Copilot for Data Analysis: Read, Verify, and Govern Generated Code

Generative AI assistants such as Microsoft Copilot can accelerate data analysis — but only when the person using them understands the code they produce, checks the results, and controls the data fed into the system; used blindly, they’re a fast path to plausible-looking but flawed numbers.

Background: why the Copilot conversation matters now​

Microsoft’s “Copilot” family has become an umbrella for multiple products — GitHub Copilot for code, Microsoft 365 Copilot for Office apps, and Windows/Edge Copilot experiences — and the tools are increasingly able to generate and execute code (mainly Python) as part of answering user requests. That change matters: when an LLM generates and runs code, the result is no longer just a language prediction; it’s an actual computation that can produce charts, aggregations, forecasts, and tables embedded back into a document or spreadsheet. Microsoft’s own product notes and blog posts document the expansion of Python-driven Copilot features in Excel and the integration of Copilot across Microsoft 365, emphasizing that Copilot will write, run, and insert Python code to surface deeper analysis directly in workbooks. (techcommunity.microsoft.com)
At the same time, high-profile model launches (including OpenAI’s GPT-5) and demo mishaps have put the limits of LLM reasoning and visualizations into sharp relief. Public demonstrations have contained chart errors and arithmetic slips that were attributed to human mistakes or model routing, prompting fresh debate over whether these assistants are trustworthy for numerical work. Independent reviews and news accounts show a mixed picture: significant claimed gains in reasoning and coding performance, but persistent examples of simple numerical errors or misleading visualizations during demos. This means that the capability to run code and return a numeric answer does not automatically guarantee numeric correctness. (businessinsider.com)

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How Copilot performs data analysis today​

The two-step anatomy: generate code, execute code​

Modern Copilot-style workflows for data analysis typically follow this pattern:

• User submits a natural-language prompt asking for an analysis, chart, or transformation of data.
• The assistant generates Python code (or a sequence of Excel formulas), often using libraries like pandas, matplotlib, or Altair.
• The platform executes that code in a controlled runtime and returns the numeric output, tables, or charts — and often an explanation of what it did.

That step of visible computation is the key difference from earlier pure-text LLM responses: you can, at least in principle, inspect the code that created the answer. Microsoft documents this explicitly with Copilot in Excel: ask for advanced analytics and Copilot will “automatically generate, explain, and insert Python code into your Excel spreadsheet.” (support.microsoft.com)

What this enables​

• Complex analytics without manual coding: Quick machine-learning forecasts, outlier detection, or multi-step aggregations that would otherwise take nontrivial Python expertise.
• Custom visualizations: Python affords far more control than native Excel charts; Copilot can build tailored plots and embed them into a workbook.
• Localization and accessibility: Microsoft has broadened language support for Python-driven Copilot features, lowering the barrier for non-English users to perform advanced analysis. (techcommunity.microsoft.com)

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Strengths: where Copilot genuinely helps​

• Speed and iteration: Copilot can scaffold an analysis in seconds — loading data, cleaning it, running an aggregate, and returning a plot. For exploratory work that benefits from rapid iteration, that’s huge.
• Democratization of complex tools: Embedding Python in Excel and wiring Copilot to generate that code brings powerful libraries to analysts without formal coding training. Microsoft’s February 2025 updates pushed this capability widely to Windows and web users, explicitly marketing it as a way to “gain deeper insights without needing to be a Python expert.” (techcommunity.microsoft.com)
• Transparency (when used correctly): Because Copilot often shows the code it executed, technically literate users can audit the logic, verify calculations, and spot errors — something you can’t do with pure black-box LLM text outputs.
• Productivity features across contexts: The Copilot branding ties developer tools (GitHub Copilot) and Office tools together, meaning organizations can use a similar mental model (and in some cases, shared models) for both coding and spreadsheet work. GitHub’s free Copilot tier and Microsoft’s more integrated Office Copilots are pushing adoption across skill levels. (github.blog)

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The hard truth: what still goes wrong​

Not a calculator — and not infallible math​

LLMs are fundamentally probabilistic language models, not symbolic math engines. They often “look like” a calculator because they generate numerical text, but they can and do produce arithmetic or logical errors. Even sophisticated models have been caught making basic decimal or visualization mistakes in public demos; those incidents show that automated code generation and execution can still deliver wrong numeric results or misleading charts unless explicitly checked. Multiple independent reports documented errors in demos of the latest general-purpose models, underscoring that errors happen at multiple layers: prompt construction, code-generation bugs, runtime assumptions, or visualization composition. (businessinsider.com)

Hidden assumptions and context sensitivity​

A Copilot-generated Python snippet may silently assume:

• a specific data schema,
• default treatment of missing values,
• particular aggregation windows,
• or internal data sampling.

