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Dodgy data dulls your senses and leads to poor decision-making. Why? The purpose of your information system is to help you do two things: make sense of what is happening in your environment and support effective decision-making.
[ Listen to audio version, read by David Hodes]
This is part 1 of the series. For more on this, see Part 2 | Part 3 | Part 4 | Part 5
Every transaction and every bit of data gathered from sensors gives a unique record, at any given moment, of the state of play of your enterprise. Sometimes what happens next is automated; sometimes it waits for a person to act, based on a process, a business rule, analysis or intuition.
For illustration, let’s look at a simple slice through an organisation, from CEO to the shop floor:
– The CEO presses an approval button for the establishment of a contract for the expansion project, and the workflow opens the floodgates to the raising of purchase orders.
– Engineering produces bills of material from the computer model of the project and collates them according to when they are due, how long their delivery will take, and then further batch them by vendor.
– Supply , under the umbrella of the contract, takes care of procurement for all the products and services the engineering project team has itemised. Finance keeps score of how much has been spent against what has been budgeted and approved.
– An RFID tag scans across a reader and inventory is recorded as received into a warehouse. The finance system raises an asset on the balance sheet and a matching liability against a vendor.
– The contractor engaged in helping out in the warehouse swipes in and immediately starts counting up the dollars earned for hours worked.
– The project coordinator requisitions a part from the warehouse as the schedule indicates it’s going to be needed the following day.
– The fitter calls over the rigger assigned by the system, and both of them go to fit the item according to the drawings they have from the engineering model. Once done, they scan the barcode on the item which updates the model, along with the finance, maintenance and work management systems.
– Operations turns some dials and flick some switches to start the process, and monitor an array of simulated dials displayed on a monitor.
– A temperature sensor detects that the safe set-point has been exceeded and a signal goes to open a valve to relieve pressure until the system can once again operate within limits.
– The system doesn’t return to a safe operating level and at the same time as the operator shuts it down, a manager tasks a planner with expediting delivery of a spare, as well as assembling an emergency crew to get it fitted.
– All the while, a sensor monitors the production volume and sends the results to the reporting system. Everyone knows the CEO has a particular interest in those production numbers, using them to determine whether or not the business is going to meet its goal.
Now, that’s a pretty simple scenario outlined above. It’s just a thin slice through a hypothetical day in a typical industrial environment. But what’s going on there besides my assertion in the first paragraph? Isn’t it all about sense-making and decision-making?
When all those systems were designed, built, tested and deployed, there was likely at least a business analyst, a developer and a business owner, amongst others, scoping out requirements based on business needs. Someone from the team would have been in charge of the data, determining what fields went in which tables. They’d be thinking about how the master data would be used with the transaction data to make sense of the processes the information system was managing. In the team’s scope were, for example, the process flows, the business rules, security settings, permissions as to who could create, read, update and delete records. You get the picture.
The system goes live, and a catalogue of errors cascades through the organisation:
– The wrong account is attached to the CEO’s press of the button, and all the resulting financial data goes walkabout.
– The engineers were in a rush to get the bills of material out and thought it unimportant to complete the delivery fields diligently, with the result that everything had a lead time of a week.
– The supply team was so busy negotiating the master contracts and raising purchase orders with their vendors that, even though they spotted the error on the lead times, they did what was expedient: increase them universally by a week.
– The materials managers were given a spreadsheet by one of the vendors which had the wrong mapping of the RFID fields. How were they to know they were capturing a code for a pump when the description was for a motor?
– The warehouse contractor swipes in, but the learning management system has him registered as a coded welder. For every hour he works, the system books it out at the stored rate for a coded welder, which is twice the dollars than should be the case. And although the guy works at the top of the warehouse racking, 10m above ground, the system fails to note that his ‘work from heights’ certification needs renewal.
– The schedule has not been updated in a week and the part requisitioned by the coordinator actually should have been there two days prior. An expeditor is sent to the stores to get back in time to be done during the day shift.
