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Research.Design.Impact.

Innovation: Why Starting Points Matter

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If you’re looking to measure some form of progress or impact connected to your innovation (a product, service, or policy) then paying attention to the starting point is critical.

Evaluators call this a baseline and it’s maybe the most important line you can draw. A baseline is really the point of comparison for all you do. When speaking about improvements or change, this is the point you refer to when making those claims.

For something so important, it’s remarkable how few organizations capture baselines well. Let’s look at what it means and how you can do a better job of determining your innovation’s baseline.

Setting a baseline

An ideal baseline is set as far back from the present as possible at the start of your innovation journey. However, as many journeys have starts, stops, and tangents it might be that the start of the innovation journey actually ‘begins’ mid-way through a timeline.

If you are already started your innovation journey, the best time to set a baseline is now. It’s possible in some cases to use retrospective data (looking backward) to assess a baseline, however that can be fraught with certain biases that are unhelpful. If looking retrospectively, consider neutral data points like dates and times, concrete descriptions of product work, and use verifiable sources of data (e.g., work activities, prototypes, expenditures) to support that work.

When setting a baseline, there are some other tips we advise to enable you to capture the most possible useful data you can. If you are innovating in a human system, it’s possible that the innovation may have many effects that go beyond the most obvious so collecting the right data to capture these effects at the beginning is key.

  • List out the resources that have been assembled to develop the innovation such as people, space, and other capital (e.g. funds). These are your starting inputs into the project.
  • Gather a project plan or schedule of activities early to help determine what happens after the project begins. This will help determine where deviations from the plan take place, when, and help you trace back what happens if or when those changes take place to the strategy. Capturing deviations is critical because it helps you go back to see what adaptations you make at the end. Without this data, these activities might appear to be random or haphazard.
  • Capture cultural/environmental factors. Using the STEEP-V (Social, Technological, Economic, Environmental, Political, and Values) model is helpful in knowing what to pay attention to. One of our clients experienced a major, unexpected removal of funding due to rapidly changing political priorities of a government that was supporting their work. By capturing these broader situational variables you can place your innovation work in a context.
  • Document the state of your organization’s readiness and preparedness, which may also include an assessment of innovation readiness. Many innovations fail not out in the market, but within the design studio. Changes to organizational priorities, resources, and personnel can scupper, delay, or change the plans for an innovation. Capturing the state of the organization is an important point as it will allow you to see where things go off track or where they are enabled because of the organization.
  • Develop a project charter and theory of change. While a project may change direction many times, a baseline assessment can help you reflect the desired outcomes and original purpose of the innovation — which are quite likely to change over time. Having this in place can help explain what changes take place and what adaptations take place.

Baselines are the key point for making any claims of change, improvement, or transformation. They are the point where we say “in relation to what?” when speaking about change.

Give yourself some time and use the baseline assessment as a chance to spur reflective and strategic planning about your innovation. You will be grateful you did and amazed at the results later on.

If you’re interested in learning more about baseline development and its role in supporting innovation evaluation, contact us and we’ll gladly help.

Photo by Suad Kamardeen on Unsplash

The Amazing Spidergram

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Illustrating action points within a complex system challenges evaluation users. Time to call on your friendly neighbourhood spidergram for help.

Visualizing complex systems is a challenge within strategy, foresight, and evaluation because each component of the system is interconnected with others. Influence on one of these is likely to influence others.

From an action standpoint, it’s easier to focus on one or two small parts of the system than tackle the entire system all at once. How do we reconcile this and provide a means to see the parts of the system without being reductionistic and neglecting the relationship with the whole?

One answer is: look to the spiderweb.

A spiderweb is a good entry-level metaphor for helping people see places they can take action within a system by creating distinctions between the parts (nodes, intersections) and the whole (branches, webs). There are two related, but different spiderweb models worth noting.

Spider Diagrams / Mindmaps

Spider diagrams (or spidergrams or mindmaps) are ways to connect ideas together through the branch-and-thread model akin to a spider’s web (hence the name). These are often called mindmaps and have been shown to facilitate learning about complex topics.

They enable the development of relationships between ideas and possible causal or associative pathways between ideas, concepts, or other data- or evidence-informed concepts. They also enable us to cluster related concepts together to identify sub-systems that may be more amenable to our intervention within the larger whole.

Spidergrams / Radar Diagrams

The other spider-related metaphor that is available to innovators and evaluators is the spidergram, sometimes called a Radar Chart or Spider Chart. These allow for the display of data collected along a scale presented alongside others that use a similar proportioned scale.

This hub-and-spoke model of data allows users to see a variety of performance indicators presented along a similar set of axes related to a common goal.

What this allows for is a view of performance across a variety of metrics simultaneously and can recognize how we make progress on one area often at the expense of another. Strategically, it can enable an organization to balance its actions and foci across a variety of key indicators at the same time.

This can be used with quantitative data such as the financial data above or social data, too.

Spidergrams/charts can also overlay data within the same domain (see example) providing even more depth into recurring or separated data points within the same topic or subsystem.

