Türk Startupları İçin Entegre Bir Veritabanı Oluşturma: Sistematik ve Yenilikçi Bir Çerçeve

Abstract

Son yıllarda, Türkiye'deki startup ekosistemi, devlet desteklerinin artması, özel yatırımların çeşitlenmesi, startup kültürünün dünya çapında yayılması ve teknolojik gelişmeler sayesinde önemli ölçüde büyümüştür. Hızla artan sayılara rağmen, farklı sektörlerdeki yeni girişimci firmalar için güncel, kapsamlı ve analitik olarak kullanılabilir bir veri tabanı bulunmamaktadır. Bu çalışma, geleneksel ETL süreçlerini modern veri mühendisliği teknikleriyle birleştiren hibrit bir metodoloji kullanarak Türkiye'deki startup'lar için entegre ve merkezi bir veri tabanı oluşturmaktadır. Şirketlere ait tüm veriler, web kazıma yöntemi ile kamuya açık veri tabanlarından elde edilmiş ve belge tabanlı bir NoSQL veri tabanı olan MongoDB'de bir veri tabanında depolanmıştır. Veri ön işleme tutarlılık, bütünlük ve yapısal bütünlük sağlarken, keşifsel veri analizi Türkiye'deki startup ekosisteminin coğrafi dağılımı, faaliyet alanları ve işgücü metrikleri hakkında kritik içgörüler ortaya koymuştur. Çalışmanın sonunda elde edilen bulgular araştırmacılar, politika yapıcılar ve farklı sektörlerde faaliyet gösteren firmalar dahil olmak üzere paydaşlara çok değerli bilgiler sunmaktadır. Ölçeklenebilirliği ve uyarlanabilirliği ile karakterize edilen çalışmada tanıtılan veri hattı oluşturma metodolojisi, diğer alanlardaki veri mühendisliği projeleri için tekrarlanabilir bir çerçeve görevi de görmektedir. Gelecekteki araştırmalar, bu veri setini finansal ölçümler ve sektörel etkilerle zenginleştirerek analitik kapasitesini daha da artırabilir.

Keywords

• Türk Startup Ekosistemi
• Bütünleşik Veri tabanı
• MongoDB
• Keşifsel Veri Analizi
• Web Kazıma

Supporting Institution

Bu araştırmayı desteklemek için dış fon kullanılmamıştır.

Project Number

FBA-2023-1211

Ethical Statement

Bu çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulduğu ve yararlanılan tüm çalışmaların kaynakçada belirtildiği beyan olunur.

Thanks

Bu çalışma, TÜBİTAK 1505 Üniversitesi Sanayi İşbirliği Destek Programı ve GSU Bilimsel Araştırma Projeleri tarafından FBA-2023-1211 proje numarasıyla desteklenen " Ar-Ge Projeleri Yönetiminde Yapay Zekâ Destekli, Uzmanlık Odaklı, Akademik Danışman ve Yeni Girişimci Firma Öneri Portalı Geliştirilmesi " (Proje Numarası 5240007) başlıklı araştırma projesi kapsamında yürütülmüştür.

Building An Integrated Database For Turkish Startups: A Systematic And Novel Framework

Abstract

In recent years, the Turkish startup ecosystem has grown significantly thanks to the increase in government support, diversification of private investments, the spread of startup culture worldwide, and technological developments. Despite the rapidly increasing numbers, there is no up-to-date, comprehensive, and analytically serviceable database for new entrepreneurial firms in different sectors. This study creates an integrated and centralized database for startups in Türkiye by using a hybrid methodology that combines traditional ETL processes with modern data engineering techniques. All data belonging to the companies were obtained from public databases and national techno-hub pools via the web scrape method and stored in a database on MongoDB, a document-based NoSQL database. While data preprocessing provided consistency, integrity, and structural integrity, exploratory data analysis revealed critical insights into the geographical distribution, fields of activity, and workforce metrics of the startup ecosystem in Türkiye. The findings obtained at the end of the study provide very valuable information to stakeholders, including researchers, policymakers, and firms operating in different sectors. The data pipeline construction methodology introduced in the study, characterized by its scalability and adaptability, also serves as a replicable framework for data engineering projects in other fields. Future research can further enhance its analytical capacity by enriching this dataset with financial metrics and sectoral impacts.

Keywords

• Turkish Startup Ecosystem
• Integrated Database
• MongoDB
• Exploratory Data Analysis
• Web Scraping
• DataOps

Supporting Institution

The author(s) acknowledge that they received no external funding to support this research.

Project Number

FBA-2023-1211

Ethical Statement

It is declared that scientific and ethical principles were followed during the preparation of this study and that all studies used are stated in the bibliography.

Thanks

This study was conducted as part of a research project entitled "Development of Artificial Intelligence Supported, Expertise-Focused, Academic Advisor and Startup Recommendation Portal in R&D Project Management" (Project Number 5240007), which is supported by TÜBİTAK 1505 University's Industry Collaboration Support Program and the GSU Scientific Research Projects under project number FBA-2023-1211

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