– The fitter and rigger wait in the crib until they’re notified that the part has been delivered to the work location. When they get there, they can’t quite believe that a pump has been provided instead of a motor.
– Finally, the right piece of equipment is fitted, and the ramp-up sequence can start. But, the commissioning team used the wrong specs to calibrate the IoT sensors and thus the system had to be shut down, despite them learning later that the instrumentation was giving a false negative.
– The false-negative doesn’t prevent the manager from tasking the planner with expediting the delivery of a spare and assembling an emergency crew to get it fitted. They don’t find out about the perfectly good, but supposedly faulty piece of equipment until it’s too late.
The CEO, meantime, sitting way above this comedy of errors, cannot have any real sense of what is going on in his business. Therefore any decision they makes would be as effective as the throw of a die would be to answering a multiple-choice exam question.
Processes and data seem to have a particular propensity for decay—it’s as if the gods maliciously choose to configure their quotient of entropy such that they cause the most harm.
There are two remedies for the problem.
The first involves leadership around the reason why it is essential to maintain true data. When you consider the enormous amount of effort people spend doing their work, wouldn’t it be the sensible thing to do to ensure that as little of that effort as possible is wasted on correcting the effects of bad data? Can you as a leader clearly articulate the opportunity cost of the wasted time not only on business outcomes, bit also on engagement, motivation and a sense of accomplishment?
The second, which can only be achieved once the first is done, is to commit to such a high level of operating discipline that these effects of false data become increasingly rare and less consequential.
In one of Eli Goldratt’s lesser—read books, The Haystack Syndrome, the fascinating introductory chapter talks about the difference between information and data. Specifically, how to find the needle of information in the haystack of data. Since its publication in 1990, the book’s subtitle, ‘sifting information out of the ocean of data’ has, besides mixing metaphors, become even more relevant in our age of big data and machine learning.
‘Information,’ wrote Goldratt, ‘is the answer to the question asked of the data.’ You have to ask good questions. Crucially, too, the data has to be reliable. From an operational perspective, I’ve found one need only ask about six measures :
• How much throughput is the system generating?
• What operating expense are we consuming to generate that throughput?
• What investment have we made in generating the profit arising from the difference between the throughput and the operating expense?
• How long is what the system’s doing going to take—that is, what’s the lead time or turn-around time?
• How reliable is system performance—that is, what is delivery to promise?
• What quality does the system produce—that is, how much product or service is either rejected or reworked?
Maintaining true data means having a reliable system of record that can provide timely answers to these questions. After all, the purpose of information is to assist in sensemaking within and across the enterprise. Having a reliable system of record ensures that we use the best possible sensemaking information to support effective decision-making.
Understanding and insight come from knowledge. We build knowledge from information. Information is the answer to the question you ask of the data.
If you want better understanding and insight, maintain true data.
This article is part of our series: Five commandments for high-performance execution
Part 1: Maintain True Data
Part 2: Work Fully Kitted
Part 3: Control Work Release
Part 4: Resolve Issues Rapidly
Part 5: Act by Priority
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What’s next?
The change from standard thinking to Theory of Constraints (TOC) is both profound and exhilarating. To make it both fun and memorable, we use a business simulation we call The Right Stuff Workshop.
We’d love to run it with you. To learn more:

[Background image: Playing cards on table, Jack Hamilton on Unsplash]
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Concept validation
A concept can look convincing in a presentation and still make an important task harder. A patient may misunderstand an indicator. A clinician may need information the design does not provide. A promising workflow may depend on support unavailable in the intended setting.
These problems become more expensive when the organization discovers them after committing to architecture, tooling or a launch plan.
We must ask how effectively concepts are validated before significant resources are committed. Patient Centered Design treats this as a learning discipline: turn ideas into testable solutions rapidly using low fidelity prototyping, put them in front of relevant users and gather evidence about whether they improve the outcomes that matter.
