Data analysis is an essential part of modern life, with countless applications ranging from healthcare to finance. However, it can be difficult to make sure that data analysis is conducted in an ethical and responsible way, as well as being effective. This is especially true when making decisions that require balancing human and system-based considerations. A topic of growing relevance and interest, particularly amongst PhD students looking at data analysis problems.

One such doctoral student is Sowmya Sudhakaran, who recently published a paper in Frontiers on “Human-Computer Interaction: Unifying Human and System Responsibilities in Data Analysis”. In her paper she argues that it is important to strike a balance between human-controlled and system-controlled decision-making when conducting data analysis — rather than emphasizing one over the other.

Sudhakaran’s research focuses on AI methods for analysing large datasets. However, she felt that these methods weren’t enough; she believes that humans must also be involved in understanding possible ethical implications of any decision or action taken. By including moral diplomacy and related theories in her research process, she was able to dynamically consider both the human and machine parts of data analysis.

Sudhakaran’s work reflects the reality in our modern data-driven world which requires us to apply a multi-disciplinary approach to technical projects. Her research found the importance of implementing guidelines around potential ethical issues that arise and how they can be addressed through better communication between humans and systems across all forms of data analysist.

The complexity of data analytics means that it’s not enough to just analyse what’s present — we also need to understand how best to use technologies responsible and humanize them as much as possible. Sudhakaran concluded her work with the affirmation that it is only by taking all available elements into consideration that responsible, balanced decisions can be made when it comes to big data analysis projects.

The advent of big data has changed the way industry professionals look at data analysis. No longer limited to traditional models, data scientists are now responsible for performing both human and system-centric activities for comprehensive results. For PhD student Leonard Smith, this is exactly the challenge that he strives to master.

As a PhD candidate in business analytics from Cornell University , Leonard understands that the success of any data project relies on both the human as well as computational elements. Enriched by his prior professional and academic experience, Leonard believes a successful effort will require a combination of AI technologies, predictive analytics and process optimization. To that end, Leonard has made it his mission during his PhD program to master the skill set necessary to apply and combine both human and system working principles in data analytics projects.

During his studies, Leonard worked closely with faculty mentors to develop an advanced skill set as a data scientist. His primary focus is on creating systems that provide solutions that can be used by decision makers to make strategic decisions with more clarity and precision. To further this mission, Leonard has worked alongside computer engineers and developers to gain advanced knowledge about how system processes work, allowing him to apply those findings to help improve how big data is leveraged in organizations.

In addition to conventional university learning, Leonard has also increased his knowledge base in other ways. He recently co-founded an analytics consultancy organization called DP Analytics Group ( This company provides a platform for businesses to access industry expertise with regard to data processing and analysis solutions. Through this experience, he has further advanced his technical acumen about how humans interact with computers for better decision making.

In sum, through combining rigorous study alongside real world practice Leonard Smith is striving to become a well-rounded scientist capable of extracting value from leveraging both human and system working principles into data analytics applications. By mastering this difficult balancing act between using humans versus machines effectively he could one day be a game changer when it comes to executing a truly impactful data project.