Open Source pilot without a parallel zoo in 4–8 weeks

What the “parallel zoo” problem is and why you need a pilot

The “parallel zoo” appears when a new Open Source solution is started alongside the old one without clear rules. Some teams move to the new tool, some stay with the old, and some work in both. As a result, the company ends up with two sets of processes, two places where data lives, and two answers to “what is the correct way”. This quickly turns into confusion, disputes and extra costs.

The issue is usually not the pilot itself but treating it as a mini second life of the system. Teams create separate chats, separate folders, separate requests, and then try to glue everything back together. The longer this goes on, the harder it is to return to a single standard.

A pilot is not meant to be “set and forget”. It should quickly answer practical questions: how reliable the solution is for your scenarios (load, failures, recovery), whether people can comfortably do daily tasks in real work, and whether support can operate it without heroics (updates, permissions, incidents).

Decide in advance which decisions cannot be made during the pilot, without data. In the first days do not announce “everyone switches”, buy hardware for scale, rewrite all procedures, or permanently turn off the old service. A 4–8 week pilot is good because it produces facts: what really works, where things break, what users accept and what they sabotage. After that the choice is honest: scale, refine the approach or stop.

Pilot goal and success criteria that don’t contradict themselves

The pilot goal should fit in one sentence and answer: what will be clear in 8 weeks. For example: “The Open Source pilot should show whether the chosen stack can cover the key work scenario without a drop in service quality and without increasing support load.”

Assign a pilot owner immediately. Usually that’s a business sponsor (the leader of the area with the biggest pain) together with an IT owner responsible for operations. The final scaling decision is best held by a small committee: the process owner, head of IT/InfoSec and a financial controller. That way you won’t argue about the importance of criteria on the last day.

Record success criteria before start and make them measurable. A convenient formulation: “success if 4 of 5 are met, and 2 items are mandatory.”

Typically five groups of criteria are enough: availability and stability (agreed SLA and number of critical incidents), support speed (response and resolution times), user satisfaction (a short NPS or a 1–5 rating after request closure), effort (hours spent on administration, updates and incident handling), and performance (response time for typical operations and peak load).

Two common mandatory items that keep things sober: compliance with security and regulatory requirements (no unauthorized access, logging works) and a clear order-of-magnitude for total cost of ownership (at least rough numbers for scaling).

Document risks and constraints on one page: which data can be used (anonymized or production), who and how gets access, which integrations are forbidden, maximum users and duration, and what counts as a stop signal (for example, a leak, critical outage, or policy violation on data retention).

How to choose scope: users, processes and constraints

Scope decides whether this is an idea check or a second prod. For an Open Source pilot go narrow: 2–4 user teams and 1–2 processes where effect is visible in 4–8 weeks.

Start by choosing teams. It’s good when they differ in work style but are not tied to critical 24/7 operations. For example: accounting (strict procedures), sales (lots of communications) and IT support (frequent requests). This helps you see where the solution helps and where it hinders.

A simple checklist helps select teams and processes: a process owner and decision-maker exist; the effect is measurable (approval time, number of requests, license cost); there is a change window and the ability to train users; rollback is possible without data loss or service interruption.

Next pick processes. Choose those that give quick, clear results: collaborative documents, ticketing, knowledge base, internal chat, monitoring. The main rule: every service must have an owner and a predefined answer to “what happens after the pilot” (keep, replace or turn off).

Do not include core systems that keep the business running if there is no change window. Usually this means ERP, payroll, payment gateways, medical systems and anything that must be up 24/7 without downtime.

To avoid creating a second production, limit the boundaries up front: one environment, one set of integrations, one sign-in method, clear timelines. Use anonymized or minimal-copy data and grant permissions by the principle of least privilege. Then the pilot tests the solution rather than becoming a “parallel zoo.”

Which services to put in the test contour to avoid duplicates

A simple guiding principle: one function — one tool. This is especially important where people work daily and quickly form habits: tickets, access requests, notifications, monitoring. If you give two equivalent options in these areas, teams will split by habit and later you’ll need to merge histories and rules.

