In part two of our series, we examine the benefits of taking a deliberate approach to discovery related to the current business context of a problem through a people, process and technology perspective.
Some things are simply better together, like a PB&J sandwich with delicious individual ingredients that come together to create a simple culinary perfection.
In the first blog of our three-part series, we examined how people, process, and technology experts approach problem definitions with our unique perspectives. In this second installment, we’ll find out how each of these three offerings might approach a discovery phase, an effort where we brainstorm how to address the problem once a team defines it.
In the sample situation from our first blog, a company built a Robotic Process Automation (RPA) solution without doing its due diligence: a full process assessment and capture or coordinated engagement and implementation plan. This resulted in several adverse effects propagating throughout the business. In general, we often know what the problem is and the symptoms of the problem. But rather than jumping to a quick fix, we always advise clients to be deliberate about making sure we don’t repeat mistakes or assume the appropriate solutions. This is the perfect use case for a coordinated discovery effort.
What is a Discovery Phase?
If you have never participated in or seen a discovery phase to set up a solution delivery, let’s explore what it is and how it benefits the outcome of the eventual project. Discovery is all about learning. The best solutions come from a complete understanding of:
- What is going on?
- How is it happening?
- Why is it happening?
- Who is impacted and how?
Not answering these questions could yield a solution or product that is a poor fit for your situation.
During any discovery effort, you need to embed a small cross-functional team of people into the midst of the folks who experience the pain of whatever problem you are addressing. The discovery team conducts interviews, digs into the data, and works together to unearth exciting and effective solutions.
For example, an internal department could tell you they need a total revamp of their software. They’re losing customers, and they think it’s because their software isn’t as user-friendly as their direct competitors. Without planned and purposeful discovery, you may listen to their perspective, confirm they are losing business, and assume their assessment of the reason is correct.
You could spend millions of dollars building the latest and greatest piece of software – only to keep losing customers – because your solution doesn’t actually address or fix the root cause of the problem.
It turns out customers weren’t leaving because of the software itself, but because the infrastructure and processes behind the software were so slow, your team couldn’t load enough content. Your competitors had more content, and you fall further behind while you dedicate resources to a new build-out.
Discovery is a time for fact-finding by people with fresh and diverse perspectives and a time for brainstorming and creative thinking. By the end of the discovery phase, the team should understand the business and understand its challenges. Often, the team discovers other issues contributing to the original challenge they aimed to solve.
Each team – people, process and technology – all approach discovery a little differently.
The Organizational Change Management Perspective
During discovery, change management professionals tend to focus on the people and their experiences within the organization. At a high level, our work includes interviews and analysis to understand the current stakeholder landscape, learn how past changes worked (and more importantly, what didn’t work), capture employees’ sentiment about the proposed changes, and assess their capacity to learn new things and work differently in the future.
Our goal is to make sure we have a clear picture of how the company’s operating environment, people and culture intersect with the specific planned changes (and vice versa). So as we plan implementation, we set ourselves up for increased adoption and buy-in.
Specifically, in our example situation, we would focus on engaging key stakeholders to learn about the past implementation of the RPA bot. What did people feel went well and what didn’t work for them? Were they prepared for the impact on their daily work?
From the initial senior management team’s comments we shared in our last post, we already know we need to dive deeper into how different groups communicate with each other. It would be important for us to have interviews with the senior management team and talk to the people in their reporting structure to make sure we have a holistic picture.
This allows us to determine how to mitigate past pain and mistrust and bring employees successfully along the journey of process improvements and technology changes that will result from this discovery.
The Process Improvement Perspective
On the process side, we like to pair up with the Organizational Change Management (OCM) team to understand, from the people doing the work, what is going on today. We typically do this by joining the stakeholder interviews to understand where processes have opportunities for improvement. Additionally, we like to see how the front-line staff accomplishes work. With all this in mind, it’s important to call out a couple of key points to the process side of the work we’re doing during the discovery phase:
- Process work is best when partnered with OCM and Technology. Every improvement has a change that impacts someone (enter OCM), and technology makes most improvements better. Don’t do process work in a vacuum, and partner closely with your people and technology teams.