Without explicit declarations, different runs or slightly different prompts can yield divergent outputs. That sensitivity is why the same Copilot prompt across different tenants, locales, or updated model versions can produce different numeric answers.

Hallucinations and grounding failures​

When Copilot “grounds” answers in tenant data or web sources, it can still hallucinate supporting facts or misattribute numbers. Microsoft’s Copilot stores interaction records for Copilot activity history, and while it claims this data isn’t used to retrain foundation models, the existence of stored interactions and the system’s grounding behavior mean users must treat Copilot outputs as drafts, not verdicts. (learn.microsoft.com)

Privacy, legal, and IP risk​

• Copilot interactions are recorded and stored (subject to tenant controls), and files you upload for analysis may be retained briefly for processing. Microsoft’s documents state stored prompts and responses are encrypted and handled per contractual commitments, but organizations with sensitive data need to treat Copilot like any external compute service and apply governance accordingly. (learn.microsoft.com)
• Code suggestions from GitHub Copilot derive from training on public code. That raises intellectual property and licensing questions for outputs that resemble training data. GitHub and Microsoft have published mitigating guidance, but risk remains for downstream reuse. (github.blog)

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Cross-checks and verifications: lessons from the GPT-5 era​

Recent model launches and demos show two important lessons for professionals who use AI for numbers:

• Independent, multi-source verification is essential. Demo slip-ups and post-hoc corrections (including public apologies for chart errors) prove that claims about model accuracy should be tested using independent datasets and methods. Relying on a single model output without cross-validation is risky. (businessinsider.com)
• Model improvements do not eliminate the need for human oversight. Reports praising the new model’s reasoning gains often come alongside examples of arithmetic or visual mistakes; the net takeaway is that improvements reduce but don’t remove error modes. Treat Copilot outputs as assisted analysis, not final decisions. (arsturn.com)

When reporting numbers or making decisions, it’s good practice to run at least two independent checks: re-run the analysis with a different prompt or tool, and reproduce the results manually (or with a simple script) if the stakes are high.

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Practical rules for using Copilot for data analysis​

If you understand code (recommended path)​

• Read the generated code before accepting results. If Copilot writes Python, step through the pandas operations, check group-bys, and ensure missing-value handling is explicit.
• Use versioned runtimes and pinned libraries. Differences in library versions (pandas, NumPy) can change results; pin versions or note the runtime used.
• Write unit tests and assertions inside the notebook. Add sanity checks: totals should sum correctly, percentages should be bounded in [0,100], row counts should match expectations.
• Use a separate, auditable script to re-run computations. Export the Copilot-generated code into an orchestrated script (with logging) so the process is repeatable and auditable.
• Ground outputs in raw data snapshots. Save the input dataset versions with timestamps and checksums so you can prove what Copilot actually computed and when.
• Prefer deterministic methods when possible. Seed random operations, or avoid stochastic approaches in exploratory summaries.

If you do not understand code (or don’t want to)​

• Don’t use Copilot alone for critical numbers. If you can’t read and verify the code, treat Copilot as an ideation or visualization helper only; get a code-literate teammate to audit any computation.
• Request verbose, step-by-step explanations and ask Copilot to “show the code and intermediate results.” But don’t rely on those explanations as proof — they are generated text and must be verified.
• Limit inputs to non-sensitive, public datasets or sanitized extracts. Avoid uploading proprietary or personal data unless governance controls are in place.
• Use built-in Excel formulas for final, auditable figures when feasible. Excel’s native formulas are easier to inspect for non-coders than a Python block you can’t parse.

