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Putting Quality Over Quantity Is The Key To A Successful Data Platform

For Biscred Product Manager Margaret Campbell, putting quality over quantity is the key to a successful data platform.


Courtesy of Margaret Campbell | Biscred Product Manager Margaret Campbell at Priest Lake in Idaho.
Courtesy of Margaret Campbell | Biscred Product Manager Margaret Campbell at Priest Lake in Idaho.

While the key to creating a useful, comprehensive database may seem to be to collect as much data as possible, that isn't the case. Large, overwhelming datasets may not meet the needs of a target audience. The true key, according to Biscred Product Manager Margaret Campbell, is to keep your audience in mind and to balance quality and quantity.


“You need a lot of grit to work with data,” Campbell said. “Building datasets requires a lot of patience and flexibility, and you learn from the data as you work with it.”


Campbell is one of three product managers at Biscred, a prospecting platform that specializes in commercial real estate-related data. Sales and marketing teams that work in industries within and adjacent to CRE use Biscred to get in contact with the right decision-makers to facilitate sales transactions.


In a conversation with Bisnow, Campbell shared the benefits of working in a startup environment, her approach to data collection at Biscred, and what advice she has for job seekers who are looking toward a career in the data industry.


Bisnow: What inspired you to pursue a career at Biscred? What about the company's goals drew you in?


Campbell: Before I was at Biscred, I was at a large software as a service, or SaaS, company working in data operations. It was a very established company, and unless there was an opening to move around and get involved with a different data process, you more or less stayed in your original job. The delineation between data operations and product was also very clear. They were two totally separate teams that didn't really interact very much. I had a counterpart on the product side but was not involved with the product roadmap.


I knew I wanted to stay in the SaaS industry but have a position in which I was more aligned with the product side. At Biscred, there’s a greater alignment of the data operations team and the product team, since they are housed under one leader. I think that setup is really critical for a data product. There needs to be consensus and coordination between how data is collected at the top of the funnel and how it is categorized and portrayed on the front end. 


Biscred is a smaller team with a startup product. I thought it would be really interesting to be able to see something grow from the beginning stages. I was interested in the type of trial-and-error work that generally only the people who are at the company in its early stages get to do.


Bisnow: Your previous roles involved project management and data collection. How have you applied what you learned from those experiences to your current role?


Campbell: I gained a lot of experience in my previous roles, specifically in regard to project management and data operations, that have benefited me as a product manager. Being in this role is about collecting perspectives, creating alignment between strategy and details, and clearly communicating priorities. You have to understand how the clients will use the data and why they want it. Knowing these aspects allows you to best determine how to identify and portray the information with the greatest impact. Working in data operations previously helped me decide what data is best collected and transformed by a researcher as well as what data can be collected and transformed programmatically.


Bisnow: What are the current goals for the Biscred platform? How have you been supporting your team in achieving those goals?


Campbell: My goal is to improve publishing counts in the platform while being very conscientious of the trade-offs between volume and quality. I want to guide conversations around how those two elements may compete, where they align, and how we can maximize both. 


You can have an aggregate number of contacts published, and it can be an exciting number, like 500,000 or 800,000, but most of the time, people who are looking at a platform are looking for a very specific subset of our contacts. They're only interested in restaurants in a particular region, for instance, and they don't care if we have half a million people on the platform as a whole. They only care about people who meet that criteria.


Bisnow: What do you enjoy most about working in commercial real estate and being part of the Biscred team?


Campbell: Commercial real estate is an interesting industry, and it impacts everyone's day-to-day lives. I found that engaging with CRE data just feels like I'm studying part of the world, and it doesn't feel niche or academic. 


I enjoy how agile we're able to be at Biscred. From the time you have an idea to the time you're able to implement can be really short. As long as you're able to make a good argument for why you think resources should be put into it, the team allows you to move forward with your idea and test it, which is really exciting. It's easy to stay engaged when you have control of your day-to-day like that.


Bisnow: If you could give one piece of advice for candidates interested in working with data, what would it be?


Campbell: When you're working with large datasets, nothing will ever be perfect. But at the same time, you have to be driven by perfectionism. You have to want to achieve it, but you also have to understand that when you're working with millions and millions of records, it’s unrealistic to expect that everything's going to be correct, so you have to know when to stop.


For example, if you are evaluating a data pipeline, there’s always going to be an edge case of some record that fails. If you're looking to import millions of records and you have a field that's working 98% of the time, it can be tempting to say, “I'm just going to fixate on this and figure out what's happening that last 2% of the time.” But that approach is not driving value to the client, nor is it driving value to the internal data collection teams.


You have to understand how you define your quality standards and represent them to other people in a clear and convincing way.

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