AI · AIC-62

Data Literacy, Quality and Governance

12 hours 2 days

Last updated 25 Sep 2026

Data Literacy, Quality and Governance is a 12-hour training course by IT Genius Institute. AI is only as smart as the data it is fed. Many AI projects fail not because of the technology but because data is scattered across many places, product or…

Training schedule

No public rounds are open right now — register your interest and we will contact you when the next round opens, or request an in-house session for your team.

Corporate training quote

AI is only as smart as the data it is fed. Many AI projects fail not because of the technology but because data is scattered across many places, product or vendor codes do not match between systems, records are incomplete, or no one clearly owns the data. At the same time many employees still lack confidence reading and questioning data, so decisions rest on gut feeling more than on facts.

This course builds the data foundation an organization needs for AI across three areas. Data literacy, so learners can read, interpret and communicate with data critically. Data quality, so they can measure quality as numbers and actually improve it with Power Query in a repeatable, automated way. Data governance, so they can define roles, standards and rules for data use following DAMA-DMBOK, connect them to data classification and PDPA, and assess data readiness before an AI project starts.

Learners finish with a Data Governance Mini-kit for the data domain they are responsible for. The content focuses on principles that apply to any system and is not training on a commercial data governance tool or a specific ERP / MDM system. (2 days, 6 hours per day, 12 hours in total, Beginner to Intermediate level.)

Objectives

Read, interpret and question data critically without falling into interpretation traps
Choose charts that fit the question and tell a data story executives can act on
Assess data quality against standard dimensions and measure it as numbers
Perform data profiling and data cleansing with repeatable, automated Power Query steps
Understand the DAMA-DMBOK data governance framework and data roles and responsibilities
Build a data dictionary and define data quality rules for a data domain
Assess data readiness for AI projects

Who this course is for

Key users and report preparers in each department
People assigned, or about to be assigned, as data owners or data stewards
Engineers and analysts who use data to solve problems
IT and DX teams who run data and AI projects in the organization
People who administer master data systems in the organization

Prerequisites

Intermediate Microsoft Excel skills such as basic formulas, Filter and PivotTable
Involved in recording, preparing or using data in the work of your department
No programming, database or advanced statistics background required
A Windows 10/11 laptop with Microsoft Excel for Microsoft 365, which includes Power Query

Curriculum

Course Details

A 2-day course, 6 hours per day (12 hours in total, 09:00-16:00), delivered as lectures with hands-on practice on simulated part master, production record and supplier datasets that carry the kind of quality problems found in real work. Beginner to Intermediate level. Practice uses Microsoft Excel, Power Query and Microsoft 365 Copilot, with DAMA-DMBOK and the Personal Data Protection Act B.E. 2562 (2019) as reference frameworks. This is not training on a commercial data governance tool or a

specific ERP / MDM system; it focuses on principles and practices that work with any system. Learners take home Power Query files for data cleansing, a data quality scorecard template, a Data Governance Mini-kit (data dictionary, RACI, data quality rules) and a data readiness for AI checklist. The course includes a 20-question pre-test and post-test plus an assessment of the scorecard and Mini-kit each learner produces, and it pairs well with the AI Governance and Responsible AI for Organizations course.

Day 1: Data Literacy and Data Quality

Section 1: Data, AI and Decision Making

Garbage in, garbage out, and why AI quality depends on the data it is fed
Data as an organizational asset that needs the same care as any other asset
Why AI projects fail: scattered data, mismatched codes and no clear owner
Examples of business damage caused by wrong data
Deciding on facts instead of gut feeling

Section 2: Data Literacy Fundamentals

Structured and unstructured data with everyday work examples
The difference between master data and transaction data
Metadata: data that describes data, and why it matters for shared use
Levels of analytics: descriptive, diagnostic, predictive and prescriptive

Section 3: Reading and Questioning Data

Mean, median and the spread of data
Spotting outliers and how they affect conclusions
Correlation versus causation: the trap behind many misreadings
Misleading charts and how to spot them
Activity: find the errors in a report that misreads its data

Section 4: Data Visualization and Data Storytelling

Choosing the chart that fits the question you need to answer
Laying out charts so they are easy to read and highlight what matters
Telling a story with data so executives see the point and can decide
Structuring content from facts through to recommendations

Section 5: Data Quality Dimensions

Accuracy and completeness: is the data correct and complete
Consistency and timeliness: does data match across systems and arrive on time
Validity and uniqueness: does data follow the defined format without duplicates
How to measure each quality dimension as a number