For an Open Source pilot a small set of services is usually enough to demonstrate value without proliferating processes: monitoring and alerts, log collection and search, ticketing, knowledge base and access management for the test contour.

To decide whether a new service duplicates or complements the existing one ask two questions. First: will the team be required to maintain data in two places (two ticket queues, two dashboards, two user lists)? If yes, it’s a duplicate. Second: does the new tool provide a unique capability that the current one lacks and not require parallel maintenance? Then it’s a complement.

Fix an “exit rule” immediately, otherwise the pilot will leave tails. A working frame: one tool is designated primary for the chosen function; the old one remains only during migration and for archive; the old system shutdown date is known in advance; data and access transfer have an owner; if pilot criteria aren’t met, the new tool is turned off rather than kept “just in case.”

Test contour: isolation, access and data handling

To prevent the pilot from turning into a “parallel zoo”, the test contour must be separated by network, accounts and data. In the pilot you test approach and usability without risking production or creating a second life for temporary solutions.

Isolation and access

A minimal set that typically covers main risks:

• separate network segment (or VLAN) and distinct DNS names for the pilot
• separate accounts and roles, without default admin rights
• one clear entry point (VPN/bastion/SSO) to avoid ad-hoc exceptions
• no direct integrations with critical systems until rules and audit are ready
• separate secrets and keys, do not copy production tokens

Working with data: what is allowed and what is not

Start with synthetic data or trimmed replicas. If real data is unavoidable, use anonymization: remove names, national IDs, phones, addresses, contract numbers. Define sensitive fields in advance and who signs off on access.

Security, logging and changes

Even in a pilot you need basic logs: sign-ins, admin actions, service errors, configuration changes. Assign a contour owner (often a platform engineer) and a security responsible. Record every change briefly: what changed, why, who did it and how to roll back. That keeps the pilot manageable and comparable to production requirements.

Pilot timeline for 4–8 weeks: a week-by-week plan

A good Open Source pilot fits into 4–8 weeks if boundaries are agreed in advance: which services to test, who participates and what facts drive the decision. Below is a scheme that prevents the pilot from becoming an endless experiment.

Weekly plan

• Week 0–1: preparation. Confirm services for the test contour, choose owners, set the role matrix (admins, support, users), describe usage scenarios and success criteria.
• Week 2–3: deployment. Bring up the environment, configure access (SSO/LDAP if available), email and notifications, backups, onboard the first 10–30 users and deliver a short onboarding.
• Week 4–6: operation. Run as in production: accept incidents, maintain a problem log, update instructions, train new users, and collect metrics on stability and real usage.
• Week 7–8: analysis and decision. Compile data, compare to criteria, prepare scaling or rollback plan, and fix budget and resources (team time, support, hardware).

Do not add new services mid-pilot without a separate decision. Otherwise you test not a product but an endless set of variants.

Artifacts that should appear

By the end of each phase there should be simple documents and facts to show leadership: pilot passport (goal, scope, risks, success criteria, timeline), role matrix and support contacts, short user and admin instructions (1–2 pages), incident and metrics report (what broke, how quickly it was fixed, blockers) and the final decision (scale or rollback) with a step plan and dates.

Example: in a medium organization in Kazakhstan you can start with one daily-used service (internal portal or file storage), connect one department and measure not only availability but how many tasks were completed without reverting to the old solution.

How to collect user feedback and not drown in it

If feedback is collected “ad hoc” it quickly becomes noise: many emotions, few actions. Agree on simple channels and rules in advance so the pilot produces clear conclusions.

Choose 2–3 channels that are convenient and one official recording point. Usually a short weekly survey (3–5 questions), several 10–15 minute interviews with representatives of different roles and a single request channel that shows frequency and topics are enough. If you use a problem/idea form, require fields “what did you do” and “what did you expect”.

To get actions rather than emotions, ask behavior-focused questions and about blockers: “At which step did you stop?”, “How long did the task take compared to the old way?”, “What did you do instead of using the new service?”, “What is needed for you to use this tomorrow without help?”.

