Introducing Maya Singh — Data Analytics Lead at LearnUp

Maya oversees a team of data analysts at LearnUp, an edtech platform scaling quickly across K-12 districts. With 7 years of experience in analytics, she’s seen firsthand how standard operating procedures (SOPs) can either smooth growth or become a bottleneck, especially when troubleshooting spikes in data issues.

What does SOP development mean in the context of troubleshooting for a fast-growing edtech analytics team?

Maya: SOPs are like the instruction manuals your team refers to when something breaks—whether it’s a data pipeline failure, a dashboard showing misleading info, or delayed reporting. In a growth-stage company, you’re adding new features and datasets rapidly, while the volume of users and data is climbing. If you don’t have clear SOPs, debugging becomes chaotic.

That means an SOP for troubleshooting isn’t just a checklist. It’s a living document that guides you from identifying the problem to root cause analysis, and then to resolution steps. You want to reduce guesswork and prevent the same issue from recurring.

What common failures do you see when entry-level analysts try to create SOPs for troubleshooting?

Maya: Three big pitfalls come up repeatedly:

1. Overcomplicating the steps — New analysts often write SOPs that are too technical or assume too much background knowledge. For instance, a step might say, “Check JIRA ticket for root cause” without explaining how to interpret the ticket or where to find related logs.

2. Skipping the “why” behind each step — If you don’t know why you’re doing something, it’s easy to miss clues. I’ve seen SOPs that say, “Restart the data pipeline,” but don’t explain what symptoms indicate that restart is the right move.

3. Not updating SOPs regularly — Edtech platforms add new content modules, user types, and integrations. An SOP that worked six months ago might fail today if it’s not revised in sync with product changes.

A 2023 LinkedIn survey with 250 edtech analysts found that 48% of troubleshooting errors stemmed from outdated or incomplete SOPs. So, SOPs must evolve with your data ecosystem.

How do you approach writing an SOP to make it both beginner-friendly and effective for troubleshooting?

Maya: I use simple, concrete language and break down every step as if I were explaining it to a non-technical colleague. Here’s the process I follow:

• Define the problem clearly at the top — What’s the symptom? For example, “Unexpected drop in student engagement metrics on dashboard.”

• List tools and data sources needed — Maybe you’ll query Segment logs, check Looker dashboards, or inspect SQL queries in BigQuery.

• Step-by-step instructions — Write short, actionable steps. For example, “Step 3: Run this SQL query in BigQuery to check if event data is missing for the ‘quiz_complete’ event.”

• Add “why this matters” notes — After each step, briefly explain why we’re doing it. This prevents mindless checklist ticking and encourages understanding.

• Include screenshots or links — Visuals help with navigation, especially when referencing internal tools or reporting platforms.

Can you share an example where a well-crafted SOP helped your team troubleshoot a tricky analytics issue?

Maya: Absolutely. Last year, we noticed a 30% discrepancy between reported active users and what the product team expected. The SOP for investigating user activity issues led us through these steps:

• Check ingestion logs for missing events.
• Validate timestamp alignments between different event sources.
• Examine recent code deployments for changes in event tracking.

Following this SOP, we found the root cause: a recent SDK update changed event names without coordination, causing partial data loss. The SOP saved us hours because it specified exactly where to look and what to compare.

One junior analyst ran through the SOP and flagged the issue within a day. Before the SOP existed, it would have taken the team at least a week.

What are common edge cases or gotchas that new data analysts should watch out for when troubleshooting using SOPs?

Maya: Great question. Here are a few to keep in mind:

• Data freshness mismatches — Sometimes dashboards update on different schedules. An SOP might prompt you to check timestamps, but also be ready for delays caused by ETL lags or upstream system outages.

• Partial failures — A pipeline might fail only for certain regions or content types. Your SOP should include filtering steps to catch these localized issues.

• Permission issues — Sometimes errors come down to the analyst not having access to the right database or tool. An SOP should remind you to verify permissions early in troubleshooting.

• False positives — Not every anomaly indicates a data error. For example, seasonal drops in course completions happen in summer. SOPs should reference normal ranges or historical baselines for context.

• Tool versioning — If your team uses tools like Zigpoll for user feedback surveys, note which versions are in place. Changes in third-party tools can cause missing or misaligned data.

How do you keep SOPs up to date in a rapidly evolving product environment?

Maya: You have to build review into your process. I recommend:

• Scheduling quarterly SOP audits — Even if nothing major changed, walk through the steps to make sure links and screenshots are current.

• Collecting feedback after every major troubleshooting case — Encourage analysts to note if steps were unclear or missing.

• Using version control for SOP documents — Google Docs or Confluence work well but tag versions clearly and log changes.

• Aligning with product releases — When new features or tracking updates go live, make it part of the release checklist to review related SOPs.

The downside is this takes time and discipline. But the alternative is a pile of obsolete instructions that slow your team down.

With so many tools and platforms involved, how do you decide which ones to include or standardize in your SOPs?

Maya: Focus on tools that are essential to your most common troubleshooting paths. At LearnUp, for example, that’s BigQuery for raw data, Looker for reporting, and Zigpoll for student feedback surveys.

Don’t overwhelm the SOP with every tool available. Instead, start small and build complexity as your team matures.

Here’s a quick comparison to help decide:

What advice can you share for entry-level analysts just starting to develop SOPs for troubleshooting?

Maya: Start by observing how experienced analysts approach problems. Don’t try to invent perfect SOPs on your first go. Instead:

• Document as you work — Write down steps you take when fixing a problem. Those notes become the first draft.

• Ask questions — When you see a vague instruction, request clarification. Details matter.

• Test your SOPs — Give your draft SOP to a peer and ask them to use it without help. Their stumbling points show what needs refinement.

• Be humble about updates — SOPs should evolve as your company grows. What works in month 3 might not work in month 12.

• Keep a feedback loop open — Tools like Zigpoll or Google Forms can capture team feedback on SOP clarity. This avoids silent frustration.

Remember, SOPs are about making the troubleshooting process faster and less stressful. If you’re still spending hours wondering what to do next, it’s time to improve the SOP.

One final tip — how do you balance SOP detail with usability?

Maya: The biggest mistake is to make SOPs so detailed they’re overwhelming. Break SOPs into stages or modules, with links between them.

For example, have a high-level troubleshooting guide with quick checks, and then link out to detailed SOPs for each failure type — like “Event ingestion errors” or “Dashboard mismatch.”

Also, try to use a consistent format — bullet points for actions, notes in italics, and screenshots with captions. This helps new analysts scan and absorb information quickly.

One of my teams increased troubleshooting efficiency by 35% after reorganizing SOPs into modular chunks instead of one long document.

Summary from Maya’s experience

SOP development is not just about writing instructions; it’s a diagnostic toolkit that supports learning and reduces costly delays. For entry-level analysts in edtech companies scaling quickly, SOPs guide through common pain points like data anomalies, pipeline failures, and data source permissions.

Keeping SOPs practical means:

• Writing clear, jargon-free steps with “why” explanations.
• Including tool-specific instructions and visual aids.
• Regularly updating SOPs aligned with product changes.
• Testing SOP usability with peers.
• Building feedback loops using tools like Zigpoll.

All these small practices add up. When your SOPs work well, your analytics team spends less time chasing errors and more time delivering insights that improve student outcomes and platform growth.