“A convincing presentation shows what the team hopes will happen. A useful test reveals what people actually do.”
A prototype without a learning objective can generate interesting comments while leaving the key decision unresolved. Start by identifying what must be true for the concept to succeed.
Does the patient intuitively understand the required action without explanation? Can the professional recognize when the result is ready to use? Does the proposed approach fit the work surrounding the task? Will people take meaningful steps toward adoption?
State the assumption and the evidence needed to assess it. Agree what would support proceeding, what would require revision and what could make the concept unviable. Gather quantitative data along with qualitative verbal feedback.
Prioritize questions with substantial consequences for the investment. An attractive enclosure deserves less attention than an unresolved issue that could prevent safe or practical use of the whole solution.
A picture is worth a thousand words. A simple user experience prototype is worth a thousand pictures. Choose artifacts that maximize learning with the least effort. The appropriate artifact depends on what the team needs to discover.
A sketch may help users explain how they interpret a proposed interaction. A physical mock-up may expose handling difficulties. A simulated interface can support a sequence of tasks. A more complete prototype may be necessary to examine timing, feedback or combined interactions.
Build enough realism to test the assumption accurately. Excessive detail can consume resources without improving the evidence, while insufficient fidelity can conceal the issue being investigated.
Make limitations clear to participants and observers. A manually simulated function may help test an experience, but it does not establish that the technology can deliver that function reliably.
Patients and healthcare professionals can differ in experience, confidence, physical ability, responsibilities, and access to support. Those differences influence whether the concept helps them complete the job.
Define the participants and contexts relevant to the test. Include people whose circumstances could expose difficulties the development team has overlooked.
A familiar expert may work around a confusing interface that challenges a new user. A clinician from a highly supported center may assume resources unavailable elsewhere. Research needs to reveal these dependencies.
Match the study to its purpose. Exploratory sessions can identify problems and guide iteration. They do not automatically establish population-wide performance or replace the evidence required for later design validation.
An Experience Café gives participants a structured opportunity to encounter concepts and talk about their experience. Its value comes from the quality of the scenario, observation and follow-up questions.
Set up situations that reflect what the person is trying to accomplish. Let participants interact with the artifact rather than rely solely on a demonstration by its designer. Build rough and rapid.
Assign roles to facilitators and observers. Agree how comments, actions and difficulties will be captured. Keep the session focused on learning rather than defending the proposed solution. Hold a conversation around the prototype. “Can you show me how you would use this?” “Tell me what you are thinking here.”
Ask participants to explain what they believe is happening and what they would do next. Observe where the concept supports that understanding and where they hesitate, improvise or seek help.
A facilitator can unintentionally make a weak concept appear successful by explaining each step or supplying missing information.
Use neutral prompts and allow participants to reveal their interpretation. When assistance is necessary, record it along with the point at which the person became stuck.
Follow observations with questions about purpose and reasoning. What did the participant expect? Which information did they look for? Why did they choose that action?
Separate a preference from evidence about task performance. “I like this one” may reflect appearance or familiarity. Successful completion, appropriate interpretation and manageable effort provide a stronger basis for assessing whether the concept addresses the need.
“Every explanation the facilitator must supply is a question about what the design expects the user to know.”
The product interaction belongs within a wider job. Test preparation, readiness, execution, monitoring and what follows, wherever these activities affect the concept’s value.
A device may work well once configured while setup introduces avoidable complexity. A result may be easy to view while the professional still needs to transfer it manually before acting.
Include relevant dependencies on caregivers, staff, information and other systems. Investigate whether the concept reduces burden for one participant by increasing it for another.
Use realistic scenarios appropriate to the development stage. Important exceptions and recovery steps deserve attention alongside routine success. An incomplete or interrupted task can reveal weaknesses that a carefully guided demonstration leaves hidden.
Ease of use involves what a person must notice, understand, remember, decide and physically do. A tidy interface can still demand substantial mental effort.
