Customer Success & Solutions Consulting · Intermediate · ~8 hours
Sales Engineering & Solutions Consulting
Translate customer requirements and technical capability into a useful, credible solution conversation.
Status: Catalogue ready. This lesson version is available in review mode while subject-matter, accessibility and assessment checks are completed.
Discover, map and hand off a solution
Technical discovery without losing the business problem
By the end of this lesson
- Structure technical discovery around business outcomes, workflows, users and constraints.
- Distinguish requirements, preferences, assumptions and questions still to validate.
- Document technical information without requesting unnecessary secrets or production access.
Technical discovery connects two kinds of language
Technical discovery sits between a business problem and the systems that may support a change. If the conversation begins with architecture diagrams only, it can lose the reason the customer is considering a change. If it stays at the level of broad ambition, the team cannot test feasibility. A solutions consultant helps connect the two: what outcome matters, which workflow is involved, what constraints are real and what evidence is needed before recommending a design. Start by restating the business problem. Northstar Analytics may want leadership reporting that is more consistent, but the technical questions depend on how reports are produced, which systems contain the data, who owns each source and how quickly information needs to refresh. Ask what happens today and where the current process breaks down. Then ask which users need to act on the information and what decisions the output should support. Technical discovery should cover people, process, data and systems. People include users, administrators, security reviewers and owners of connected services. Process includes triggers, approvals, exceptions and handoffs. Data includes source, quality, sensitivity, retention and expected volume. Systems include interfaces, authentication, environments, monitoring and support. The questions should be proportionate: a fictional exercise can discuss categories without requesting credentials, real tokens, personal data or production exports. Record what is known and how it was learned. A customer statement, public documentation, approved technical note and consultant assumption are different evidence types. Mark an assumption for validation rather than hiding it in a diagram. This makes later solution review safer and prevents a demonstration from being mistaken for a production commitment.
Requirements have different levels of certainty
A requirement describes something the solution must support or a condition it must meet. “The report must be available before the Monday leadership meeting” is a timing requirement. “Only approved analysts can view the underlying data” is an access requirement. “The interface should feel modern” is a preference that needs clarification. “The customer will provide a clean dataset” may be an assumption or dependency, not a requirement. Use a requirement table with columns for statement, type, source, priority, evidence and open question. Type can be functional, non-functional, security, operational, data or commercial. Priority should be agreed rather than inferred from tone. Evidence can be a customer statement, test result, policy or document. If a requirement is not yet testable, rewrite it into something that can be observed. “Fast” might become “the user receives the first view within an agreed range for the agreed dataset and environment.” Ask what would make the requirement fail. This reveals edge cases and trade-offs. A real-time feed may increase complexity or cost. A strict retention rule may affect reporting history. An integration may work technically but require an approval process the customer cannot complete this quarter. The solutions consultant does not decide the customer's priorities for them; they make the trade-offs visible so the decision can be informed. Close discovery with a validation plan. Which questions need a product specialist, security reviewer or customer administrator? Which can be tested in a safe sandbox? Which are outside the scope of the current conversation? A clear boundary is a mark of professionalism.
Technical trust depends on restraint
Presales technical work often happens before full access exists. That is normal. The responsible response is to design around the information available, identify what must be checked and avoid pretending that a high-level discussion proves implementation feasibility. Use fictional or synthetic data for exercises. Ask for categories and constraints before asking for examples. Follow the customer's security and privacy process when a real evaluation requires deeper information. A technical discovery brief should be readable by an Account Executive, product colleague, implementation team and customer stakeholder. Include the business outcome, current workflow, in-scope systems, stated requirements, assumptions, risks, questions, stakeholders and next action. It should make a later solution map easier to challenge. If a colleague cannot tell what is verified, the brief needs clearer labels. The best discovery conversations create options. Sometimes the right next step is a demonstration. Sometimes it is an architecture review, a proof-of-concept plan, a data-readiness check or a decision not to proceed. The consultant's value is not measured by making every situation look like a fit. It is measured by helping the buyer and internal team understand what is possible, what is uncertain and what should happen next.
Workplace example
Northstar Analytics wants more reliable leadership reporting. The solutions consultant learns that data comes from two systems, access is role-based and a Monday meeting creates the timing constraint. Instead of requesting production exports, the consultant documents the categories of data and proposes a synthetic-data validation workshop.
Try this walkthrough
- 1.Write the business outcome and current workflow before listing technical features.
- 2.Create a requirement table with type, source, priority, evidence and open question.
- 3.Identify two security or data boundaries that should shape the next step.
- 4.Produce a discovery brief that marks assumptions and proposes a safe validation activity.
Watch for these traps
- Starting with an architecture or feature list before understanding the business problem.
- Treating preferences and assumptions as mandatory requirements.
- Requesting credentials, production data or unnecessary personal information for an early discussion.
- Presenting a demo or high-level diagram as proof of implementation feasibility.
Knowledge check
Choose the response you think is strongest. These checks are practice until the course version has passed subject-matter review; graded attempts will be handled server-side.
For the reviewed pilot, answers are checked on the server. This draft result is not yet added to your official learning record.
Practice studio
Make a decision. Test a response. Leave evidence.
Use the case from this lesson to practise judgement without creating a score or changing your course record.
Scenario decision
Northstar Analytics wants more reliable leadership reporting. The solutions consultant learns that data comes from two systems, access is role-based and a Monday meeting creates…
Choose a response
Branching roleplay
A colleague asks you to move this situation forward. What do you say next?
Evidence workspace
Draft a short known / assumed / next note from the fictional case. Use fictional details only.
This is a session-only practice draft. It is not saved, graded or added to your learning record. Never enter real personal or confidential information.
Practical assignment
Create a technical discovery brief for Northstar Analytics with the business outcome, current workflow, eight requirements, four assumptions, two data boundaries and a safe validation next step.
Pass this lesson's knowledge check before recording it as complete.
Portfolio evidence
Practical portfolio project
Prepare a technical discovery brief, solution outline and short demonstration plan for a fictional buyer.
Turn the practical project into a bounded evidence record. Save a draft while you work, then submit it for human review when it is ready.
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