Analyst Problems: Should I Learn to Code?
By Gemma JoyceApr 24
Published December 9th 2016
One of the many perks of working at Brandwatch is the sheer volume of incredibly smart women (and men! You’re great too!) I get to spend time with, and talk to, every day.
I feel like I’m constantly inspired – working in the tech industry is never slow, and never boring. I can watch conversations on Slack about machine learning and data science while I’m eating string cheese.
I can chat to analysts working on projects for the world’s biggest brands while grabbing a coffee. I never thought I’d end up working in tech – but, hey, I’m so glad I’m here.
In the next of our series talking to women working in tech about their experiences, challenges and how they see the future, I took some time to speak with statistician, data miner and author, Meta Brown.
— Meta S. Brown (@metabrown312) November 30, 2016
Based out of Chicago, and a Forbes contributor (among many other things), Meta describes herself thus:
I’m author of Data Mining for Dummies, and creator of the Storytelling for Data Analysts and Storytelling for Tech workshops.
My work focuses on two challenges: 1) helping technical experts communicate effectively with everyone else, and 2) providing guidance for organizations launching or expanding analytics programs. I’m a hands-on statistician and data miner who has worked with clients across three continents, in industries ranging from manufacturing to healthcare to law enforcement.
While I’m best known as an expert in text analytics and data mining, my first love is good, old-fashioned statistics, particularly statistics for quality improvement.
For those who like alphabet soup, I hold an S.M. in Nuclear Engineering from M.I.T., a B.S. in Mathematics from Rutgers, CQE, CQA and CRE certifications from American Society for Quality, and CPHQ certification from National Association for Healthcare Quality.
Seriously, seriously impressive.
So, what advice does Meta have for other women thinking of a career in the tech space? And what does she think of the diversity issues the industry undoubtedly suffers? Fresh from her talk at Innovations Enterprise, we found out.
I’m a consultant who helps technical people communicate with non-technical executives and clients.
After a series of clients came to me for help with technical communication challenges, communication became the focus of my business.
Small and effective.
My own consulting and training services.
Many of my clients focus on social data. My job is to help them understand what they can accomplish with it, and how to communicate about it with their own clients or managers.
Often, those who work primarily with social and web data have a weaker understanding of their data and analysis than others in the data analytics space.
Acknowledging that is a fundamental challenge, one that’s necessary in order to move forward.
The more willing you are to accept that weaknesses exist in both the data and your understanding of it, the more easily you can take steps to deepen your knowledge, improve quality and enhance the impact of your work.
Cheap computing power.
It wasn’t so long ago that the possibility of having a computer of your own, capable of performing all the mathematics, document preparation, messaging and other tasks an average worker might need was just science fiction.
I followed the work. I was one of the first women in my engineering graduate program, and I was there for the same reason as my classmates, to gain skills that would enable me to earn a living.
The challenges for women are subtle.
Everybody knows you’re not supposed to say that you’ll only hire a man. But managers can get away with promoting a man over a better-qualified woman, and they do it every day.
My own field of analytics is half women, yet conference speaking rosters skew heavily male.
The software industry has been a safe haven for all sorts of sexist behavior; the stories I hear from young women in the industry are far worse than anything I encountered at the start of my engineering career.
A little sick to my stomach. It’s remarkable to me that we’re still working on this in 2016.
— Analytics Channel (@IE_Analytics) December 5, 2016
For many reasons, keep notes of what you do, who you meet, what you learn, what goes on in your workplace and other details that you observe as you work.
Make a record of the people you meet, and work those contacts. Use notes on things you’ve learned to review and refresh understanding.
Make a record of what you do, and use that information to research the value of your work, and make a case for yourself in performance reviews and when looking for work.
Keep notes when you encounter treatment that’s not appropriate, you may need it when you argue for improvements in your workplace, or when you need to take legal action.
Investors are going to do some thinking about where they’re putting money.
Business models have been neglected for a number of years, on the philosophy that large numbers of users will equate to money down the road, but that’s not always working out. So it’s time to start making business model and exit strategy part of the startup plan once again.