Interview with Meta S. Brown, Business Analytics Consultant at A4A Brown Inc., Management Consulting

Meta S. Brown is a business analytics consultant with over 20 years of experience in the field of data mining, statistical analysis, and predictive analytics. She is President of the Chicago-based management consulting firm A4A Brown, Inc. Her primary focus is helping technical people communicate with executive decision makers. She is the author of Data Mining for Dummies, and the creator of “Storytelling for Data Analysts” and “Storytelling for Tech” workshops. She is also a frequent contributor to Forbes and has written a series of blog post for LinkedIn titled “Meta’s Binders Fulla Women in Analytics.” She is the author of numerous technical guides on subjects like neural networks, quality improvement, statistical process control, and other statistical applications.

Ms. Brown holds an S.M. in Nuclear Engineering from M.I.T., a B.S. in Mathematics from Rutgers University, and numerous certifications, including CQE, CQA and CRE certifications from American Society for Quality, and a CPHQ certification from National Association for Healthcare Quality.

Interview Questions

[OnlineEducation.com] Because analytics is a relative new field, you find people in the profession who studied math and statistics, computer science and programming, or maybe business. You got your master’s from MIT in nuclear engineering. How did that lead to a career in analytics?

[Ms. Brown] Many people have looked at my degree and the work that I do, and interpret it as a dramatic career change. It’s not as dramatic as it might seem. What I have done throughout my career is applied statistics. So, as an undergraduate, I studied mathematics, and that included a great deal of statistics. In graduate school, I studied engineering, and within nuclear engineering a very important discipline is probabilistic risk assessment. It involves using probability theory in order to imagine the structure of a complex system — in this case a nuclear power plant — and assess the risk of failure of each part of that complex system. A person who is familiar with statistics is also familiar with probability theory and can use that kind of knowledge. I had also studied biology and chemistry, and my knowledge in those areas was helpful for understanding things related to radioactive waste and its environmental impacts. So I was taking things that I had already studied — mathematics, biology, and chemistry — and applying them to the field of engineering, which is really applied mathematics.

When I went to work, I continued to look at real-world problems that could be addressed mathematically, and gradually I realized that good statisticians were harder to come by than people who understood accidents in nuclear power plants. So, I found myself doing applications of statistics that moved further and further away from nuclear engineering.

My main focus today is with data analysts. They may call themselves data scientists, web analysts, or many other job titles. The issues they bring to me aren’t always mathematics problems. Most often they are business and communications problems related to analytics. For example, “Our customers do not understand what we are offering them.” Or, “We have a team of sophisticated data analysts, and we don’t understand what they are telling us.”

[OnlineEducation.com] So, you use you use your background in statistics to understand what is being done on the analytics side, and you communicate that across an organization or enterprise. Is that a fair assessment?

[Ms. Brown] Yes, that is right. It is absolute crucial in what I do to have hands-on knowledge of data analysis, statistics, and engineering, and to also be able to tell stories about data. I’ve taught courses in data storytelling, and I’ve found that there were two groups of people using the term “data storytelling.” To some, data storytelling means data visualization — in other words, graphs. For me, and many others, data storytelling means bringing out the human experience behind the data, in story form – not math, not graphs, but real stories. There are folks out there who are expert storytellers, but who have no technical training, and the risk is that the stories they tell may not be completely true. A good data storyteller must have a clear technical understanding of the data, plus the ability to get the point across in terms that decision makers understand. That’s my niche in this industry.

[OnlineEducation.com] It sounds like one thing you’d point out to women considering a career in analytics is that it’s not just about crunching numbers; it’s also a field in which having the skills to explain the results of quantitative analyses is highly valued.

[Ms. Brown] I would emphasize that it’s not just about being able to communicate the end results. Communication is extremely important from the beginning of the process, because if you don’t communicate well at the beginning, then it’s difficult to do the work properly. Good analytics begins with a clear understanding of the problem that is being addressed. We don’t often solve that problem in one pass. If you don’t begin with a clear understanding of the problem, and get all of the constituents to agree on a statement of the problem that they are all comfortable with, you’re not going to have happy people at the end. I would also say, the communication skill of listening can be even more important than the communication skill of sharing results. Listening involves being able to think through what you hear, asking probing questions, and listening to the responses. This is a learned skill.

[OnlineEducation.com] Are there particular concerns or issues that women should take into account if they are considering a career in analytics?

