The Microguide To:
How to avoid common pitfalls and ensure your initiatives deliver value, fast
Business survival is dependent on making smarter decisions with the resources and data at your disposal right now. As a result, organizations have been forced to roll out solutions more rapidly to address market needs, and digital transformation has been key to moving forward and accelerating that innovation.
For many businesses, this acceleration has taken the shape of data innovation labs. You may have heard these labs being called a variety of things: accelerators, innovation spaces, incubators, research hubs — and they’re becoming more ubiquitous. Walmart, Lockheed Martin, Coca-Cola, Daimler, H&M, and many others have opened innovation labs to accelerate new products, understand market trends, and develop solutions for customers. According to Harvard Business Review, over half of the financial services firms have their own dedicated spaces, and it’s almost an unspoken industry standard for retailers and healthcare companies to have at least one innovation lab.
Generally, seeing more organizations take the initiative to begin innovation labs is a great sign. But do these innovation labs add value and generate growth? Just because it’s a common approach doesn’t always mean these labs are creating value for businesses. In fact, according to a report from Capgemini, the vast majority of innovation labs — up to 90%, one expert says — fail to deliver on their promise.
Let’s dig into why that is – discussing some of the inherent issues that can crop up with data innovation labs depending on your company’s capabilities, foundation, alignment, etc., and actions you can take to better ensure that your data innovation lab delivers in the way you intend it to.
Many teams can’t see innovation progress for a multitude of reasons: limited resources, not having the required talent, lack of top-down support, outdated processes or tooling. Businesses need to have a better knack for identifying these innovation lab red flags and others like them in order to navigate the biggest question of the innovation lab process – is this project going to work or isn’t it?
A lack of a really robust project-selection process can be a big obstacle to innovation labs. Teams need to explore what they really want to spend their time on or face making the project feel like an academic exercise. Historically, business leadership has a bit of a track record for overrunning projects. This drastically erodes trust with the exact individuals tasked with carrying the innovation lab forward because it creates a weak framework that doesn’t take innovation team needs into account.
When working through the data innovation lab process, teams need to determine whether the initiative is feasible, desirable, and viable for moving the business forward in order for it to be selected.
If, for a given initiative, you get feedback along the lines of, “Why are we doing this?” from your stakeholders, you already have a problem with buy-in from the very teams who will be working on it. This problem tends to arise when the specific innovation center doesn’t have a clear strategy that’s aligned with the organization’s — or simply doesn’t have one at all. In this space, teams can be left unsure of whether they’re supposed to serve the core business or disrupt it.
How can teams confront this issue quickly so momentum doesn’t die out and they can gain more buy-in? It all goes back to demonstrating value, but it can also be a bit of a chicken and egg situation. You will always need top-down buy-in for value. Teams need to avoid having the initiative turn into an academic exercise and be able to show how the lab accelerated an idea beyond how it could be built up on its own.
When enterprise businesses find they lack the ability to quickly scale, whether from having a poor foundational data layer or not having the necessary resources or required talent – it becomes a two-fold problem for your data innovation lab.
Not having the necessary data for setting a good foundation already stalls innovation, but taking the time to resolve these things (if you can) only compounds the problem further. In this scenario, teams get overwhelmed when they find certain parts of their innovation initiative, like getting the right data or setting a foundation, take far too long to provide any value.
Any innovation lab starts to lose its appeal to leaders when they’re unable to measure its success or see how it positively affects their bottom line. The perceived value of innovation vs. production will be different for every organization, but the issue here is that many innovation labs are started without any success metrics in place at all.
This is a serious problem – innovation labs that have poorly conceived metrics or that lack them entirely are essentially doomed to fail. Even if your team is given more room to run, your innovation lab eventually needs to have some sort of return, one that should be determined in advance of the innovation lab rollout and tracked over time.
Knowing their organization needs to be innovative to remain competitive, oftentimes business leaders make the mistake of getting so preoccupied with whether or not they could that they don’t stop to think if they should (Thanks to Jeff Goldblum for this piece of wisdom that applies far beyond the coasts of Jurassic Park). This logic enters into data innovation labs often and can completely sink initiatives.
