From “Paper Chaos” to Structured Analytics: My Experience Digitizing a Library Collection

Цей допис відображає мій особистий професійний досвід у сфері підготовки даних. Інструменти АІ було використано для забезпечення професійного перекладу та форматування цього допису.

– How do you turn over 4,800 records into an effective analysis tool?
– Recently, I completed a project that became an excellent practice in Data Quality and data automation.

The Task: Digitize a library archive, including titles, authors, inventory numbers, and the presence of signed copies.

Challenges:
Physical Condition: Paper records with significant wear, faded numbers, and damaged text.

Language Diversity: The data included not only Ukrainian and Russian but also English, French, Polish, Czech, and other languages.

What I did:
1) Digitization: I used AI-powered text recognition tools, followed by extensive manual data verification in Excel.

2) Structuring: I broke down unstructured descriptions into separate attributes (author, title, genre, year, city, language, etc.) for deeper analysis.

3) DQ Processes: I performed data deduplication, identifying and cleaning repeats based on inventory numbers.

4) Analytical Add-ons: I used Excel formulas to segment the collection (distribution by language, time ranges, and counting signed copies—over 600 items).

5) Optimization: I created a multi-layered file architecture with separate tabs for data collection and a final consolidated dashboard.

The Result: I transformed a non-structured paper archive into a manageable database, ready for analysis and informed decision-making.

This project proved to me that a love for order and attention to detail are the cornerstones of a successful data analyst.

– How do you overcome challenges when working with “dirty” data?

Please note: The book titles and author names in the table are illustrative placeholders generated by AI. They are intended to demonstrate the structure and data quality processes applied to the actual dataset.

Будь ласка, зверніть увагу: назви книг та імена авторів у таблиці є ілюстративними заповнювачами, згенерованими штучним інтелектом. Вони призначені для демонстрації структури та процесів контролю якості даних, застосованих до реального масиву даних.