[Ms. Brown] The first thing that I would want people to know is that a woman in analytics is as common a creature as a man in analytics. To understand that, you have to avoid getting too wrapped up in titles. There are reasons why one job is called a data analyst, and another is called a data scientist, and another is called a statistician, and so on. Those reasons are as political as they are technical.

Data science and data scientist titles are now in vogue. This is coming from the computing world. And the computing world has been pushing women out for the past thirty years. So, we’re letting the term be defined by people who are tech- and programming-driven, rather than people who have an understanding of business, communications skills, and what people tend to call soft skills. It’s just bad terminology. Technical communications skills are as valid a skill as any other technical skill.

[OnlineEducation.com] Do you view the terminology or the culture surrounding data science is an impediment for women who a seeking a career in analytics?

[Ms. Brown] Let me back up a bit. Many different titles are used for people working in analytics. What is happening is that ideas about analytics are being filtered through data science and its conception of analytics. That’s where we need caution. I think it’s important, especially for new people entering the field, to realize that “data scientist” is one of the many, many, many flavors of data analysis experts who go by as many as a hundred titles or more, many of which don’t fit the Silicon Valley concept of a data scientist. Analytics encompasses a much broader set of skills than the current conception of data science would suggest. Data scientists are ordinary mortals who are very good at certain things. Data science comes from a community of programmers — that’s their strong skill — and that can imply a lot of other things.

[OnlineEducation.com] What are the practical implications of that trend for a woman interested in pursuing a career in analytics, whether it’s as a data scientist in some other area of specialization?

[Ms. Brown] Here’s what I would say to anyone: if you have an interest in analytics, that’s not a one-to-one match to a graduate program in data science and a job with a title of data scientist. An interest in data analysis can be a pathway that leads into a variety of other studies and an even wider variety of careers. For example, let’s talk about the real women I have been profiling in my “Binder Fulla Women in Analytics” posts. In one post, I chose women who had written books about analytics. I was trying to build awareness of highly qualified women who should be prospects for speaking at conferences, just to point out how many there are. I found 262 women who had written books on analytics. If you looked through to see their job titles, many of them were academics, and if you asked these women what field they worked in, very few would have said, “I’m a statistician,” or “I’m a data miner.” Many of them would have said, “I’m a teacher,” “I’m a psychologist,” and a whole host of other fields. Many of the most sophisticated statisticians I’ve worked with have had degrees in psychology or sociology. It’s similar to me studying nuclear engineering. I worked with statistical analysis, but when I finished my degree I would have called myself a nuclear engineer rather than a statistician. A lot of the people doing valuable, applied work in data analytics don’t call themselves data scientists and they may not even call themselves data analysts. They identify themselves by the application.

[OnlineEducation.com] You see data analytics as an approach to solving real-world problems empirically, with statistical models and applied mathematics. And, your point is that analytics has applications in fields like healthcare, the financial sector, social services, public policy, and any number of other places where people might not call themselves data scientists.

[Ms. Brown] Right. After all, for most of us, the application is more important than the way that we get there. The application is what motivates us to do the rest of it. Some people like sewing for itself, but most of us are interested in having a pair of pants at end of the process. You may enjoy the process, but there is a goal. We cook, we sew, we do statistical analysis, and we may love the process, but we do these things with the aim of having something valuable at the end.

[OnlineEducation.com] I believe it’s your contention that over half of the analytics community is comprised of women. Is that based on the idea that, unlike psychology, where you must be formally licensed by the state in which you practice, data analysts aren’t formally licensed, so counting the number of women in the field depends on how you chose to define “data analyst” or “data scientist”?

[Ms. Brown] Yes. The thing to be aware of is that there is no set definition of data analyst or data scientist. A strong defining factor might be a credential, such as a license or a widely accepted certification. There are a lot of product certifications in analytics that are focused on particular vendors and particular technical skills. That’s not what I mean by a widely accepted certification. A university degree serves as a meaningful and widely accepted credential, maybe less so than board certification or a license, but we don’t have that in analytics. That means that it’s up for grabs for people in the field to decide how they want to define what it means to be a data scientist or a data analyst. Recruiters are publishing what look to be sophisticated research papers on who is a data scientist and what the profession is. But if we start scratching the surface, we’re going to realize that this is not independent research. This is a recruiter who is getting data from a recruiting staff talking to job candidates, which is not a situation that lends itself to complete candor. I don’t know about you, but I don’t tell everything to recruiters, and I doubt anyone else does. So there’s a client willing to pay a recruiter to find people with particular credentials, and that’s fine in that particular situation. But, it’s frightening when I see conclusion about the number of women in analytics drawn from that type of research and then quoted in the press.

