Is Your Data Quality Good Enough for the AI Era?

When AI troubleshooting fails, people blame the Prompt, then the model. More often the problem is upstream: the telemetry you feed the AI is not good enough — APIs, SQL, entrypoints, and hops don’t line up, so even a strong model can only tell you to “go dig the traces yourself.” Same rule as RAG and AI support: bad data, fancy layers don’t help.

How do you know you “pass”? This article does three things:

1. Set a four-question bar
2. Score DataBuff, SkyWalking, Jaeger, Pinpoint, SigNoz, and OpenObserve with the same ruler
3. Show how DataBuff uses 12 fixed metric_service_* tables to meet that bar

1. A scenario you’ve probably hit

Checkout is slow in production. You plug AI into your APM and ask:

“Why is checkout slow? Which SQL is the bottleneck?”

Fail

“Please open the trace detail, find the root span manually, then correlate the DB span…”

AI becomes a fancy search box — barely better than no AI.

Pass

“Slow on SELECT … FROM orders, triggered by /checkout; the payment hop has the highest latency.”

No raw-trace digging — a direct answer.

In the AI era, data quality wins. Everyone can copy Prompts; a curated metric schema can’t be copied overnight. Bad datasets just make stronger models tell you to dig yourself.

Analogy: raw telemetry is unlabeled video footage; a high-quality dataset is a fixed-column spreadsheet — which API, which SQL, who triggered it — AI can answer from that.

2. The bar: four questions AI must answer

Forget field names. Ask your APM these four (with or without AI) — can it answer directly? That’s the bar.

1. Which API is broken?
   
   HTTP, DB, and MQ must not share one Span-name pile. Redis GET and checkout POST need separate stats.

2. Which SQL / which database is slow?
   
   “MySQL avg 50ms” is not enough — you need the exact statement.

3. Which page triggered the slow SQL?
   
   The critical question. Many stacks can’t answer — you’re sent back to hunt the entry by hand.

4. Which hop on the call path is dragging?
   
   order → payment → MySQL: traffic and errors per hop — not just a topology thumbnail.

Pass = all four answered directly. If one answer is “go dig the traces yourself,” the dataset fails.

3. App-performance matrix: where the six diverge

Same-environment application performance matrix. Basics (topology / service list / Trace) are common; the highlighted capability rows open the gap: call analysis, service flow, middleware pages.

Legend: ✅ verified · △ entry exists / limited depth · ❌ no equivalent

Versions tested: DataBuff v0.1.4 · SkyWalking 10.4.0 · Jaeger 1.76 · Pinpoint 3.1.0 · SigNoz 0.133 · OpenObserve 0.91-rc1.

Capability	DataBuff	SkyWalking	Jaeger	Pinpoint	SigNoz	OpenObserve
1. Global topology	✅	✅	△	✅	✅	❌
2. Service list / golden metrics	✅	✅	❌	✅	✅	✅
3. Service-level topology	✅	✅	△	✅	△	❌
4. Service call analysis	✅	❌	❌	❌	❌	❌
5. Instance golden metrics	✅	✅	❌	△	❌	❌
6. Instance topology	✅	❌	❌	❌	❌	❌
7. Instance call analysis	✅	❌	❌	❌	❌	❌
8. API-level topology	✅	❌	❌	❌	❌	❌
9. API call analysis	✅	❌	❌	△	❌	❌
10. Service flow	✅	❌	❌	❌	❌	❌
11. Middleware pages (DB / cache / MQ)	✅	❌	❌	△	❌	❌
12. Error analysis	✅	❌	❌	△	❌	△
13. Trace list / search	✅	✅	✅	✅	✅	✅
14. Trace detail	✅	✅	✅	✅	✅	✅
15. Span ↔ logs	✅	✅	△	❌	✅	✅
16. Log list / search	✅	✅	❌	❌	✅	✅
17. Log detail	✅	✅	❌	❌	✅	✅
18. Logs ↔ Trace	✅	✅	❌	❌	✅	✅

How to read it

1. All six can search traces; the gap is call analysis, service flow, middleware pages.
2. For humans staring at UI, SkyWalking / Pinpoint often suffice; for AI to answer the four questions, you need that depth.
3. Those green cells aren’t extra menus — they’re the backbone of the causality chain: which API, which SQL, who triggered it, which hop dragged.

