AI Training · Data & BI Analysts

AI Training for Data & BI Analysts

Every reporting cycle brings another ad hoc "pull this number" request. Dashboards need a narrative instead of a chart, while the data dictionary fell behind the schema months ago. This program turns SQL copilots, BI-tool assistants, and everyday chat tools into a faster drafting and documentation workflow, while every query result and insight still gets checked against source before it reaches a stakeholder.

modules: 6
hours: 12

Ask a data team what ate their week and the list barely changes. There is one more "can you pull this" request and a dashboard that needs a narrative. The stale data dictionary is still waiting too. None of that is the analysis the team was hired to do.

The failure mode is specific. A model can write a SQL clause against the wrong join, or state a KPI figure that sounds right and isn't, and that error can reach a stakeholder deck faster than a clumsy sentence ever would. Analysts under deadline pressure already paste schema samples and query output into consumer chatbots, sometimes straight from production tables.

Over two days, analysts draft and debug SQL against their own schema and keep documentation in step with it. They turn dashboard numbers into defensible narratives and attach an assumption checklist to every stakeholder report. Teams focused on budgeting and variance analysis will get more from our AI training for FP&A and financial analysts. This program stays inside data and BI analyst work.

You leave with a review standard and a shared query library, plus a plan for what happens after the workshop ends. The AI training programs index covers the neighbouring roles. Here, that means analysts spend less time assembling numbers and more time explaining what they mean, with every figure still checked against the query that produced it.

Ad hoc query requests eat the day

A steady stream of "Can you pull this one number" requests competes with the actual analysis work. AI can draft the query and a plain explanation. The analyst still decides what the number means.

Dashboards don't explain themselves

A chart shows that a number moved but rarely why it matters or what to do next, so analysts lose hours on narratives AI can draft without taking over the conclusion.

Numeric and query hallucination is a real, specific risk

A SQL clause that looks right against the wrong join is a different failure than a clumsy paragraph. So is a KPI figure that sounds plausible and simply isn't. Analysts need training built around verifying every AI-touched number before it ships.

Shadow AI meets production data

Under deadline pressure, analysts already paste schema samples and full query output into consumer chatbots for a faster answer, sometimes straight from live tables. Structured training turns that habit into a sanctioned workflow with a clear line on what a prompt should never contain.

The tools are already inside the BI stack

Copilot-style assistants already sit inside the SQL editor and BI tool, as well as the spreadsheet your analysts use every day. The gap is knowing which reporting tasks to hand over and how to prompt for them. Someone still checks the result before it reaches a decision-maker.

01
AI foundations for data & BI analysts
90 minBeginner
What large language models do well with structured data and reporting text, and where they misfire against a live schema or a real number. Analysts leave this session knowing what to hand off before they touch a production query or dashboard.
What large language models get right with structured data, and where plain-language explanation trips them up
Query and numeric hallucination: a plausible but wrong join or filter, including a KPI figure
Tasks worth handing to AI versus judgment calls that stay with the analyst
Approved tools, settled early
Low-risk starting points across a typical reporting cycle
02
SQL and query drafting with AI
120 minBeginner
Draft and explain SQL against your own schema, then debug the SQL or BI-tool query. Production credentials and full customer tables never go into a chat window. Every drafted query gets checked against a known-good result before anyone trusts it.
Drafting a SQL query from a plain-language stakeholder request
Getting AI to explain an unfamiliar or legacy query line by line
Debugging a failing query with AI-suggested fixes you check yourself
Moving between SQL dialects and BI-tool query languages
What a prompt should never contain: schema and credential boundaries
Checking every AI-drafted query against a known-good result set
03
Data documentation and dictionaries
120 minIntermediate
Data dictionaries are usually the first thing to go stale. This module builds the habit of drafting definitions from schema and sample data with AI. Teams verify each one against source before it reaches a shared wiki.
Drafting a data-dictionary entry from schema and sample values, grounded in existing usage
Documenting metric definitions and edge cases the same way every time
Tracing a field back to its source system and transformation logic
Flagging undocumented or ambiguous fields for owner review
Keeping documentation in step with a schema that keeps changing
04
Dashboard and insight narratives
150 minIntermediate
Dashboard numbers become insight summaries, trend call-outs, and anomaly flags stakeholders can read, while the underlying query remains the source of truth an analyst can point back to.
Drafting an insight summary straight from a dashboard's underlying data
Writing trend and anomaly call-outs in one consistent house voice
Explaining a metric-definition change in language the audience can use
A reusable narrative template
Knowing when a chart needs a caveat
Reconciling a drafted narrative against the query behind it
05
Stakeholder reporting and analysis QA
150 minIntermediate
The weekly report and the one-off stakeholder request both get drafted faster with AI. What comes next is an analyst's own QA discipline: an assumption checklist and a challenge question before a number gets filed.
Drafting stakeholder reports and one-off analysis requests straight from a data pull
An assumption checklist for every AI-assisted analysis
Generating challenge questions that pressure-test your own findings
Reconciling AI-drafted summaries against the underlying query output
Answering the question behind the question in a stakeholder request
Confidence language, by audience
06
Governance and adoption for analytics teams
90 minAdvanced
Individual skill fades fast without a system behind it. This module builds a review standard and a shared prompt and query library the whole team can draw on. It ends with a 90-day rollout plan and a date to check whether it's working.
A review standard for every AI-assisted query or dashboard, including reports
Building a shared prompt and query library for the analytics team
Access and retention rules, with an audit trail for tools touching production data
Logging every AI-assisted analysis
Picking pilot workflows with time savings you can actually measure
A 90-day rollout plan you leave with

What you will learn

Draft a SQL or BI-tool query straight from a plain-language ask, then check it against a known-good result
Turn a dashboard's raw numbers into a narrative that names the next step
Fewer stale data-dictionary entries
Ship a stakeholder report with its assumption checklist already attached
Challenge questions ready before a stakeholder asks them
Know which schema details a prompt may carry, and which credentials it must never touch
One review standard, every time
Build a shared prompt and query library the whole team uses

Who should attend

Data analysts and BI analysts across every function
Analytics engineers and reporting specialists
Product analytics and insights teams
BI managers and analytics team leads
Analysts embedded in sales or operations, including marketing
Data teams that own stakeholder and executive reporting

Reporting calendars anchor the two-day program so live sessions don't collide with a close or a release. Onsite and live-online formats run the same syllabus.

Format: Onsite or live online
Duration: 2 days (about 12 hours, can be split into half-day sessions)
Group size: Up to 16 participants per group
Language: English or Turkish
Materials: Prompt library, SQL and query templates, and a review checklist
Certificate: Certificate of completion

Zeo started in 2011 and now works out of San Francisco, Istanbul, Ankara, and Lisbon. We run Copilot Academy and organize Digitalzone, an international digital marketing conference. This program draws on the 10+ years of consulting and training work behind that, applied to corporate AI adoption.

2011founded in Istanbul
10+years of consulting and training experience
3offices: San Francisco, Istanbul, Ankara, Lisbon

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We start by looking at your BI stack, your current tools, and where query requests or stakeholder reporting eat the most time, then build the modules and templates around what we find.

Two illustrated figures planning together around a shared screen

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AI Training for Data & BI Analysts