The art of templatized thinking

Who is this relevant for

When you're working at the intersection of business strategy, consumer needs and human-centered innovation, more often that not, you come across the need to identify opportunities for your clients, organizations and communities. These opportunities don't appear out of thin air or show up when you google for them. They are deep-seated somewhere (unknown, at the start) and would need your undeterred finder skills to look for data which can consequently lead you to the 'real opportunities'.

Human brain is designed to respond to structures

Data can be naive most times. It's the human skill that gets them to make sense, when put together logically. Also, data can be overwhelming and would need human skills, again, to filter them as per contextual relevance. But most of all, data can act like a 'black hole' - the more you look for them, the more you find them. Imagine falling into an interminable trap with no harness. That's where it becomes key to tame data even before you seek for it and the 'harness' that helps you navigate through this process is the art of templatizing.

Your brain responds well to what it knows than to what it doesn't. Just like research.

-Charles F.

Templatizing is not storyboarding

If you have been in the consulting space, you would have, inarguably, come across the term 'storyboarding'. But this is not that. Templatizing is an art of preempting and visualizing data in form of matrices and thinking how to create a compelling canvas, where each cell has a story to tell. The template acts like a sounding board for the information you find and the insights you make of it. This, then, becomes your hook and makes it overly simple, for you to logically connect the dots for your stakeholders and convince them of your findings and recommendations.

A sample template, above, to gauge impact of the global pandemia around Covid-19 on the gig Economy. This isn't the final template but a good starting point before one looks for data.

Ambiguity doesn't have to be the starting point

Imagine you were looking for opportunities in the digital space for the healthcare industry. How would you go about it? This may seem more open-ended than something like identifying opportunities around intelligent technologies like machine learning for the healthcare industry. Although, the latter looks scoped-down, it still is a vast landscape to begin with.

Let's consider a totally different scenario - Imagine you were conducting a primary research as part of whitespace innovation for your client. You are talking to respondents and taking notes on the side in your iPad. You come back and sit with your team for a debrief. You have noted it all down - What respondents said, what behaviours you observed, what expectations they shared and more. But how do you start the debrief, which points from the notes to start with, how to find what data is on which page, how would have your team members captured their notes, what is an effective way to go through the key observations and ask what your team members thought about it. Overwhelming, right?

Let's take a step back. Imagine at the start of your research, you and your team had a template at your disposal to capture the information you came across in both the examples. This template had designated columns and rows to catalogue data while you were getting them - making it easier for you to capture, map, and analyze, all at the same time. Having a template like this, beforehand, minimizes the ambiguity. With the comfort of familiarity and guided research, you know what you're looking for.

Stakeholder approach is the science

There's no one template. It is demandingly different, most times. This ensues from the fact that every client that comes with a problem statement, comes with its nuances, too. That's where it becomes key to design a unique template that would best-fit the needs and persona of your client. A template must, at all times and for all problem statements, cover atleast the following elements:

• Key functions around which the problem statement is based off of - Considering the first example, the key functions become the value chain of the client's business which is now the cornerstone to your research
• Key themes of analysis - These are the core vectors of comparison and the most critical piece in a template. They are the objects of comparison and for the first example, they become opportunity themes in healthcare industry. For example intelligence for predictive care, voice-enabled remote care, and smart diagnosis among others
• Partners and Competing entities - For the same example, this element can be covered by looking at partners across the valechain and competitors of the client
• Indicators to show impact or intensity of comparing variables - When you've reached this point, you would have already have a reasonable idea around the indicators. In this case, indicators could be gauging the level of maturity of players from previous step, in the technology - ranging from early-stage, prototyping, growing, to leading. Furthermore, the indicators widely vary on the type of comparison you want to make and the number of levels you want to assign
• Objective takeaways - A template becomes powerful when it has objective takeaways. This is where skills of synthesizing and insights generation come into picture and it is imperative that by the end of filling a template out, you have conclusive answers to the most pertinent consulting question - 'If this, so what?'

Staying open to the unexpected

A good template covers all the above aspects but a great template is flexible and capable of adding elements, if chanced upon an 'Eureka moment' of data research. Many a times (and that is mostly), it so happens that you may across something very relevant or powerful through your research but you may not be able to fit it into your template. That's where you add on this additional row or column to the template and marry yourself to it, for the rest of the research. What transpires at the end of it, is a comprehensive canvas of logically-bunched data, connected dots and undeniable takeaways. Therefore, it is essential to stay open and embrace serendipity, wherever applicable, throughout your research.

Where can you apply this

Practically everywhere. From 0 to 1 type of assignments to 1 to n types, thinking in templates, jumpstarts things for you by taking away ambiguities. When you have to explain someone something, try using templates to break it down. Or when you are starting a new assignment, templatize themes, activities, actionees, ETAs and everything else that you need done and share it with your teams. Even when you are creating something new, design a template to capture the Whys, Hows, Whos and Whens. You'll be surprised to see how people respond to structures.

Myths around thinking with templates

Interestingly, before I started to write this down, I looked up google to find out if there was something already available around this topic. To my surprise, there was none. That got me to thinking, after having deployed templatized-thinking for over a ton(~100) of clients, I have realized the power of this methodology. It classically conditions your automatic brain to think logically, builds your analytical capability and hones your outcome-based outlook towards things. Some of the myths that may cloud the idea of thinking in templates could be:

• When I don't know the topic, how can i know what I am going to look for - Designing a template doesn't restrict the efficiency of your research, it just guides you through the way - To look for relevant data points that could be stringed together once your research is complete. It is a very flexible framework and is at the user's discretion to be modified as per needs
• Templates will not take care of blind spots - There will always be blind spots in calculated research. For a passionate researcher, it is difficult to timebox their research since they are positively reinforced by the problem of plenty, meaning, the more the data, the merrier it is - making it difficult for them to switch-off and get back to the actual synthesis. Templates help researchers emotionally detach from irrelevant and/or excess data in their research and guides them to consider only the ones that can fit into their final deliverables. For everything else that doesn't fit, there's Appendix
• Templates are counter-intuitive to creativity - If anything, templates help creatives bring rhythm to the randomness as they move from Diverge to Converge in the process of design-thinking and building great products.

What are your thoughts around templatized thinking? Have you experienced anything similar at work or otherwise?