[OnlineEducation.com] If there are unique challenges for women entering the field of analytics, it seems that one of them has as much to do with perception as with reality. Is that your point?

[Ms. Brown] That’s a big one. The more that the public image of data analysis is based on the computing community’s concept of data science, the worse that perception is for women. Let’s not go too far with that. Women are gravitating to the analytics profession. In fact, I have seen articles that remark on the flood of women coming into analytics. And I’ve seen that informally here in Chicago. A chapter of the Women in Machine Learning and Data Science was recently founded in Chicago, maybe a year ago. I spoke at their first meeting and I’ve been to several other meetings. They are packing a pretty big room with women who have earned or are in the process of completing graduate degrees, and who are intending to have a career in analytics. They are in the kinds of educational programs that are perceived as being male dominated, and that probably have been in the past. But, that may not last. Women have been attracted to analytics before, they’re attracted to analytics now, and lots of women are coming into even what I would call the most masculine flavored versions of analytics. And, as I’ve said, if we think about all of the applications of analytics, in areas like psychology and medicine, then we’re no longer talking about male-dominated professions. So, public perception is a big part of this.

[OnlineEducation.com] Is it important to have this conversation about women in analytics and data science, and, if so, why do you think it’s an important issue? Is there something unique that women bring to field?

[Ms. Brown] Let me give you an anecdote. I often speak about analytics process. In particular, when I’m out encouraging people to use good process, I often talk about an open standard for analytics process called Cross Industry Standard Process for Data Mining (CRISP-DM). I’ve made it a personal mission to encourage the use and the further development of this open standard. There’s nothing inherently masculine or feminine about good analytics process, and certainly not about CRISP-DM. It was developed by an international consortium of over 200 organizations, and most of the people involved in that were probably men.

Process is important. Men are capable of understanding that. However, when I’ve talked to groups of people who identify as data scientists about an open standard, I’ve noticed some differences. I gave several talks on this topic about a year ago to two groups that were mostly men. I wasn’t expecting all of them to like what I had to say, but I was hoping that some of the people in the room would want to go on and join the Society of Data Miners, which is a young professional organization that was founded in London. I got all of the pushback I anticipated, and I got comments like, “This is so ten-years ago,” and “Facebook and Google don’t do this,” and “You expect me to write four things before I even get started?”

I gave that same talk at that first meeting of the Chicago chapter of Women in Machine Learning and Data Science. It was a room full of women and I got no pushback. What I got instead was understanding. They saw the value in good process, and many of them were already using similar approaches.

What I have found, anecdotally, in the community of people who identify as data scientists, is that women are more respectful of process, more willing to engage in process, and have a clearer understanding of the value of process. If, on the other hand, you’ve been immersed in and have embraced a culture that says, jump on in, take risks, and fail fast, and if you overvalue the technical challenge of writing new code, then you’re more likely to be resistant to a process like CRISP-DM. When people tell me that they want to write R code to do some sort of statistical analysis when there’s already existing software to do that, well, you’re introducing error every time you do that. There’s no time to be writing code for things that have already been coded. That’s a terrible waste of resources. And it introduces unnecessary risk.

[OnlineEducation.com] Do you believe that there’s an inherent value to having more women in the field of analytics?

[Ms. Brown] Personally, I believe that a brain is a brain, and a female brain and a male brain can do the same things. So, theoretically it shouldn’t matter. However, real life has everything to do with culture and experience. If you want to benefit from the information that comes from the full breadth of our culture, you have to involve people who know and have experienced those things. If we only involve a narrow range of people, then we only have their narrow range of problem solving abilities. In practice, I see this. Analysts often make decisions about how to tackle particular problems based on personal experience and observation. There are analytics problems that are so complex that we need people from different life experiences and different cultural backgrounds working on them. It’s an argument for diversity, not because it is ethically right, but because it is right for business. Is it also ethically right? Yes, it is. How convenient that it’s both practical and ethical.

[OnlineEducation.com] From a practical standpoint, what advice would you give to a woman or any other person about preparing for a career in analytics? Specifically, do you see any more or less value in pursuing a degree in computer science, psychology, mathematics, statistics, or any of the other fields we’ve discussed?

