Biggest Challenges Faced by a Senior Data Scientist at Newfront Insurance
Matthew, a Senior Data Scientist at Newfront Insurance, identifies the biggest challenge as gaining stakeholder adherence to data-driven approaches when data quality is lacking, noting the difficulty in explaining data issues and building trust, especially when not dealing with "data advocates"; the goal is to establish credibility so stakeholders don't constantly question the data, enabling real insights and preventing the blame game when anomalies arise.
Data Quality, Stakeholder Communication, Data Accuracy, Building Trust, Data Advocacy
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
Job Functions
Traits
Matthew Slodowitz
Senior Data Scientist
Newfront Insurance
University of Michigan
Mathematics, Data Science, Statistics
Insurance, Technology
Data and Analytics
Greek Life Member
Video Highlights
1. Difficulty gaining stakeholder buy-in for data-driven approaches, especially when data quality is a concern.
2. The importance of educating stakeholders about data limitations, anomalies, and how data quality impacts insights.
3. Building trust and credibility in data to prevent stakeholders from dismissing data insights due to occasional inaccuracies.
Transcript
Q6: Biggest challenge in this role?
One of my biggest challenges in my current role, and I think it applies to many in the data realm, is getting stakeholder adherence when you're trying to be data-forward. This is difficult if the data isn't clean enough.
It's hard to get users to understand how the data works, why there are issues or anomalies, and why the data outputs as it does, without good data quality. I think that's the hardest thing to explain.
When you don't have many data advocates in a company, you get a lot of pushback, which leads to mistrust with the data. The biggest thing is trying to explain that the data is inaccurate because of specific issues, but that we can make it more accurate by adhering to certain rules.
This helps keep the data as clean and accurate as possible, allowing for real insights. When people see what data and dashboards are capable of, they get excited. However, if something goes wrong, they may blame the data.
You have to build credibility to a point where they aren't questioning your data as often. There will be data problems and anomalies, but you don't want them questioning the data so much that nothing gets accomplished.