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ML Culture, ML Achiever

ML Achiever: Allison Nakagawa, Senior Data Manager

 

 

Allison Nakagawa

Allison studies patterns and trends … as a data manager and in her outdoor pursuits … to solve problems and find the best path forward. 

Congratulations on being named the latest ML Achiever. Can you tell us a bit about yourself? 

Thank you!! I live in Los Angeles with my husband, who is a pilot, and my teenage daughter. I am an outdoor sports enthusiast and especially love kayaking, and mountain biking. I enjoy traveling, so being married to a pilot is a big plus!  

I'm also a bit of a Monopoly addict. My daughter and I pulled out the board when the COVID lockdown started and have played ever since. I've learned a lot about her and from her as a result. For instance, the power of persistence. She often wins because of her persistence, and that is a quality I'm applying more myself. She is always the dog, and I'm always the iron.   

[Author's Note: Interesting that Allison chose the iron. Marketplace had this to say about the traits of people who play with this (sadly now retired) token: "Iron players are "pluggers" — they persist against long odds and often prevail. They handle adversity well."] 

And I am an avid reader striving to finish the "100 Greatest Novels of all Time." The list evolves, and because of that, I doubt I will ever actually finish it. I've read some duds, but I like that the list forces me to try something I wouldn't have initially picked. I just finished Invisible Man by Ralph Ellison.  

What led you into a Data Management career?  

I have a degree from Ohio State University in Applied Math. I am very "number" oriented and have an affinity for analyzing data and finding trends. I really enjoy studying data and understanding the story it tells to solve problems for clients.  

Interestingly, I think that is why I enjoy kayaking so much. It's very analytical as well. You must create a solution after reviewing the data points like the pattern of the water and your own ability. I like to break a rapid up into parts. For example, if I can find a calm spot in the middle where I can pause, it gives me an opportunity to re-evaluate my situation.  Perhaps I can see more of the rapid that way, or the view from a different angle changes my opinion.  These additional data points I collect help me confirm my plan. Or sometimes I realize I am in trouble and need a plan B.  I use similar strategies when reviewing datasets and find there is a benefit to evaluating frequently throughout the project as often new data points bring new perspectives.  

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What led you to ML?  

I had worked in market research for 15 years. My employer was terrific and so successful that they wound up being acquired. I like to think I played a role in their success! After the acquisition, I decided to work remotely to allow me the freedom to indulge my other passions.  

And that is what brought me to ML. I discovered an opportunity on a job board to work with them. The role sounded like a great fit from a lifestyle perspective, providing flexibility and freedom to work from any location and not be tied to one spot. Once I visited the website and saw the tenure of the team members, I was pretty confident this would be a good fit. The fact that others who seemed very much like me had stayed so long was a great endorsement.  

ML's core values, too, were appealing (Achievement, Commitment, Improvement, Fun.) Reading the bios, I felt I had much in common with the team. I also felt that I would have an opportunity to invest my data management skills in ML and its clients, and that similarly, they would invest in me. I'd have an opportunity for continuous learning and a voice to make a difference. So far, that has proven to be the case! I've created a process to standarize data cleaning and am working on a view that will support comparing results to KPIs in real time.  

“Allison embodies the ML Achiever. A quick-thinking problem solver, she has a unique perspective on data and a passion for trying new things. Before Allison came to ML, we used a variety of methods within HubSpot to ensure data integrity. While it worked, it was not easy to quickly see the status of data maintenance. Allison developed a dashboard that moved processes originally housed in lists, saved views, and reports into one location. Now, we can easily see areas of the data that need attention. This is a huge time saver and helps keep our whole team on the same page. We have implemented Allison’s process with every one of our clients. A couple of months after we initiated her improvements, HubSpot’s annual Inbound Conference featured a session called HubSpot Portal Maintenance. Hosted by Gem Rugg-Gunn (HubSpot Strategist at Babelquest), it touted the use of dashboards for data maintenance. Allison could have co-hosted that session!”
Erin Studstill, VP Operations and Technology 

 

Let's talk about data as it relates to supporting a B2B sales organization. 

Sure, there was lots for me to learn when I started at ML about B2B sales, and this particular lesson was a big one. While data management is very process-oriented, you must also be flexible and open to interpreting more than just the data. Other drivers must be factored into the equation, like our client and their offerings, the industries they support, etc. Every situation is unique and you have to keep an open mind. Don't make assumptions; look at your data through a number of different lenses.  

Here's an example from a recent project. Our sales team reported that they were being referred to individuals in accounts we originally did not consider our target contacts. We thought we should speak with medical administrative professionals but were referred to the information technology group. Our initial thought was that we had been off course in our assumption of who the buyer of our client's offering was. However, on closer analysis and team collaboration, taking into account all of the intel we had gleaned through our close working relationship with the client, we realized that was not the case. The issue was actually our messaging. We quickly pivoted and revised our emails and phone scripts and are already seeing improved engagement with the original target audience.  

One thing I think is critical when working with data, regardless of the type is context. Collaboration to bring context into view is something I believe ML does exceedingly well. When it comes time to report data, it’s not just me sitting there with a lot of numbers. That is definitely part of the process, but the other element is gaining intel from the entire team. We have collaboration meetings as a standard part of the project timeline. Those meetings help meld together the extensive experience each team member brings as well as their unique viewpoint. I feel my job is a lot easier, and the findings are more accurate when I have the backstory behind the numbers. 

“Collaboration to bring context into view is something I believe ML does exceedingly well. When it comes time to report data, it’s not just me sitting there with a lot of numbers.” 

 

Thank you, Allison. Are there any additional tips you would like to share? 

Yes; I am a big fan of benchmarking in support of uncovering data trends. ML has many benchmarks in place, allowing me to do a deep analysis. I like comparing month over month. I also compare the data from when a project first starts, and then again after we have made a pivot (like in the example above). This allows me to see if we are getting the expected results from the pivot. If not, perhaps it's time for another.  

For those interested, I recommend the book The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford.  One of the rules is identifying ways to put data in context and I think trending and benchmark data is a great way to do that. 

I have found that, just like in the game of Monopoly, you must be persistent in your data analysis! 

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