It’s important to classify messages consistently, otherwise discussions will be endless. A practical scheme: bug (broken, needs fix and deadline), training (function exists but people don’t know how, needs a guide), improvement (works but is inconvenient, planned later), “not suitable” (business requirement is not met even with configuration).

Rhythm is simple: one responsible person (product or pilot PM) triages incoming items daily and tags them; weekly they show a summary to the process owner and IT. If you don’t show the summary regularly, issues accumulate and trust drops.

Record decisions in a short journal: date, question, decision, reason, who approved. Then you won’t argue “why we did this” at the pilot’s end.

Metrics and observability: what to measure from day one

If you don’t embed measurements into the pilot, the result will be based on feelings. Agree on a simple pilot dashboard of 10–15 indicators that update weekly and are visible to all participants.

Pilot dashboard: 5 indicator blocks

• Operational: availability (uptime), number of incidents, mean time to recovery (MTTR), share of noisy alerts, response time to critical alerts.
• Change reliability: release frequency, percentage of failed updates, average rollback time, downtime caused by releases.
• Team load: support hours per week, number of manual operations (e.g. account creation, granting access), average time per typical request.
• User outcomes: time to complete a key task (before/after), share of successful scenarios without support, number of recurring user problems.
• Pilot-level finances: human costs (engineer and support hours), infrastructure resources (CPU, RAM, disk, licenses if needed), estimated cost of downtime for the business.

Record not only what broke but why. For each incident note the cause, affected scenarios and actions that will prevent recurrence.

If users say “it’s slow”, don’t argue. Compare median task time and the 95th percentile, then check whether latency increases align with load, releases or request queues.

If the dashboard shows clearer alerts, falling MTTR, fewer manual actions and growth in successful scenarios, that’s a strong signal to scale.

When to decide on scaling and how to document it

Make the decision on a prearranged date — for example week 6 or 8. By then you should have data on success criteria, risks and support effort. If you delay, the pilot quietly becomes a permanent “temporary” contour.

When “yes” is justified

Say “yes” when thresholds are met and you know how to operate the solution:

• key scenarios run without workarounds or manual patches
• most pilot users are ready to switch, complaints are isolated and repeatable
• support fits into a clear runbook (incidents, updates, access)
• security and data risks are closed or there’s a clear remediation plan
• total cost of ownership and rollout timeline are clear and preferable to alternatives

When it’s better to say “no”

Saying “no” is better than scaling into a worse state. Warning signs: many exceptions “only for department X”, inability to update without downtime, dependence on a single hero admin, constant performance complaints, or the need to maintain two equal systems without a shutdown plan.

A “yes, but” option works if problems are limited and fixable. Document the fixes required before expansion: what to do, who owns it, deadline, readiness criterion. Scaling does not start until the list is closed.

A rollback plan is always required. At minimum: how to restore access in the old system, how to export and re-import data, and which system is the final source of truth on rollback day.

Summarize the pilot on one page for leadership: goal and scope, results by criteria, risks and their cost, decision (yes/no/yes-but) and the next steps with dates.

Typical pilot mistakes that create a “zoo”

The main reason for a parallel zoo is simple: the pilot is made into a showcase of dozens of tools instead of a check of one clear scenario. This creates duplicates, conflicting settings and endless debates about what to keep.

The same mistakes often lead to the zoo. Teams start with a set of platforms “just in case” and then compare UIs and features instead of solving a business problem. No service owner is assigned: nobody decides, collects requests or approves changes. Pilot and production are confused and iterations are feared, even though the pilot’s value is in quick tries and rollbacks. Security is relaxed: broad access is granted, audit is disabled, production data is copied without rules, and debts remain. Training is forgotten, so basic difficulties are wrongly attributed to a “bad product.”

Small example: if you set up a test contour on a dedicated server but give access to everyone and leave the old service without rules, you’ll have two sources of truth and two support queues.

