Data Literacy, Quality and Governance is a course in reading and questioning data, measuring data quality, cleansing data with Power Query and Copilot, and setting up data governance based on DAMA-DMBOK with a data dictionary and AI readiness check. It suits key users, report owners, data owners and stewards, and IT and digital transformation teams.
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.)
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.
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
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
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
The usual dimensions are completeness, accuracy, consistency between systems, timeliness, validity against formats and rules, and uniqueness. Each can be measured as a number, such as the percentage of part codes with no unit of measure, and the results combined into a scorecard so you can track whether quality is improving or getting worse.
Start by loading the data into Power Query and turning on Column Quality and Column Distribution to spot blanks, errors and duplicates. Then trim extra spaces, standardise capitalisation, set the correct data types, replace inconsistent spellings and remove duplicate rows before loading the result back into Excel. Every action is recorded as an applied step, so a single refresh repeats the whole clean-up on new data.
A data owner is the manager accountable for a data set from a business point of view, deciding its definitions, who may access it and what level of quality is acceptable. A data steward looks after the data day to day, checking quality, correcting errors, maintaining the data dictionary and making sure everyone follows the same rules. Setting out these roles in a RACI chart gives each data set a clear owner.
It suits key users and report preparers in each department, people assigned as data owners or data stewards, engineers and analysts who use data to solve problems, and IT or digital transformation teams running data and AI projects. You should use Excel at an intermediate level, including basic formulas, filters and PivotTables, and work with data in your job. No programming, database or advanced statistics knowledge is required.
You will be able to read and question data critically, measure data quality with a scorecard, profile and cleanse data with repeatable Power Query steps, write a data dictionary and data quality rules for a data domain based on DAMA-DMBOK, and assess whether data is ready for an AI project. Participants who complete the training receive a Certificate of Completion with its own number, which can be verified online.
Bring a Windows 10 or 11 laptop with Microsoft Excel for Microsoft 365, which already includes Power Query. Practice uses simulated data sets, such as a part master, production records and supplier data, containing the kinds of quality problems found in real work. The course focuses on principles that apply to any system rather than on a commercial governance tool or a specific ERP.
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