4. How do you prepare a passing dataset?

Those capabilities aren’t pages bolted on later — incoming spans are written into fixed tables by type. DataBuff ships this as 12 metric_service_* tables.

Spans arrive
↓ Split by type: HTTP / DB / Redis / MQ / RPC… one table each
↓ Freeze key columns: entry API, SQL digest, path hops with the metrics
↓ Query joins the chain; UI grows middleware pages / service flow / call analysis

#	Table	Stores	Answers
1	metric_service	Service-entry RED	Is this service healthy?
2	metric_service_trace	Trace root	End-to-end success and duration
3	metric_service_http	HTTP API typing	Which URL / method / status is bad?
4	metric_service_db	DB calls	Which SQL is slow, who triggered (entry on same row)
5	metric_service_flow	Entry path tree	From entry, which hop drags?
6	metric_service_rpc	RPC calls	gRPC / Dubbo method and status
7	metric_service_redis	Cache calls	Who issues GET/SET, is it slow?
8	metric_service_mq	Messaging	Topic produce/consume, lag
9	metric_service_remote	External deps	External API QPS / latency
10	metric_service_exception	Entry exceptions	Exception name / code
11	metric_service_config	Config reads	Are Nacos / ZK reads slow?
12	metric_service_instance	Instance metadata	Pod / host / Java version (JOIN)

For the four questions, lead with tables 1 / 3 / 4 / 5.

Three words before you query:

• tag — filter columns (url, sqlContent, rootResource)
• field — numeric columns (cnt, sumDuration)
• virtual service — e.g. [mysql]demo_apm as a topology node

HTTP / DB / MQ / cache typed into topology — from component tables, not one Span-name dump

5. Four questions → which table

Question	Primary table	Key tags
Is the service healthy? (warmup)	metric_service	service, errorType
1. Which API is broken?	metric_service_http	url, httpMethod, httpCode
2. Which SQL is slow?	metric_service_db	sqlContent, isSlow
3. Who triggered the slow SQL?	metric_service_db	rootResource (same row as sqlContent)
4. Which hop drags?	metric_service_flow	entryInterfacePathId, pathId, parentService

① metric_service — warmup

Answers: “service-a QPS, error rate, avg latency today?”

Only entry requests write a row — filter out DB / Redis / MQ spans.

Service list — product face of metric_service

② metric_service_http — Q1

Answers: “Slowest URL? GET or POST? 4xx or 5xx?”

URLs like /demo/checkout as their own rows

③ metric_service_db — Q2 and Q3

Answers: “Which SQL is slow? Which entry triggered it?” — same table, same row.

sqlContent + rootResource on one row is the most important design for the bar: no Trace hunt for the trigger.

[mysql]demo_apm / ES as virtual services

④ metric_service_flow — Q4

Answers: “From service-a entry, how much response each hop contributes?”

The path tree is computed once a Trace is complete — not one hop per arriving Span.

Entry service-a expands downstream with response share

6. End-to-end: slow checkout

1. Entry health — metric_service: did service-a error rate / avg latency spike?
2. Pin the slow API — metric_service_http: confirm url is /demo/checkout
3. Slow SQL + trigger — metric_service_db: rootResource='/demo/checkout' AND isSlow=1
4. Which hop drags — metric_service_flow: expand from service-a, compare response share
5. (Optional) which machine — JOIN metric_service_instance

That’s data quality: filter and aggregate on fixed columns — not guessing Span names, not sending humans back into raw traces.

7. Three-step self-check

1. Run the four questions — if one answer is “dig the traces yourself,” you fail. Chat ≠ troubleshooting.
2. Check the highlighted matrix rows — call analysis / service flow / middleware pages — especially “slow SQL → entry.”
3. If you fail — add fixed columns (entry API, SQL digest) at write time, or switch to a more complete dataset.

Close: In the AI era, ask is your data quality good enough — before Prompts and models. Four questions set the bar; the matrix shows the product surface; the 12 tables are why.

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Try DataBuff

Open source · OpenTelemetry · datasets designed for AI querying

• Live Demo: https://demo.databuff.ai
• GitHub: https://github.com/databufflabs/databuff
• Full post: https://databuff.ai/blog/en/trace-metrics-data-quality/

If this helped, leave a ❤️ or a Star on GitHub — and tell us which of the four questions your stack still can’t answer.