- Talking to front-line staff is important. Managers and leaders can tell us how teams should accomplish the work. However, the front-line staff tells us how they actually accomplish the work. Additionally, some of the best solutions to challenges front-line staff face each day come from the folks doing the work daily. They encounter the same problems and develop their own workarounds. Listening to ideas from front-line staff helps us identify more meaningful and impactful solutions.
In our sample situation, we would sit with the data entry team, observe the steps to enter the data and understand their frequently encountered issues. From that point, it’s best to follow that data through the rest of the process, observing how they create reports and make decisions based on the data.
Doing these interviews and observations help us to form a complete picture of the current state. Knowing the current state (how you do things today) may seem unnecessary, but doing this builds an essential foundation for building the future state. If we don’t have a good understanding of how your business works today, it’s harder to build a realistic future state or understand the impact of the changes from current to future state.
The discovery phase for process means working with the people and technology teams to understand the challenges the business faces and listening to the staff doing the work to develop a better solution for those challenges.
The Data and Analytics Perspective
During discovery, data-minded people tend to focus on uncovering whatever truth the numbers can tell. At a high level, we could conduct or analyze quantitative research that compares the current state with potential outcomes. Or, we could examine any relevant data architecture, data flow diagrams or data management structures in place.
Specifically, in our example, we’d focus on how the RPA bot is accessing, manipulating and storing data. We’d also consider how humans or data pipelines managed these before implementing the bots. Without a discovery phase, we might immediately jump to the conclusion that the bot is merely storing data incorrectly and immediately build another bot (or tweak the current one), so data “lands” in the appropriate format.
The discovery process could uncover a better way to store the data, given the innovations constantly taking place in the business. You could learn the introduction of the bots broke the reports, but they weren’t optimized anyway. So, your business would benefit more from launching a new reporting solution.
Without the context of people and processes, knowing how your data flows and how you store it in a vacuum is only a blurry view of the real picture — only presenting some numbers to describe why any solution is the best one generally doesn’t wow stakeholders or get widespread buy-in. When people interpret and fully use the numbers is when the magic happens.
Tie it Together
The discovery phase is the optimal time to enlist and enroll a diverse team to examine the situation from multiple perspectives. Assembling a team that is insatiably curious about learning why things operate as they do helps those impacted by the problem see the situation with fresh eyes, form options for resolution in an inclusive way and create buy-in. This is the time for embracing creativity and building rock-solid relationships with key stakeholders and difference-makers that can, in turn, bring the rest of the organization along on the journey.
Only by involving people, process and technology perspectives will your team successfully understand the situation to develop the overall best solution to implement. As a group, they can understand the people and processes at play, evaluate the current state, and identify any potential pitfalls. They will put together a thoroughly thought-through and actionable project plan to deliver a solution by working together.
About the Authors
Joel Longanecker is a Senior Operational Excellence Consultant for St. Louis with expertise in process improvement, business strategy, and has a Lean/Six Sigma Black Belt. He managed teams in the restaurant and healthcare industries for seven years before moving to business consulting. He enjoys hiking with his wife and three dogs or playing basketball and soccer with friends (when there isn’t a pandemic that ruins everything).
Becky Gandillon is a Data & Analytics Manager for St. Louis with expertise in data storytelling, data strategy and visualization. She worked in the healthcare industry as a biomedical engineer for eight years before making the jump to full-time analytics work. She and her husband have two daughters (ages two and four), and she spends some of her free time analyzing and predicting crowd patterns at Disney World.
Olivia Kopicky is a People and Change Manager in St. Louis with expertise in change management and user experience. She has worked with clients to realize their strategic business vision for over 7 years. Outside of work, Olivia enjoys cooking, spending time with her three dogs, and is preparing for her first child in June.
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