Ali: Please describe general developments in the technology and how your company will contribute to the field.

Yilmaz Tekirdag: Our work is based on databases and data analysis technologies, and we are specifically interested in graph databases and its applications, albeit our area of operations covers a wide variety of data solutions if customers ask for them. Graph database is a type of database where we keep data in the form of nodes and edges, unlike the traditional database, where data is stored in tables. Data of most institutions is stored in the latter way, but graph databases are advantageous in operations like relational data search in comparison. So, we provide institutions the tools to see and analyse their data in graph database visually within their currently existing traditional databases. This allows them to benefit from the graph database while also not losing their current databases and its advantages.

Kenan: What are the main applications for this technology?

Yilmaz Tekirdag: Any institution which has or uses big data is a potential customer for data technologies, and most of today’s institutions fall under this category. They have the data of their workers, the data of their customers, the data of their financial operations, the list goes on. All of these data can be analyzed and the outcoming results can be used to improve their businesses, which is what data technology is mainly concerned. These can be done using traditional databases or graph databases, their corresponding query languages and visualization tools, and more recently, through the integration of machine learning. The latest work of ours is related with that as well, we are producing a data analysis tool to determine the efficiency of employees in a ministry, using their work related data, and machine learning is an integral part of this tool.

Ali: What are the risks and benefits of this technology?

Yilmaz Tekirdag: The most important benefit of this technology is to provide us a balance of new opportunities and individual values. We use an evaluation system called Privacy Impact Assessment (PIA). For the nonsensitive and unidentified data, this system help us to understand how personal inf ormation can be used, stored, shared out and also decreases the risks for privacy. The biggest and widespread risk is the privacy. We generally go to the companies and work in there for the data analysis process so their data is in their company so there isn’t an outdoor data sharing for the companies. However, the public is too hesitated to give data about themselves that is another risk and lead to a decrease for the reflectivity of data.(less reliable data because of not having lots of shared data).

Kenan: What sort of modifications will be made to the technology in the future?

Yilmaz Tekirdag: The transparency into automated processing operations and decision-making processes should be increased by using relevance factors and the profiles in marketing for example. For the privacy issue, the problems are coped better in the design stage and these processes are worked by organizations to look for privacy issues repeatedly.