The web of engagement

What makes these tools powerful is that they display a lot of data at the same time in a manner that can facilitate engagement with a group of people tasked with making decisions. Visualizing data or systems brings the benefit of literally getting people’s focus on the same page.

People around a table can then literally point to the areas they are interested in, concerned with, do not understand, or wish to explore assumptions about.

Complex systems introduce a lot of data and a lot of confusion. Sensemaking through the use of visuals and the discussion that they encourage is one of the ways to reduce confusion and get more from your data to make better decisions.

If you’re stuck in the web of data and complexity, call on your friendly neighbourhood spidergram.

Want to learn more about how this can assist your innovation efforts? Contact us and we’ll gladly swing over and help (without the costume).

Photo by Jean-Philippe Delberghe on Unsplash. Spidergram by

Evaluation’s Hidden Value: Clarifying Your Strategy

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Evaluation is more than just the assessment of merit, worth, and significance of a program, product, or service. Although these are the primary functions of evaluation, it can serve far more than this when designed and implemented in the appropriate manner.

Evaluation focuses attention on the things that your organization builds, has built, and implements and the purpose and role(s) they serve. In doing so, evaluation serves as a primary support tool for strategy. In order for an evaluation to do its job, it must be aligned with the purpose of the thing it is evaluating.

For example, if an evaluation is being asked to assess behaviour change, the focus of the program or stimuli (e.g., app, message, etc..) for that change is a core part of the strategy. By matching the program components with the activities and intended outcomes, evaluators can help determine the degree of alignment and assess (and advise on) the likelihood of success before any data is collected. It is through the data — the evaluation itself — that this alignment and the assumptions behind it are tested.

Theory into practice

The process of aligning the planned activities with the outcomes is part of what a Theory of Change is designed to do. To illustrate, consider a program designed to promote some kind of behaviour change (e.g. engaging in a new activity, doing more or less of something, etc..). For a change to take place, there needs to be a certain logic in how the intervention (i.e., the activity or service) is set up based on some established design principles.

We also know from the wealth of knowledge of behavioural science, that there are many different ways to promote change and that some are more efficacious than others. A skilled evaluator can help determine whether the approach being used is likely to produce the desired change by building out a Theory of Change and then matching that with the appropriate outcomes.

This is what the heart of a strategy is all about: aligning the resources, intentions, and actions together to produce an outcome.

Like the image above, evaluation helps peel back the top coat to reveal what is happening underneath. It helps take the mystery out of why programs work or don’t work and what the reasons are why people do things (or don’t).

It’s one of the reasons why we bring strategy, evaluation, and design work together. By connecting them, you get far more performance out of your program than if you just keep the cover on.

To learn more, contact us and we’ll be happy to show you what’s under your hood.

Image credit: Nathan Van Egmond

Developmental Evaluation Trap #4: Organizational culture

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In this latest post in a series on Developmental Evaluation (DE) and its traps, we look at the innovator’s trap of culture. In a quote widely attributed to Peter Drucker

Culture eats strategy for lunch

Culture is basically “the way things are done around here”, which includes the policies, practices, and people that make what happens, happen. Culture can be a trap when we fail to prepare for the change in how we do things that comes with adopting a developmental approach.

Just as we might fear success (discussed in an earlier post), we may also not prepare (or tolerate) it when it comes. Success with one goal means having to set new goals. It changes the goal posts. It also means that one needs to reframe what success means going ahead.

Many successful sports teams face the problem of reframing their mission after winning a championship: what it takes to win the first time is different when the motivation, context, and opportunities shift because of that victory. The same thing is true for organizations.

Building a culture of innovation

We’re not naïve enough to think that we can simply offer you a few bullet points and expect you to change your culture overnight. It is a big, important job. However, there is much research on behaviour change and organizational development that we’ve drawn on to help get you started.

  1. Manage deadlines differently. Deadlines drive much of our organizational behaviour and strategy, yet research has shown that deadline-driven work is less satisfying, that people’s decision-making gets impaired under the stress of urgency, and that priority isn’t always given to the more important tasks. Consider developmental milestones, which are similar to what we use in other areas of life. It’s a mindset that looks at tasks, accomplishments, and expectations in an evolving context with adaptive targets.
  2. Planning your stops as well as your starts. Developmental Evaluation can not only help you build out your innovation, but it can also determine what needs to stop. This means making hard choices about what kind of activities you might continue, what needs to be stopped, and what new things you wish to build out. The Tamarack Stop Start Continue tool provides a practical means to guide an organization through the process of making these strategic decisions. Deciding what you will stop is just as important as creating innovations.
  3. Measure performance compassionately. New research has emerged to show that intensive performance measurement can actually hamper the performance of an organization. Collecting too much data or making it onerous to do, not disclosing the purposes of the data, not using data collected, failing to engage those involved in the program in the design of the data collections, or neglecting to collect context-rich data that can help understand the mediators and moderators of performance are ways to alienate people from evaluation and limit its use. A strong DE approach seeks to engage users, making for more effective evaluation overall.
  4. Give data back. Show those involved in the program and the evaluations associated with it what the data reveals and what decisions are made from it. By making evaluation data transparent, you build trust, but also a culture of innovation by showing the purpose of the data and the ways in which it is used to make decisions. This kind of accounting to your users and stakeholders is part of the culture development for innovation.