[Ms. Brown] The first thing I would tell anybody is this: what you major in is only a starting point. I have a daughter who will be starting college in just a few years, and this is what I tell her. People can and do change what they do throughout their careers. You have to be adaptable. My view is that you should learn good transferable skills that can be adapted to a wide variety of work. So, if my kid loves music, she can study lots of music, but she should also study math, writing, history, and all of the complementary subjects that create a good foundation. Acquire good adaptable skills.

If you desire a career in analytics, the first question I would ask is what job would you like to do when you graduate. It’s good to have a starting point. I would make an effort to meet and speak with people in that profession, and see if you can confirm that you actually want to work in that area. People’s ideas of what happens in Silicon Valley, for example, and what actually happens in Silicon Valley are not the same. This is true of every workplace and every profession. So, talk to people in the field, find out what it’s really like, and ask them what type of training will help you get that first job. There may be some real preferences, not just in terms of what type of degree you should get, but also what kinds of internships are most useful.

For a graduate student – someone who already has a degree – and for the career changers out there, if you want to get into analytics, take a deep breath. The reason that I want to slow people down, especially when it comes to the currently very popular graduate analytics programs, is that I think people may have unrealistic expectations for what those degrees will do for them. But, if you understand what your goals are, you can look for the best pathway toward that goal. I’m not against education. However, there are many ways to learn, and getting additional degrees is just one of those ways. If you already have a technical degree, or if you’ve already taken a couple of classes in statistics and have learned to program on your own, you may not have to go back to school right away. That technical background may allow you to move into analytics at your current job. If that’s not possible, there may be an area of volunteer work, or a similar job you could move into in order to start working in analytics without going back to school.

After considering that, you may want to get to the next level, which might involve further education. If, for example, you decide you want to be a college professor, then a graduate degree is very important. But, for a lot of people who want to move into analytics, it’s not necessary to start with a graduate degree.

[OnlineEducation.com] There is a better defined data and analytics curriculum today then there was even a few years ago. Do you think those programs are more useful?

[Ms. Brown] My concern isn’t about the content of those programs. I believe that those programs provide good technical training. But, the programs can be costly, and you want to make sure that they’re going to provide the payback that you want. The problem is, many people think that if they put the time and money into a particular program, they are going to get the kind of job they have heard about. And, that reminds me of what happened in the past with MBA degrees, and with the Webmaster credential, which for a very short period of time worked quite well for job placement. I worry that that’s already happening in analytics. It’s not that you can’t earn a good living and have a good career with an analytics degree, it’s that getting a degree that requires a large investment that may not be the most effective way to get there. I just encourage people to really think it through. Look for a way to get into analytics first, see if you like it, and then consider a program. If you’re not trained in statistics, and you’re not trained in computer programming, and you want to get into analytics, you should probably start by taking a statistics class at a community college, by learning some programming skills, and then make a decision. You can go from an art degree to a job in analytics, and I know someone who went from a physical education degree to a full-time career in analytics. It’s not easy. It’s not for everybody. But, it is doable. Talk to people who hire in the profession, and find out from them what they are looking in the people they are hiring. And, use that same mind you would use to solve an analytics problem to figure out where you can get the best value in education.

[OnlineEducation.com] Is there anything you’d like to add?

[Ms. Brown] I’d like to come back to the issue about diversity. Recently, I had a conversation with someone who was organizing an analytics event. Our conversation was about getting more women speakers in the field of analytics. And it was in the context of a discussion about the inherent biases in the data world. This man was aware that bias is an issue. He was also aware that I had considerable data indicating that a large number of women work in analytics. But he still refused to believe my many data sources, even though he had nothing but his own impressions to refute it. He insisted it was different in his niche, and that maybe statisticians are different from business data analysts. And that was his excuse for failing to include a substantial number of women speakers at the event.

What does this mean for a person who is looking at a career in analytics? Analytics is actually pretty diverse. But certain segments have fewer women and greater bias against women. If you love programming and computer science, but if you don’t see other people who look like you in the field, it can be discouraging. For example, you may notice that you’d be the only woman, or the only person of a particular background who would be working at a particular company, and worry about isolation or discrimination. I want everyone to know that there is inherent value to your presence because you bring not just technical skills, but also varied experiences and a broader way of thinking to the organization. Organizations suffer from narrow thinking.