Section 6: Data Profiling and Cleansing with Power Query

Data profiling to survey the problems before fixing anything
Finding duplicates, fixing data types and splitting or merging columns
Standardizing codes and unpivoting data so it is ready for analysis
Combining data from multiple sources in Power Query
Building repeatable automated steps instead of fixing data by hand every month
Using Copilot to help detect anomalies in a dataset

Section 7: Workshop: Data Quality Scorecard

Brief: a simulated part master and production record dataset with many kinds of problems
Scoring data quality before cleansing against the dimensions covered
Cleansing the data with Power Query and scoring it again afterwards
Analyzing the upstream root causes of the quality problems found
Workshop: summarize the scorecard and root causes to carry into day 2

Day 2: Data Governance and Data Readiness for AI

Section 8: The DAMA-DMBOK Data Governance Framework

Overview of DAMA-DMBOK and the scope of data management
The principles, policies and standards at the core of data governance
Metrics for tracking data governance performance
An incremental starting approach for manufacturing organizations

Section 9: Data Roles and Responsibilities

Data owner: decides on and is accountable for the data of a domain
Data steward: maintains data standards and quality in practice
Data custodian: manages the systems and technical storage of data
Data user: uses data and follows the rules that apply
Building a RACI for data processes

Section 10: Master Data and Data Standards

Why accurate master data shared across the organization matters
Code and naming standards such as Part Number, Supplier Code and Machine ID
Managing reference data so every system uses the same set of values
Preventing code mismatches between systems at the source

Section 11: Metadata and Data Catalog

Business glossary: defining business terms so every function shares one meaning
Data dictionary: the details, data type and meaning of each field
Data lineage: tracing where data comes from and the path it takes
Bringing it together in a data catalog so data can be found and used correctly

Section 12: Data Classification, Security and PDPA

Data classification and how it links to the ISMS of the organization
Setting access rights in line with data classification levels
Using personal data only for its stated purpose under the Personal Data Protection Act B.E. 2562 (2019)
Preparing data safely and correctly before it goes into AI

Section 13: Data Readiness for AI

A checklist for assessing data readiness before an AI project starts
Availability and quality of the data to be used
Access rights that must be clearly settled
Labeling and whether there is enough data
Summarizing the assessment as an AI readiness score

Section 14: Workshop: Data Governance Mini-kit

Building on the upstream root causes found on day 1
Choosing one data domain from your own work or a case study prepared by the instructor
Sample data domains: Part Master, Production Record and Supplier
Producing a data dictionary, an owner / steward RACI and data quality rules
Workshop: assess the AI readiness score and present the domain Mini-kit

Frequently asked questions

Who is Data Literacy, Quality and Governance for, and what background is needed?

Built for Key users and report preparers in each department · People assigned, or about to be assigned, as data owners or data stewards · Engineers and analysts who use data to solve problems Background you should have: Intermediate Microsoft Excel skills such as basic formulas, Filter and PivotTable · Involved in recording, preparing or using data in the work of your department Not sure the fit is right? Talk to our team on LINE @itgenius or call 02-570-8449.

How much does Data Literacy, Quality and Governance cost and how long does it run?

THB 7,900 (currently THB 7,110 on promotion). The course runs 12 hours. The fee covers course materials, lunch and refreshments throughout. Pay by bank transfer to the company account, confirm it on our payment page, and we can issue the receipt or tax invoice in your company's name.

Do I get a certificate?

Yes. Everyone who completes the course receives a Certificate of Completion from IT Genius Institute. Each certificate carries its own number, and anyone holding that number can verify it online on our certificate page, so you can add it to your portfolio or pass it to HR as evidence of training.

Where does the training take place, and is there an online option?

You can attend onsite at IT Genius Institute or arrange to join online, and we also run it as a private in-house session for your team. Ask about dates and venues on LINE @itgenius or call 02-570-8449.

What if I fall behind or miss a session — can I retake it?

Yes. You may retake the same course free of charge in a later round, under the institute's conditions. Tell our team which course and round you attended, and we will check it and offer you the rounds that still have seats. Ask us on LINE @itgenius or call 02-570-8449.

How do I enrol, or request a quotation for my company?

Enrol online with the registration form on this page. You can register several attendees at once and enter your tax ID and billing address for the tax invoice. Or request a company quotation straight from the quote button. For anything else call 02-570-8449 or reach us on LINE @itgenius.