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Hazal Şimşek

Hazal Şimşek

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

Hazal is an industry analyst at AIMultiple.

Research interests

Hazal focuses on

  • process intelligence including process mining
  • enterprise automation including IT automation and low-code no-code (LCNC) automation

Professional interests

She has experience as a quantitative market researcher and data analyst in the fintech industry.

Education

Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.

Latest Articles from Hazal

Enterprise SoftwareAug 22

Oracle EBS: Features, Pros & cons in 2026

Although Oracle Fusion Applications (Oracle Cloud ERP) was released in 2011, many businesses still rely on Oracle E-Business Suite (EBS), as shown by the Google Trends graph, which highlights the ongoing popularity of Oracle EBS compared to Oracle Cloud ERP (see Figure 1).

Enterprise SoftwareAug 15

Best 14 Process Improvement Techniques in 2026

Companies use process improvement techniques to identify bottlenecks and improve them in a process flow based on their process scope and objectives. Yet, these techniques have been losing popularity with the emergence of process intelligence tools.

Enterprise SoftwareAug 14

Compare Top 12 SAP Utility Solutions in 2026

1,500+ companies utilize SAP utility solutions like SAP IS-U, one of the leaders in utility Customer Information Systems (CIS). Yet, adoption still faces significant challenges, primarily due to integration issues. The main hurdles include: Therefore, we recommend users to implement SAP utility solution that can integrate with SAP utilities module.

DataAug 6

Best 6 Real-Life Enterprise Information Management Examples

With global data volume projected to hit 149 zettabytes by 2045, organizations have more data than ever. However, 60-70% of enterprise data remains unanalyzed, limiting its potential value as ML stats show. Understanding enterprise information management examples can help businesses turn raw data into actionable insights, improving decision-making and efficiency.

Enterprise SoftwareJul 26

What are 5 Best Process Mining Algorithms to Consider?

Process mining algorithms are examples of how machine learning process mining applications can facilitate process discovery. TThey help clean the required data and generate process models with different strengths and weaknesses. Technical professionals and developers must decide which algorithm to use based on the data and models of the processes they want to automate.

DataJul 26

Data Curation in 2026: Key Concepts and Best Practices

Data curation is an important part of data management. Data curation is the process of collecting, wrangling and preserving data. It allows companies to store sustainable and accessible data to share and apply self-service analytics. Data-driven insights are crucial as data-driven sales strategies enable companies improve their sales productivity by 20 %.

Enterprise SoftwareJul 18

Process Visualization: 8 Use Cases & 13 Best Techniques

According to neuroscience studies, people tend to transmit information by 90 % more in a visual format, which is why they can follow directions in the visual format 323% better than text format.

Enterprise SoftwareJun 11

Top 11 Free Trial & Open Source Process Mining Tools ['26]

Software specifications and requirements are different in each industry, but it is important to choose the correct software. 52% of business leaders identify the lack of right process mining solution or expertise as the key problem behind their low rates of adoption, according to process mining stats.

Enterprise SoftwareMay 15

How to Improve Meter-to-Cash Process in 10 Helpful Tips

In the EU alone, adoption of smart meters have reached up to 72%. Such adoption rates may positively impact the meter-to-cash process by facilitating more accurate consumption data and reliable service.

Enterprise SoftwareApr 28

Discover Latest 6 SAP Datasphere AI Integrations in 2026

SAP Datasphere is AI tool that can help deliver meaningful data to the enterprises that struggle to utilize 60% and 73% of their data for analytics, as the latest big data statistics indicate.