# Data Quality Training: Find, Fix and Prevent Bad Data > Data quality training for analysts and data owners: profile datasets, clean errors with spreadsheets, SQL or scripts, and fix problems where they start. - Source: https://bodhih.com/data-quality-training/ - Publisher: Bodhih Training (Bengaluru, India; since 2008) - Last updated: 2026-10-12 - Contact: solutions@bodhih.com · +91 99000 11601 --- Data quality training for the people who live with bad data every day. In one hands-on day, analysts, MIS teams and business data owners profile a messy dataset, hunt down duplicates, blanks and rogue codes, clean them with spreadsheets, SQL or simple scripts, and agree who fixes what at the source. ## At a glance - **Duration:** Recommended: 1 hands-on day in person, or 3 × 2.5-hour live virtual labs, plus a 30-day fix-at-source project - **Who it’s for:** Analysts, MIS teams, data stewards and business users who own or maintain data - **Level:** Intermediate - **Format:** In person · Live virtual · Blended - **Track record:** Since 2008 · 2,000+ companies Data quality training is a hands-on program that teaches analysts, MIS teams, data stewards and business data owners to recognise, measure and fix poor data. It covers profiling, duplicates, missing values, inconsistent codes and dates, cleaning with spreadsheets, SQL or scripts, simple quality rules and ownership habits. Bodhih customises it with the ADDIE model. - Recommended: 1 hands-on day in person, or 3 × 2.5-hour live virtual labs, plus a 30-day fix-at-source project - Level: Intermediate · In person · Live virtual · Blended - Designed with the ADDIE model and customised to your context - Offered by Bodhih since 2008 to 2,000+ organisations across 7 regions **Also known as:** data cleaning training, data quality management course, data stewardship training, data profiling workshop **Where and how:** data quality training as an in-person workshop in [Bengaluru](https://bodhih.com/corporate-training-in-bangalore/), [Mumbai](https://bodhih.com/corporate-training-in-mumbai/), [Delhi NCR](https://bodhih.com/corporate-training-in-new-delhi/), [Gurugram](https://bodhih.com/corporate-training-in-gurugram/), [Hyderabad](https://bodhih.com/corporate-training-in-hyderabad/), [Chennai](https://bodhih.com/corporate-training-in-chennai/), [Pune](https://bodhih.com/corporate-training-in-pune/), [Kolkata](https://bodhih.com/corporate-training-in-kolkata/), [Ahmedabad](https://bodhih.com/corporate-training-in-ahmedabad/) and [Jaipur](https://bodhih.com/corporate-training-in-jaipur/); as a live online course; or delivered overseas in [Dubai](https://bodhih.com/corporate-training-in-dubai/), [Singapore](https://bodhih.com/corporate-training-in-singapore/) and across the Middle East, Asia and Africa. Last reviewed 12 Oct 2026 by [Siddharthan VGJ](https://bodhih.com/siddharthan-vgj/), Founder · Bodhih Training, Bengaluru ## Why data quality has moved from IT's problem to everyone's Every AI pilot, dashboard and month-end report rests on the same foundation: rows of data typed, imported and copied by people across the business. When one customer appears three ways and dates switch format halfway down a column, the reports look confident and are quietly wrong. Most fixes happen downstream, by an analyst cleaning the same file every Monday. Data quality training moves the effort upstream, giving data owners shared rules, simple checks and the habit of fixing errors where they start. > AI and big data, networks and cybersecurity, and technology literacy are the fastest-growing skills to 2030. Source: [World Economic Forum, Future of Jobs Report 2025](https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/) ## What participants will be able to do ### Define good data for your context Describe accuracy, completeness, consistency, timeliness and freedom from duplicates using examples from their own datasets. ### Profile a dataset fast Produce a quick profile of row counts, blanks, distinct values and outliers before trusting any file. ### Clean common errors Remove duplicates, handle missing values and standardise codes, names and dates with repeatable steps. ### Write simple quality rules Turn expectations such as 'every order has a valid branch code' into checks that run automatically. ### Own the data Agree who owns, maintains and approves each critical data element, and how issues are logged and closed. ### Fix problems at the source Trace a recurring error back to the form, process or import that creates it and propose a lasting fix. ## Who should attend - Data and business analysts who spend too much of the week cleaning files - MIS and reporting teams in BFSI, manufacturing, retail and logistics - Data stewards and data owners named in a new data governance setup - Operations staff who maintain customer, product or vendor master data - GCC teams supporting global reporting, analytics and AI projects - Team leads who sign off reports built on data from several departments ## Program outline Recommended design, customised to your context after a short needs analysis. ### **What good data looks like**Module 1 · 60 min - Six everyday dimensions of data quality, with workplace examples - The cost of bad data: rework, wrong decisions and failed automations - Critical data elements: the fields that really matter ### **Profiling a dataset**Module 2 · 75 min - Row counts, blanks, distinct values and ranges - Spotting outliers and impossible values - Profiling in spreadsheets and with simple SQL queries - Lab: profile a 20,000-row customer file ### **Hunting common errors**Module 3 · 90 min - Exact and near duplicates, including name variants - Missing values: when to fill, flag or leave - Inconsistent codes, units and spellings - Date and number formats that break quietly ### **Cleaning with spreadsheets, SQL and scripts**Module 4 · 90 min - Repeatable cleaning steps instead of manual edits - SQL patterns for standardising and de-duplicating - When a short [Python](https://bodhih.com/python-for-data-analysis-training/) script beats a spreadsheet, with a guided example - Keeping a cleaning log others can follow ### **Quality rules and checks**Module 5 · 60 min - Writing rules in plain language first, then as checks - Thresholds and alerts for key fields - A simple data quality scorecard for a monthly review ### **Ownership and fixing at source**Module 6 · 75 min - Owner, steward and user: who does what - The issue log: capture, assign, fix, verify - Tracing root causes back to forms, imports and handovers - Planning your 30-day fix-at-source project ## Sample training plan: 1-day hands-on design This hands-on day starts with a messy customer file and ends with a plan to stop the mess at its source. Participants define good data in their own words, profile 20,000 rows in under an hour, hunt duplicates, blanks and rogue codes, and clean them with repeatable steps in a spreadsheet, SQL or a short script. They write plain-language quality rules, turn them into checks and trace a recurring error back to the form that creates it. Each team leaves with a 30-day fix-at-source project. 