All of this can lead to innovation theater. Essentially, this is an innovation initiative that is done purely to signal that innovation is happening – innovation for innovation’s sake. Boards and business leaders roll out labs that lack any real impact in order to check the box of having a team (that may or may not have the necessary talent and resources) dedicated to innovation.
As we mentioned earlier, some people feel data innovation labs can be something of an academic exercise, so data teams and leaders need to establish value early to avoid these issues. Companies need to figure out how they want to define their project selection process and define what’s worthy of project selection.
For RevUnit’s part, we had people looking for, identifying, breaking down, and analyzing projects – then bring them to the team in a pitch-style process. With that foundation, companies can then build a rubric or scoresheet to assess the projects they’re considering across certain important metrics. The project with the highest score may not necessarily always win, but it injects intentionality into the selection process. In essence, this scoring acts as a conversation on what the team should move forward with.
While we wholeheartedly are in favor of intentional innovation, organizations shouldn’t do away with their entire way of working in pursuit of it. It is neither possible nor practical to simply throw out what’s working and embrace the newest innovations without any consideration. The problem though is that organizations rarely have the tools, processes, or talent to operate in such a way without affecting other areas of business. So how do they bridge this gap?
Most leaders are starting to understand that they rarely have all of the qualified individuals to carry out innovation at their own organization, and that a better way of working will almost certainly come from outside the company. Accelerators provide the necessary “innovation interface” that allow big companies to connect with and evaluate outside innovations without having to change their entire organizational structure.
So what does this form of data innovation lab look like? Again, the idea behind it needs to be feasible, desirable, and viable, and building a framework around these three things is how a lab should be approached. It casts the vision of what the end-product could be, but the project still has to solve problems for customers or internal users.
For our part, RevUnit builds out a process and a framework, and works in tandem with our clients and continues to innovate alongside them – essentially showing them the ropes – and then either continues the partnership or hands things off to a client tech team to continue on their own so they can be self-sufficient in that space.
This process is akin to building the plane while it’s in flight. In this way, we simultaneously build value while defining what the necessary framework could look like. Ideas came from a variety of sources, like leaders who felt X was necessary to implement the lab, individuals who had good ideas but were unsure how to execute – all to get solid buy-in and avoid making the innovation lab feel like an academic exercise.
There’s also a definite marketing element that comes with every lab, where you need to not only deliver results, but share your vision and outcomes with the rest of the organization. Teams are just generally skeptical about innovation labs, so telling the right story is essential.
To explore the notion of whether they are experiencing innovation theater, or whether a certain initiative they’re working on just isn’t possible, Basecamp uses a model where they work on a client’s data lab initiative for six weeks, then reassesses to see whether a project is still viable before moving on for another six weeks or any additional time.
In RevUnit’s innovation work with a leading national retailer, prototyping was the key to building out the initiative, getting research and feedback on what users want to see. With this cadence, RevUnit was able to take a playbook (a list of the artifacts needed to build, roll out, and scale the specific technology product) back to the users to validate and build a relationship with the client tech team early in order to have them buy into the solution since the project would eventually be handed off to them entirely. Individuals from the client could also sit in on the process and see what it looked like in practice in order to build buy-in and show value to those who would be taking it over.
Before they get going, leaders need to give serious consideration to the implications of opening a lab – whether it will complement or seriously disrupt their current way of working – and go through the difficult task of determining how these new innovative ideas will be executed.
Something RevUnit has uncovered in all of our engagements through our way of working is that it’s essential to let clients know the tech and hiring needs that they have to address before diving in. After all, the idea isn’t to run the innovation lab forever. RevUnit works alongside our clients, but the goal is to grow the initiative and hand it off to the client once they possess the required knowledge and bandwidth to carry on alone.
Consider collaborating with an external partner to find a solution that’s feasible, desirable, and viable for addressing the challenges your organization is facing. You’ll deliver a better product, grow stronger development skills, and learn best practices to carry your products into the future.