By getting the right data, using it respectfully, and making it visible are ways in which the culture of an organization can change through developmental evaluation and lead to the kind of system shifts that allow great ideas to emerge and flourish.

Interested in learning more about what DE is and how implementing evaluation in this manner can support your innovation efforts? Contact us to find out more.

Developmental Evaluation Trap #2: The Pivot Problem

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In this third in a series on Developmental Evaluation traps, we look at the trap of the pivot. You’ve probably heard someone talk about innovation and ‘pivoting’ or changing the plan’s direction.

The term pivot comes from the Lean Startup methodology and is often found in Agile and other product development systems that rely on short-burst, iterative cycles that include data collection processes used for rapid decision-making. A pivot is a change of direction based on feedback. Collect the data, see the results, and if the results don’t yield what you want, make a change and adapt. That sounds pretty familiar to those looking to innovate, so where’s the trap?

The trap is that the decisions made aren’t grounded in data or the decision-making is flawed in how it uses data. In both cases, the decision to change direction is more arbitrary than evidence-based.

What do we mean by this?

The data problem

When innovating, it’s important to have the kind of data collection system in place to gather the necessary information required to make a useful decision such as whether to continue on with the process, make a change, or abandon the activity. James Dyson famously trialed his products hundreds of times with ‘tweaks’ both large and small to get to the right product design. A hallmark feature of this process is the emphasis on collecting the right data at the right time (which they call testing).

Dyson has since expanded its product offerings to include lighting, personal hair products, industrial drying tools, and an array of vacuum models. While the data needs for each product might differ, the implementation of a design-driven strategy that incorporates data throughout the decision-making process remains the same. It’s different data used for the right purpose.

Alas, DE has given cover to organizations for making arbitrary decisions based on the idea of pivoting when they really haven’t executed well or given things enough time to determine if a change of direction is warranted. Here are three things that one needs to heed when considering DE data.

  1. Process data. Without a clear indication that a program has been implemented appropriately and the constraints accounted for, how do you know that something ‘failed’ (or ‘succeeded’) based on what you did? Understanding what happened, under what conditions, and documenting the implementation behind the innovation is critical to knowing whether that innovation is really doing what we expect it to (and ensuring we capture the things we might not have expected it to do).
  2. Organizational mindfulness. The biggest challenge might be internal to the organization’s heart: it’s mindset. Organizational mindfulness is about paying attention to the activities, motivations, actions, and intentions of the entire enterprise and being willing (and able) to spot biases, identify blind spots, and recognize that, as much as we say we want to change and innovate, the reality is that it is disruptive for many people and something often unconsciously thwarted.
  3. Evaluability assessment. A real challenge with innovation work is knowing whether you’ve applied the right ‘dosage’ and given it the right amount of time to work. This means doing your homework, paying attention, and having patience. Homework comes in the form of doing background research to other, similar innovations, connecting to the wisdom of the innovators themselves (i.e., draw on experience), and connecting it together. Paying attention ensures you have a plausible means to connect intervention to effect (or product to outcome). This is like Kenny Rogers’ Gambler:

you got to know when to hold ’em

know when to fold ’em

know when to walk away

know when to run

An evaluability assessment can help spot the problems with your innovation data early by determining whether your program is ready to be evaluated in the first place and what methods might be best suited to determining its value.

The decision-making problem

Sometimes you have good data, but do you have the decision-making capabilities to act on it? With innovation, data rarely tells a straightforward story: it requires sensemaking. Sensemaking requires time and a socialization of the content to determine the value and meaning of data within the context it’s being used.

Decision-making can be impeded by a few things:

  1. Time. Straight up: if you don’t give this time and focus, no amount of good data, visualizations, summaries, or quotes will help you. Time to substantially reflect on what you’re doing, discuss it with those for whom it makes sense.
  2. Talent. Diverse perspectives around the table is an important part of sensemaking, but also some expertise in the process of decision-making and implementing decisions — particularly in design. An outside consultant can assist you in working with your data to see possibilities and navigate through blindspots in the process as well as be that support for your team in making important, sometimes difficult, decisions.
  3. Will. You can give time, have talent, but are you willing to make the most of them both? For the reasons raised above about being mindful of your intentions and biases, having the right people in place will not help if you’re unwilling to change what you do, follow when led, and lead when asked.

Developmental evaluation is powerful, it’s useful, but it is not often easy (although it can be enormously worthwhile). Like most of the important things in life, you get what you put into something. Put a little energy into DE and be mindful of the traps, and you can make this approach to evaluation be your key to innovation success.

Want to learn more about how to do DE or how to bring it to your innovation efforts? Contact us and we’d be happy to help.