80% hands-on practice · 6.8 contact hours · Cohort: 10–20 participants · In person · Live virtual ### Day 1: Find it, fix it, stop it Participants can profile and clean a dataset, write quality rules and plan fixes at the source. 1. 09:30–10:00 · Spot the dirty data (Energiser) 2. 10:00–10:45 · What good looks like here (Concept burst) 3. 11:00–12:00 · Profile 20,000 rows (Hands-on lab) 4. 12:00–13:00 · Clean it, repeatably (Hands-on lab) 5. 13:45–14:45 · Rules before tools (Build sprint) 6. 14:45–15:30 · Whose data is it? (Case clinic) 7. 15:45–16:45 · Follow the error upstream (Group challenge) 8. 16:45–17:30 · My 30-day fix-at-source project (Action planning) Recommended: 1 hands-on day in person (09:30–17:30), or 3 × 2.5-hour live virtual labs, plus a 30-day fix-at-source project. A sample design: every Bodhih program is customised after a short needs analysis. ### How long is data quality training? The recommended design is one hands-on day in person or three live virtual labs of two and a half hours, followed by a 30-day fix-at-source project, with reviews at 30, 60 and 90 days. Analyst groups can extend it to two days with deeper SQL and scripting labs. ### What activities are included in data quality training? Participants spot hidden errors on paper, define good data for their critical fields, profile and clean a 20,000-row file, write quality rules and turn them into checks, assign ownership in a lending case and trace a recurring error back to its source. ## How we deliver it ### Your data, safely Labs use realistic sample files, or anonymised extracts of your own data where approved, so every technique transfers directly. ### Tool-flexible labs Each exercise can be done in a spreadsheet, in SQL or with a short script, so business users and analysts learn side by side. ### Rules before tools Participants agree what 'good' means in words before writing any check, which keeps business owners and technical teams aligned. ### Fix-at-source project Every team leaves with one recurring data problem to trace and fix upstream within 30 days. ## Tailored versions ### For business data owners Less code, more ownership: defining good data, writing rules in plain language and running the monthly quality review. ### For analysts and MIS teams Deeper labs in SQL and scripting, reconciliation between systems and automated checks inside recurring reports. ### For AI and analytics projects Focus on preparing data for models and dashboards, documenting known issues and checking inputs before they feed a decision. ## How we measure impact We measure the impact of data quality training in the Evaluate stage of the ADDIE model. Participants take two AssessAll pre-assessments, the Data Cleaning Skills Assessment with Executed Scripts and the Data Governance & Data Quality Stewardship Judgment, and repeat them as post-assessments at day 90. A 30-60-90 day follow-up tracks the fix-at-source project, issue-log closure and error rates on the chosen dataset with the data owner and your analytics lead. AA Pair this program with [AssessAll](https://assessall.com), Bodhih’s AI assessment platform, for pre- and post-program skill measurement. ## Frequently asked questions ### What is data quality training? Data quality training teaches people who create, manage or use data how to recognise, measure and fix errors. Typical topics are profiling, duplicates, missing values, inconsistent codes and dates, cleaning methods, quality rules and ownership. Bodhih's version is hands-on, with labs in spreadsheets, SQL or simple scripts on realistic files from your industry. ### What are the main dimensions of data quality? The most commonly used are accuracy, completeness, consistency, timeliness, validity and freedom from duplicates. In practice, the program helps teams decide which matter most for each critical field. A customer phone number may need to be valid and never duplicated, while a monthly revenue figure must above all be timely and consistent across reports. ### Do participants need to know SQL or coding? No. Every lab can be completed in a spreadsheet, and SQL or scripting options are offered for those who already use them. Mixed groups work well because business owners see what analysts struggle with and analysts learn why errors occur upstream. We tailor the technical depth after the pre-assessment. ### How do you fix data quality problems at the source? Log each recurring error, then trace it to where it enters: a free-text form field, a manual import, a missing dropdown or a handover between teams. Fix that point, for example with validation or a clear owner, and measure whether the error rate falls. Downstream cleaning should be the exception, not the weekly routine. ### What does a data steward do? A data steward looks after the quality of a defined set of data, such as customer or product records. They agree definitions and rules, monitor quality checks, coordinate fixes with the people who enter data and keep the issue log moving. The program includes a module on stewardship roles and day-to-day habits. ### Can the training use our own data? Yes, where your organisation approves it. Teams can share anonymised extracts so participants profile and clean the files they actually work with. Otherwise we build realistic sample datasets from your industry, such as loan applications, product masters or delivery records, so the exercises still feel familiar. ### How long is data quality training, and where is it delivered? The recommended design is one hands-on day or three [live virtual](https://bodhih.com/live-virtual-classes/) labs, plus a 30-day fix-at-source project. Bodhih runs it in Bengaluru, Mumbai, Delhi NCR, Hyderabad, Chennai and Pune, and master trainers travel to Dubai, Riyadh and other cities. Analyst groups can extend it to two days.