To keep the pilot from growing, fix constraints up front: one scenario, one toolset, one responsible person; a clear list of pilot users and participation period; data handling rules; one request channel and a weekly decision; short training of 30–45 minutes and a 5-action cheat sheet. Then the pilot stays a controlled experiment, not a dump of “tried-and-abandoned” tools.

Short checklists before start and before the final decision

Spending an hour or two to set ground rules before launch saves weeks of disputes and reduces the risk of a “parallel zoo” where new services live alongside the old without a plan.

Pre-start checklist

First confirm scope: which teams participate, which processes are affected, pilot duration (4–8 weeks) and constraints (security, regulation, budget, change windows).

Then fix service boundaries: what exactly is being tested and what is forbidden to add during the pilot. For example: agree on “one collaboration service and one task service” and no extra chats.

Check the test contour: it must be isolated, with clear access and data sources. Decide which data can be used (real, anonymized, test) and who approves access.

Also prepare the feedback collection plan (how, when and who reviews) and the list of metrics with owners. If a metric has no owner it usually “dies” in week two.

Pre-decision checklist

At the pilot end compare facts to pre-agreed success criteria. It must be clear what “scale” and “stop” mean, without subjective interpretation.

Keep three documents at hand in one place: metric results, scaling plan (steps, resources, risks) and rollback plan (how to return without data loss or service interruption). If any of these are missing, it’s better to extend the pilot by 1–2 weeks than to scale blindly.

Example scenario: a pilot in a medium organization without extra duplicates

An organization with 200 employees: office, warehouse and a small IT team. There’s a current service desk, basic server monitoring and a set of collaboration tools (chat, files, calendar). The goal is to run an Open Source pilot without keeping two identical systems for everyone and confusing people.

For 8 weeks they chose a limited but active scope: one department (about 40 users) plus the entire IT team, since IT handles tickets and incidents daily. The pilot included only areas with quick visible impact and minimal external dependencies.

In the test contour they moved the internal service desk for the chosen department and IT, monitoring for key services (2–3 critical servers and network devices), a knowledge base for routine instructions and a single sign-on via the existing directory (no account replacement).

Corporate email, final document approvals and shared file storage remained as before. Users didn’t have to live two parallel workdays.

Feedback was collected across three roles. Staff asked for clear ticket forms and faster search. Support specialists complained about extra clicks, lack of templates and poor notifications. Team leaders wanted reports: how many tickets closed, queue locations, and what fails most often.

The scaling decision almost failed because metrics showed rising average ticket handling time and more tickets returned for clarification. Before the second rollout they simplified forms (fewer fields, hints), added 10 short articles to the knowledge base and tuned routing rules. After these fixes handling time returned to baseline and the pilot could be expanded without creating duplicate systems.

Next steps after the pilot: scaling without extra pain

When a pilot succeeds there’s a temptation to roll out immediately. This moment often creates a parallel zoo: teams replicate settings differently, choose alternative plugins and create separate access rules. Before expansion, lock in a single “golden” configuration and a clear change process.

The first practical step is a map of services and owners — not a complex diagram, but a short list: what belongs to the target solution, who owns each component, who approves changes, who is on incident duty. This reduces the risk that a new department silently spins up a duplicate or follows different rules.

Plan support and training for the first 30–60 days after the pilot. Define where users ask questions, who answers and which mini-guides are needed. Usually a simple set covers it: “how to sign in”, “how to request access”, “what to do if it doesn’t work”, “where to send improvement ideas”.

Before scaling check infrastructure: is capacity sufficient, how is redundancy arranged, who will perform updates. Also consider procurement and lead times if new servers or seats are needed so rollout isn’t blocked by hardware.

If a single vendor is needed, evaluate who will take responsibility for integration and 24/7 support as well as for the holistic setup (access, monitoring, backups, updates). Often that’s cheaper than assembling on-call duty from multiple teams.

In Kazakhstan these tasks are often handled through a system integrator. For example, GSE.kz (gse.kz) can help with system integration, infrastructure and round-the-clock support, as well as supply domestic PCs and servers for expanding the test contour and subsequent industrial launch.