Human performance depends on limited attention, working memory and feedback. Examine how the concept supports those processes during a task.
Does it make the relevant information perceptible? Can the user understand its meaning? Is the next action clear? Can they recognize whether that action succeeded?
Look for unnecessary steps, demands to remember information across screens and alerts that interrupt without helping a decision. The best change may simplify a sequence or improve feedback rather than add another feature. Confirm its effect through observation and appropriate measures.
Choose measures that reflect the priority outcome and the study’s purpose. These may include completion time, errors, requests for help, repeated actions or failure to notice important information.
Subjective workload measures can complement observed performance. NASA Task Load Index can help structure participants’ assessment of workload, while PURE provides an expert assessment of the effort required to complete tasks. Satisfaction ratings on a Likert scale can be used to compare concepts directly with each other and a baseline solution.
Do not treat one measure as a complete account of usability. Faster completion may accompany misunderstanding. A participant may complete the task correctly while reporting effort that makes repeated use impractical.
Interpret results together and examine the conditions under which they occurred. Record the limitations so decision makers understand what the test has established.
Pretotyping examines whether people take meaningful action toward using or adopting an idea before the full solution exists. It addresses a different question from whether a working product can be engineered.
A clearly described concept, service simulation or expression-of-interest process may provide evidence about engagement. The strength of that evidence depends on the action required and the conditions surrounding it.
A click demonstrates less commitment than a substantive implementation conversation. Neither establishes clinical efficacy or successful device use.
For medical concepts, keep representations accurate and make the test status clear. Interpret behavioral evidence alongside clinical appropriateness, access requirements and the responsibilities of the people making adoption decisions. Interest alone cannot establish that the solution should proceed unchanged, particularly when workflows in the clinical setting are involved.
A value proposition explains which customer the concept serves, the job it supports and the difficulties it intends to reduce. It should connect directly with the outcomes identified through research.
Compare that proposition with the evidence from concept tests. Did participants experience the intended benefit? Did another difficulty become more important? Did the concept require assumptions that the intended setting cannot support? Did the concept do something better than a competitor’s current solution?
Capture both positive and negative findings. Use them to revise the concept, the proposition or the team’s understanding of the need.
Then test the important changes. Iteration should answer a question left open by the previous round. Repeating sessions without changing the artifact or research objective can accumulate comments while leaving uncertainty untouched.
Early concept validation supports decisions about direction and investment. It does not establish all the evidence needed for a finished medical device.
Technical performance, reliability, clinical benefit and intended-use safety require appropriate subsequent work. A convincing simulated experience may depend on functions the team has not yet demonstrated.
Make these boundaries visible in the decision record. State who participated, what was tested, the conditions, findings and unresolved issues. Explain how those findings affect the next commitment.
Overall, having a “winning concept” in hand, backed by user Experience Café data along with user needs in the form of outcome statements formally structured with a direction of improvement and unit of measure, sets a development team up for success. The outcome statement user needs provide a solid foundation for the design history file.
Rapid prototype experience testing gives the team answers to key decisions and sets a target to pursue early on. This results in faster development cycles, more robust regulatory submissions, and better returns on investment spending.
A Value Management Office can connect this learning with resources and stage decisions. Testing should help the organization commit at a pace justified by evidence and concentrate effort on the uncertainties that govern progress.
“Early validation creates value when it changes the concept or the commitment before an unresolved assumption becomes expensive.”
Examine one proposed investment. Which assumption could defeat the concept, and what is the least costly credible way to test it?
Prepare an appropriate artifact, involve representative users and observe meaningful tasks. Review the evidence, revise the concept and record the next learning decision. This gives Patient Centered Design a practical route toward solutions people can successfully use and organizations can responsibly develop.
A development team can become committed to a concept before it has seriously considered an alternative. The idea is familiar, a senior sponsor favors it or an available technology makes it attractive. Detailed engineering begins while the reason for choosing that approach remains largely untested.
