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Özge Aykaç

Özge Aykaç

Industry Analyst
47 Articles
Stay up-to-date on B2B Tech

Özge is an industry analyst at AIMultiple focused on data loss prevention, device control and data classification.

She is a member of the AIMultiple DLP benchmark team and evaluates the effectiveness of the top DLP providers.

Latest Articles from Özge

DataAug 25

Data Labeling for NLP with Real-life Examples in 2026

NLP technology is increasingly being used to enable smart communication between people and their devices. Companies like Google, Amazon, and OpenAI have invested billions in NLP technologies that can understand, interpret, and generate human language with remarkable accuracy. However, behind every sophisticated NLP model lies an important foundation: labeled training data.

Enterprise SoftwareAug 21

Top 5 Open Source MDM Software in 2026

Mobile devices are a significant source of business data breaches. While some companies require sophisticated closed-source MDM software, others prefer open-source solutions to protect their devices.

Enterprise SoftwareAug 13

Digital Transformation for Telecoms with Case Studies ['26]

The telecommunication or telecom sector is a ~$1.5 trillion market that makes communication possible worldwide. As remote work becomes more widespread, consumer needs continuously change in the telecom sector, demanding better services from telecom service providers. Like every other sector, the telecommunications sector can also benefit from digital transformation to improve its services.

AIJul 25

7 Steps to Obtain Computer Vision Training Data in 2026

Computer vision (CV) technology is advancing rapidly in various industries. As demand for computer vision systems rises, so does the need for well-trained models. These models require large, high-quality, accurately labeled datasets, which can be costly and time-consuming to collect.

DataJul 25

Automated Data Collection Tools & Use Cases in 2026

Automated data collection involves using automated systems to gather, process, and analyze information efficiently. Since automated data is produced from multiple sources and comes in various formats, understanding the different types of data and their origins is crucial for effectively implementing data automation.

DataJul 22

Top 6 AI Data Collection Challenges & Solutions in 2026

AI adoption was slightly lower last year (Figure 1); one reason could be the various challenges in implementing AI. Training data collection has been identified as one of the main barriers to AI adoption. To avoid data-related challenges, businesses are opting to work with AI data collection services.

AIJul 19

LLM Data Guide & 6 Methods of Collection in 2026

In the expanding AI and generative AI market (Figure 1), large language models (LLMs) have emerged as pivotal. These models empower machines to generate human-like content, heavily reliant on quality data. Here, we present a guide for business leaders on accessing and managing LLM data, offering insights into collection methods and data collection services.

DataJul 19

Top 4 Field Agent Competitors & Alternatives in 2026

Monitoring retailers across different countries can pose a challenge for manufacturers of CPGs (consumer packaged goods) and FMCGs. Planogram audit services or retail audit companies offer solutions to help overcome these challenges. Field Agent is a service provider specializing in retail monitoring services for consumer packaged goods (CPG) producers.

DataJul 9

Top 4 Facial Recognition Data Collection Methods in 2026

Despite the controversies surrounding this technology, the facial recognition systems (FRS) market continues to grow. Facial recognition applications are everywhere, from helping improve mental disorder diagnoses to finding fugitives. Developing and improving these systems requires facial data, which sometimes can be challenging to obtain due to security and privacy-related concerns of people.

DataJul 3

Data Quality Assurance with Best Practices in 2026

Optimal decisions require high-quality data. To achieve and sustain high data quality, companies must implement effective data quality assurance procedures. See what data quality assurance is, why it is essential, and the best practices for ensuring data quality.