration is Key: Foster a sense of community and collective responsibility within teams, encouraging shared ownership of project outcomes.

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