Concept engineering provides a more disciplined basis for commitment. It develops several viable approaches, compares them against important needs and combines their strongest elements where doing so improves the solution.
Within Patient Centered Design, the purpose is to identify a concept that can help patients and healthcare professionals accomplish their jobs successfully. Question 2.1.1 examines the effectiveness of that work.
“A winning concept earns its place by addressing important needs and surviving comparison with credible alternatives.”
Concept work begins with a concise problem statement and an agreed objective. Both should describe the progress the intended users need, rather than assume the answer.
An illustrative problem might concern a clinician’s difficulty identifying whether the information needed for a treatment decision is complete. “Build a new dashboard” commits to one response before exploring the problem.
Clarify the users, operating context and desired outcome. Review the patient journey and the clinical work surrounding the interaction. Establish which unmet needs the concept must address.
Resolve major differences in interpretation before the selection exercise. A mechanical engineer, clinician and commercial leader can otherwise compare ideas while quietly applying different definitions of success. Shared language makes their conversation more useful.
Concept selection needs criteria grounded in research and requirements. Include the outcomes patients and professionals consider important, particularly those existing approaches satisfy poorly.
Then identify necessary capabilities and constraints. Relevant considerations may include safety, reliability, usability, manufacturing, service requirements, implementation and the resources available to develop the solution.
Separate mandatory conditions from characteristics that permit tradeoffs. A concept that cannot satisfy a necessary safety condition needs resolution or rejection; attractive performance elsewhere cannot compensate for that failure.
Define the criteria clearly enough for consistent comparison. “Easy to use” leaves the team guessing. The number of required actions, time to complete an activity or ability to recognize readiness can provide more concrete grounds for conversation, depending on the need.
Concept engineering encourages broad idea generation, with constraints applied more deliberately during selection. This gives the team room to consider alternatives before familiar solutions dominate.
Use sketches and short descriptions to make ideas visible. Invite different functions to contribute approaches, concerns and connections the team might otherwise miss.
The concepts should represent meaningful alternatives. Several versions of the same preferred design may leave the underlying assumptions untouched.
Explore how the required function could be achieved differently. Could information arrive earlier? Could preparation become simpler? Could a service change remove work the proposed product was intended to automate?
A concept should communicate its operating principle, important interactions and dependencies. A simple drawing can expose differences that remain hidden in a verbal description.
Explain what the patient or professional would do, what the system would provide and what supporting resources would be required. Include the activities before and after the visible product interaction.
Build enough detail to support the decision at hand. Early concept selection does not require complete engineering specifications, but an appealing image alone gives little basis for comparison.
Record what is known and what is assumed. If one idea appears stronger because its description omits difficult tasks, the matrix will reward an incomplete account of the solution. Consistent descriptions support a fairer evaluation.
The Pugh matrix provides a structured way to compare concepts. List the criteria as rows and the candidate concepts as columns. Choose a reference concept, known as the datum, against which alternatives will be assessed.
For each criterion, judge whether a concept is better than, similar to or worse than the datum. The simple notation is positive, same or negative.
Discuss the basis for each rating. Record supporting evidence and unresolved questions. The matrix makes differences visible and gives the team a shared place to examine them.
Totals can help identify promising candidates, but they should not make the decision automatically. The significance of a weakness depends on the criterion involved. Several small advantages may leave a concept unable to address its most important requirement.
“The matrix is useful when it exposes the reasoning behind a choice and the evidence still needed to defend it.”
Early conversations often rely on incomplete information. Simple comparisons can be more useful than elaborate scores that suggest a level of precision the team does not possess.
Weighted methods may help when criteria genuinely differ in importance. Paired comparisons can support more structured judgments about relative priority. When measured engineering performance is available, use those data directly in the evaluation.
Choose the method for the decision and evidence, rather than adding complexity by default. Explain how weights or ratings were established and which assumptions could change the result.