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A recommended audit checklist before publishing numbers derived from Copilot​

• Confirm the raw data snapshot used by Copilot (file name, checksum, row count).
• Export the generated Python code and run it in a controlled environment you can inspect.
• Add sanity checks:
• Totals and subtotals equal expected sums.
• Date windows and time-zone assumptions are explicit.
• No silent type coercions (strings parsed as numbers).
• Recompute the same result with a second method or tool (e.g., Excel pivot, SQL query).
• Verify any charts: axis scales match reported numbers, labels correspond to data columns.
• Ask Copilot to show its intermediate results (group-by tables, aggregated frames) and compare them to your independent run.
• Store the entire analysis package (data, code, outputs) in version control or a secure archive for later auditing.

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Recommended workflows for newsroom and enterprise contexts​

• Small teams / reporters: use Copilot to prototype analysis, but require a code-reviewed “sign-off” step before publishing. The reporter drafts the narrative; a technically fluent editor validates numbers and stores reproducible artifacts.
• Data teams / analysts: incorporate Copilot into an automated pipeline: generate code drafts with Copilot, but execute tests and reproducibility checks in CI (continuous integration) pipelines that block deployment until assertions pass.
• Enterprises with governance needs: enable Copilot tenant grounding and control model training/data usage settings. Microsoft provides tenant-level grounding features to let Copilot reference organizational data; pair that with enterprise Purview/Content Search to manage recorded interactions. (techcommunity.microsoft.com)

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The policy and privacy angle: what organizations should demand​

• Explicit retention and deletion rules. Confirm how long Copilot stores prompts and outputs (Microsoft documents prompt/response retention and provides controls to delete conversations). For sensitive datasets, require contractual guarantees and process isolation (e.g., on-prem or VNET-isolated runtimes) before allowing uploads. (support.microsoft.com)
• Audit logs and exportability. Ensure the platform provides complete activity logs that can be exported for compliance reviews.
• Model training assurances. Obtain written confirmation whether organization data will or will not be used to improve foundation models; Microsoft states that user-uploaded content is not used to train its foundation LLMs, though prompts and interactions are stored for activity history and customer personalization settings. (learn.microsoft.com)
• IP and licensing controls. For code produced by GitHub Copilot, review the licensing guidance and legal opinion before republishing code or shipping it in products. GitHub’s free tier announcement reiterated the training-on-public-code basis, which raises IP considerations for derived outputs. (github.blog)

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When Copilot is clearly the right tool — and when it isn’t​

Copilot is well suited to:

• Exploratory analysis, chart prototyping, and quick statistical summaries.
• Bridging a skill gap: helping analysts learn Python idioms by example.
• Generating complex visualizations that would otherwise require more developer time.

Copilot is not suited to:

• Producing final, auditable numbers for regulatory filings or financial statements without strict verification.
• Handling highly sensitive PII/PHI without specialized privacy controls.
• Replacing a domain expert’s judgement in edge cases where context and nuance affect interpretation.

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Conclusion: tools that augment judgment, not replace it​

The practical takeaway is straightforward: generative AI assistants that can create and execute code are transformative for data work — but they shift the locus of responsibility, not eliminate it. When a Copilot writes Python and returns a chart, you’ve been handed a calculation, not a guarantee. The value arises when the user treats that calculation as an audited product: inspect the code, rerun it under known conditions, and cross-verify with independent methods.
For journalists, analysts, and enterprises, the rules should be explicit: use Copilot to accelerate analysis, but maintain human oversight, reproducibility, and governance. If you understand code, Copilot will multiply your effectiveness; if you do not (or will not), then Copilot should only be a sketching tool until a code-capable reviewer validates the work. The technology reduces friction — it does not remove the need for skepticism.
Finally, the public incidents and model reviews of the last year prove the point: models have dramatically advanced, yet they still make avoidable numerical and representation errors. Those errors are no longer opaque hallucinations; they are code-level mistakes you can detect — if you know where to look. Treat Copilot as a responsible assistant: require versioned inputs, verifiable outputs, and human sign-off before any figure goes public. (techcommunity.microsoft.com)

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Source: Online Journalism Blog Microsoft Copilot – Online Journalism Blog