Who is this program built for?

Data and BI analysts, analytics engineers, product analytics teams, and BI managers who own dashboards, ad hoc analysis, and stakeholder reporting. Cohorts can mix levels since the modules move from foundations to advanced governance.

How is this different from your FP&A & Financial Analysts program?

Our FP&A program stays inside financial planning through budgeting and forecasting against a governed model. Variance analysis stays there too. This one covers data and BI analyst work instead: SQL, dashboards, documentation, and stakeholder reporting outside the planning cycle. If your analysts do both, we combine the two.

Does this cover model training or LLMOps, including fine-tuning?

No. Building, fine-tuning, evaluating, or operating your own models belongs to our separate Fine-Tuning & LLMOps program. This one stays inside day-to-day analyst work with existing AI assistants.

Do participants need a data-science background?

Working SQL and BI-tool knowledge, yes, since the exercises run on your own queries and dashboards. Data science, machine learning, or coding beyond SQL is not required.

Which tools will we be using?

Core skills get practiced on SQL and BI-tool copilots plus mainstream assistants such as ChatGPT and Microsoft Copilot, then adapted to whatever your organization has approved for analytics work.

How do you handle production or sensitive data during the training?

Schemas and sample rows in the exercises are synthetic. Anything pulled from a production warehouse is fully anonymized before use. One module draws the line directly: which schema details a prompt may carry, and which credentials must never leave the vault. Production access stays inside tools your organization has already approved.

Does the training let AI make decisions, or replace analyst judgment?

No. AI drafts queries, documentation, and narratives here. What a number means is still the analyst's call, and so is the judgment behind it. Every AI-assisted output gets checked against source before it reaches a stakeholder.

What decides the price?

What you pay tracks four things: group size, delivery format, how deep the customization goes, and location. You get a proposal scoped to your team structure and reporting calendar.

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, not just another chart, and the data dictionary has been behind the schema for months.

This program turns SQL copilots, BI-tool assistants, and everyday chat tools into a faster drafting and documentation workflow. Every query result and insight still gets checked against source before it reaches a stakeholder.

modules: 6
hours: 12

Some of the 500+ brands we've worked with

Selected brands we’ve worked with across industries.

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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.

A SQL clause built on the wrong join, or a KPI figure that sounds right and isn't, can reach a stakeholder deck fast — especially once deadline pressure sends schema samples into a consumer chatbot. Over two days, analysts draft and debug SQL against their own schema, turn dashboard numbers into defensible narratives, and attach an assumption checklist to every report, with every figure checked against the query that produced it. Teams focused on budgeting and variance analysis should use our FP&A training instead; neighbouring roles sit in the AI training programs index.

Ad hoc query requests eat the day

Steady "can you pull this number" requests compete with real analysis; AI drafts the query, the analyst still decides what it means.

Dashboards don't explain themselves

A chart shows a number moved but rarely why it matters; AI can draft the narrative without taking over the conclusion.

Numeric and query hallucination is a real, specific risk

A wrong-join SQL clause or a plausible but wrong KPI figure fails differently than bad prose — every AI-touched number needs verifying first.

Shadow AI meets production data

Analysts under deadline pressure already paste schema samples and query output — sometimes from live tables — into consumer chatbots.

The tools are already inside the BI stack

Copilot-style assistants already sit inside the SQL editor, BI tool, and spreadsheet — the gap is knowing what to hand over and how.

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

Enterprise RAG6 modules · 15 hoursTwo-day RAG training for AI and data teams: corpus design, retrieval engineering, grounded citations, evaluation, and the production security patterns most pilots skip.View training

AI for FP&A & Financial Analysts6 modules · 12 hoursAI training for FP&A and financial analysts, built around the planning calendar: driver trees, forecast assumptions, variance commentary, and board-ready packs, with every figure still reconciled to governed data.View training

NotebookLM6 modules · 11 hoursResearch, enablement, and knowledge teams get practical NotebookLM training: building bounded source-grounded notebooks, checking citations, generating briefings and Audio Overviews, and rolling the workflow out across a team.View training

Fine-Tuning & LLMOps6 modules · 14 hoursTwo-day applied training for AI engineering teams on the full model adaptation lifecycle: the fine-tuning decision, dataset engineering, SFT and PEFT, evaluation, deployment, and ongoing LLMOps.View training

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 colleagues handing over a reviewed work item between their role stations

Who is this program built for?
Data and BI analysts, analytics engineers, product analytics teams, and BI managers who own dashboards, ad hoc analysis, and stakeholder reporting. Cohorts can mix levels since the modules move from foundations to advanced governance.
How is this different from your FP&A & Financial Analysts program?
Our FP&A program stays inside financial planning through budgeting and forecasting against a governed model. Variance analysis stays there too. This one covers data and BI analyst work instead: SQL, dashboards, documentation, and stakeholder reporting outside the planning cycle. If your analysts do both, we combine the two.
Does this cover model training or LLMOps, including fine-tuning?
No. Building, fine-tuning, evaluating, or operating your own models belongs to our separate Fine-Tuning & LLMOps program. This one stays inside day-to-day analyst work with existing AI assistants.
Do participants need a data-science background?
Working SQL and BI-tool knowledge, yes, since the exercises run on your own queries and dashboards. Data science, machine learning, or coding beyond SQL is not required.
Which tools will we be using?
Core skills get practiced on SQL and BI-tool copilots plus mainstream assistants such as ChatGPT and Microsoft Copilot, then adapted to whatever your organization has approved for analytics work.
How do you handle production or sensitive data during the training?
Schemas and sample rows in the exercises are synthetic. Anything pulled from a production warehouse is fully anonymized before use. One module draws the line directly: which schema details a prompt may carry, and which credentials must never leave the vault. Production access stays inside tools your organization has already approved.
Does the training let AI make decisions, or replace analyst judgment?
No. AI drafts queries, documentation, and narratives here. What a number means is still the analyst's call, and so is the judgment behind it. Every AI-assisted output gets checked against source before it reaches a stakeholder.
What decides the price?
What you pay tracks four things: group size, delivery format, how deep the customization goes, and location. You get a proposal scoped to your team structure and reporting calendar.

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AI Training for Data & BI Analysts