In this edition of WOW Women In Technology, Scott talks about her approach to leadership, the barriers women face in the field of data, and the impact data has on artificial intelligence (A.I.).
What sets Kristen Scott apart from many of her colleagues in the field of data management and analytics is her background in anthropology.
“A lot of anthropology is understanding why certain systems are in place, and the building blocks of what created that,” Scott says. “That goes hand in hand with data.”
Since joining Whereoware as our Senior Director of Analytics, Scott has enhanced our team with her expertise and unique approach to data.
Scott continues our proud tradition of a majority-woman workforce, particularly in the male-dominated tech industry.
How did you get into data as a career?
I actually started out doing search engine optimization.
And through that, I really got invested in working with analytics tools, building out search engine optimization strategies. We were working with a lot of analytics tools to look at trends and how different keyword categories performed.
I found myself enjoying working with those tools - seeing how the strategies evolved over time - and that got me embedded a little bit more into working within analytics and data in general.
After that job, I went straight into analytics. I had a role as an analytics implementation specialist, and that expanded into data management and compliance.
What is one of the most valuable lessons you learned during your career?
The most valuable lesson I've learned in my career is to be flexible.
After working on multiple projects, with different clients, across various roles, you see that even the best plan can go awry, and that's why it’s important to be flexible.
When you're flexible with your strategy and how you take on a project, you know when and where you can pivot, and where you can provide value.
That makes you comfortable in your recommended solutions and gets the best outcome possible for the client.
What I take to heart is really, no matter what happens, be flexible and find that end solution.
Where do you find inspiration as a leader?
I find inspiration in a lot of different places. How I use it within leading the team is that I have these aspects:
- Treat people with kindness and respect.
- Always understand the conversation that you're in.
- Always come in with solutions.
I think that not only has to do with the workforce, but how you take on your life and how you work with people in your life, whether that be family, acquaintances, or friends.
What would you say is the biggest misconception that people outside of your field have about data?
The biggest misconception is that there's a machine running it all.
Within data, there's a time and place for AI and machine learning - a need and growth in terms of what those strategies can present and the outcomes they can impact.
But, that's just a subset of data and the data practices out there.
There are people embedded in every data process really making sure it's running correctly and set to the spec that makes sense. I think that's the biggest misconception, that it's all done by machines and that there's really no human aspect to it.
Really, there's a big human aspect within it.
The more people feel comfortable with data, the more they'll understand that really it's driven by people.
What kind of data do many companies have that they're simply not leveraging?
There is a lot of owned data within systems that can help build strategies, make more personalized content or experiences, because it's data that a customer is allowing you to have.
I would tell our clients or other companies out there to really look at your first-party data stores - what you have available, and how you can embed that into your strategies today.
It's relevant to your audiences. They're your customers. So, you should use that first-party data to provide the most relevant experiences, so they can appreciate the value you're providing them.
What do you think is the most significant barrier to female leadership in general?
The biggest barrier is representation. We don't see a lot out there, whether it be on LinkedIn, The Harvard Business Review, etc.
Look at any big piece of promotional material on companies or individuals making their mark in the world of data, and it's rarely revolved around women, what their process was, and how they brought their leadership skills to a project or piece of work.
So, the more we see representative content out there, the less shocking or uncommon it will be to see women really do well in data.
Women have been in data for a very long time and can create an impact, and I think that the value of women in data is astronomical.
It really has to do with bringing in different sets of ideas, and how we can utilize that to get to a better state, a more creative process, or a new process in general.
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