Check whether the leading concept remains attractive under reasonable changes to uncertain judgments. A decision that reverses after a minor rating adjustment deserves closer examination before the organization makes a substantial commitment.
Hybridization is a central part of concept engineering. A concept with several strengths may also have a weakness another option handles well.
Examine whether the useful elements can be combined into a stronger approach. For an illustrative monitoring solution, one concept may support straightforward preparation while another provides clearer feedback when a connection fails.
A hybrid might preserve both benefits, but it needs its own evaluation. Combining features can introduce new interfaces, added complexity or conflicting operating requirements.
Redraw the combined concept and reassess it against the criteria. Investigate whether it actually removes the weakness or merely moves the burden elsewhere. The aim is a coherent design whose interactions work together, rather than an accumulation of everyone’s favorite features.
Concept selection brings technical judgment, professional experience and organizational interests into the same conversation. The quality of that conversation affects the outcome.
Agree on meeting ground rules and use a team with relevant expertise across functions. Ensure that a strong personality or senior title does not become a substitute for evidence.
When disagreement cannot be resolved, capture the question and define an action. Assign an owner, the evidence required and a completion date. An independent facilitator can help when team dynamics obstruct useful comparison.
The method can make judgments more transparent, but it does not eliminate subjectivity. Leaders should expect the team to explain its reasoning and show how important uncertainties will be resolved.
The leading idea may depend on uncertain technical performance, user behavior, supplier capability or implementation conditions that significantly affect time to market. Identify those dependencies before detailed development absorbs significant resources.
Plan focused experiments, research or prototypes to address them. Match each activity to a question that could change the selection decision.
If a concept depends on clinicians adopting a new preparation routine, a technical bench test will not establish that assumption. If it depends on a material performing under particular conditions, user enthusiasm will not resolve the engineering question.
Prioritize uncertainties with serious consequences for the concept’s viability. Early learning should help the team avoid expensive redesign while preserving options until the relevant evidence is available.
At this point in the design process, using patient and healthcare professional journey mapping satisfies the Empathy stage of Design Thinking. Understanding the jobs to be done and desired outcomes satisfies the Define stage of Design Thinking. Concept engineering at its core is a practice that ensures the best solution direction is chosen before significant expense is incurred and satisfies the Ideate stage of Design Thinking.
With a selection of “winning concepts” in your pocket, complete the Design Thinking stages by rapidly prototyping and testing solutions with customers to gather data on which concepts your customers desire most. The Design Thinking stages provide an explanation of why a solution is preferred and a record of why alternatives were not chosen.
Retain the criteria, important comparisons, hybridization decisions and unresolved risks. This record helps future teams understand the consequences of a proposed change.
Selection establishes a direction for further learning and development. It does not prove that the solution meets user needs or performs safely in practice. Concept validation and subsequent engineering evidence must support those conclusions.
Keep the relationship with the original needs visible. A chosen architecture can gradually lose important benefits as specifications and implementation choices evolve. Traceability helps the team notice when a tradeoff undermines the reason the concept was selected.
The Voice of Business and Voice of Technology should inform selection alongside customer evidence. A concept needs a realistic development route and an operating environment capable of supporting it.
A Value Management Office can connect these choices with resources and portfolio priorities. Systems thinking helps the team examine whether the proposed improvement addresses a constraint governing patient access or successful use.
Evaluate the complete contribution. A technically impressive device can introduce training, service or clinical work that limits its benefit in the wider pathway.
“Concept engineering should leave the organization with a reasoned choice, a manageable learning plan and a clear connection to patient benefit.”
Review one current concept decision. Can the team show credible alternatives, agreed criteria, the basis for selection and the assumptions requiring further evidence?
Bring the relevant disciplines together, compare options and examine stronger combinations. Then test the uncertainties that could change the decision. That is how Patient Centered Design builds a defensible route from important needs to better products.
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