Women in code series: Denise Gosnell, DataStax

The Computer Weekly Developer Network and Open Source Insider team want to talk code and coding. But more than that, we want to talk coding across the diversity spectrum… so let’s get the tough part out of the way and talk about the problem. 

If all were fair and good in the world, it wouldn’t be an issue of needing to promote the interests of women who code — instead, it should and would be a question of promoting the interests of people who code, some of whom are women.

However, as we stand two decades after the millennium, there is still a gender imbalance in terms of people already working as software engineers and in terms of those going into the profession. So then, we’re going to talk about it and interview a selection of women who are driving forward in the industry.

Denise Gosnell, DataStax

Dr. Denise Gosnell is head of the Global Graph Practice at DataStax. As an Interdisciplinary graduate education research traineeship (IGERT) NSF Fellow, Dr. Gosnell earned her Ph.D. in Computer Science from the University of Tennessee and a Masters of Science, Mathematics: Graph Theory at East Tennessee State University. Her passion centres on examining, applying and evangelising the applications of graph data and complex graph problems. 

CW: How well do you think graph is understood in the software developer space?

Gosnell: In how best to use your data as a graph, I think the biggest issue is not necessarily a misconception, but a gap. When I say that, I specifically see a massive gap in skills between understanding data like a graph and then using data like a graph… and this happens all the time.

We work with customers around the world, who want to explore graph technology generally. You will have your engineering team and some architects at a whiteboard and they will discuss their data problems and drawing out that some data is in a siloed system that links to data in another siloed system. They then quickly, as a team, realise that they might have a graph that is within their data within their enterprise.

Usually, you will segment a team that’s going to explore using graph technology to solve that problem.  But, that team quickly discovers there’s a massive and steep learning curve around implementing that graph database or graph structure. That steep learning curve comes from the ease at which they understood it at the whiteboard to actually using it in practice. To me, that’s the biggest misunderstanding that we have right now in the graph industry – it’s just navigating that big gap and having more tools to navigate the steep learning curve from idea to production application.

CW: So there’s a skills shortage essentially?

Gosnell: To me, it’s not exactly just a set of skills. There’s a mindset shift that we need to educate people about and, yes, that’s a skill, but it is also a new way of thinking.

Specifically, this is a mental shift from thinking about the entities or objects in your data to prioritising the relationships across your data. We call this evolving from entity-first design — to relationship-first design.

With more advanced and mature technologies, sometimes skills can be developed by taking a few courses and becoming certified in a certain technology. We’re not exactly there yet in the graph world. We’re not at a point where if you need to become good at a certain API there’s exactly a course for it. More work needs to take place across the industry to develop understanding around graph.

CW: Do you think diversity is an issue in software and around graph specifically?

Gosnell: First off, I haven’t seen any data. Whenever I talk about diversity, I like to take a data-driven approach and see what types of surveys have been out there and what the respondents are saying. The 2019 Stack Overflow survey came out recently and all of the data uniformly across that survey is still indicating that we have a massive gap in diversity across tech.

Within the graph industry specifically, I have found personally – and this is more anecdotal and not data-driven – there are more women involved. I don’t have data-driven statistics today, but it would be pretty interesting to specifically attempt to have a survey to try and address that.

One thing that I can speak to is the data I am privy to see in enrollment trends in one department at one university. In 2012, I was the founding president of a group called Systers that had the mission to recruit, retain and mentor women in Computer Science at the University of Tennessee, where I am now an Industrial Advisory Board Member. What is interesting to note is that while the department has seen 3x growth in enrollment, the percentage of female students has not seen the same growth.

CW: In your day-to-day, do you work with a lot of women or have women colleagues who focus on graph in the enterprise software space? Plus, has that evolved in the last few years?

Gosnell: In the technical field here at DataStax, there are quite a few women in leadership positions and we do a lot of coordination on projects across the company. There are many women that I have the true honour to work with every day to help solve different problems for large companies around the world, where some of their teams are led by women who are solving graph problems within the very largest enterprises and organisations.

However, without that real data-driven survey, in my opinion it’s hard to really put a metric on the ground. From my purview we’re doing a decent job, but you can always do better.

CW: Did you have a female mentor yourself or someone who helped you navigate being a woman in enterprise software?

Gosnell:  It hasn’t necessarily been just one person – I’ve had a lot of mentors, both male and female – but the person who opened up my eyes to the world of graph theory was Dr. Teresa Haynes who was my Master’s advisor. She is extremely well renowned in the academic graph theory space. I started my work in graph theory with Teresa Haynes as one of my direct advisors in addition to Dr. Debra Knisley. So, at the core of my foundation when I got started, there were two very prominent female researchers who really helped highlight and pave the way for me.

CW: Is there one piece of advice you could give to the upcoming generation of aspiring women leaders in technology?

Gosnell: Out of context this could always get a little hairy, but in all honesty, one thing that I have always done is to ask for forgiveness, not permission. When I see opportunity and when I see a project that’s going to bring value to my company or to whom I’m working with, I go about getting as far down the process as I can to essentially create a proof of defensibility of its usefulness so that you can get that done.

I often spend time with those in my network talking about the differences in how men and women approach challenges all the time. We commonly see, in both professional and athletic environments, that there is a stark difference in how men and women approach problems and learning. Anecdotally, we find that men are willing to try anything regardless of how they appear during the process. On the other hand, we find that women want to understand the correct result before stepping up to the plate.

From my experience, the focus on the correct destination instead of the lessons learned along the journey inhibits the learning process and professional development. The focus on destination perfectionism, regardless of gender, is holding individuals back from learning, making progress and gaining experience. Though not exactly 50/50, I see this falling on a gender divide and it is detrimental to an individual’s progress.

CW: What will success look like for you in the future? What are you most excited about for the next year?

Gosnell: In the next nine months or so, I can’t wait to get my book finished and out the door. Dr. Matthias Broecheler my co-author and I are collaborating on an O’Reilly text to explain the mindset shift and implementation details of graph technology that we talked about earlier. Completing this book together is going to be a huge achievement that I started driving last year.

Longer-term, I think that graph data and graph technology has the real potential to be the next wave of innovation that’s going to transform the tech industry and there’s a lot of work between now and graph technology really solving those problems that we know that they can solve.

I’m really looking forward to graph technology solidifying and finding its place as a tool that’s mature. That’s a much bigger objective that I would see coming into play three to five years from now.

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