Season 1

You know how it is when you meet someone and you can go on talking for hours? That’s how it was in this interview with Debbie Reynolds, a data privacy professional who’s known as the data diva. Fortunately, I managed to keep the interview relatively short since I respect your time. Without going off tangents, the two of us discuss various privacy matters, ranging from regulations like GDPR to a privacy evaluation framework for organizations, to what you and I can do to help preserve our privacy in practice. Debbie provides various hands-on resources and advice, while the conversation goes beyond the pre-scripted questions I had prepared. Probably one of the most enjoyable informational interviews I’ve conducted so far. Hopefully, you’ll enjoy it too.

It may seem like something simple that you can do on your own, or even delegate to a junior member of your team. However, ML testing is much more complex when dealing with performance-critical data products that require not just a solid model but one that can remain reliable for the foreseeable future. Also, many datasets today involve some PII, which needs to be handled properly, as we’ve stressed in previous episodes. What if you could do all that in a straightforward, no-code, platform? Mr. Yunus Bulut, a co-author of mine on two occasions, joins me in this deep dive on the topic. He also covers the key aspects of his latest project, validAItor, an open-source platform for ML testing, to improve this whole process.

Programming languages may seem a quite technical topic, but when it comes to data analytics, it’s hard to ignore them. This is especially the case when we delve into data science projects. In this relatively impromptu interview episode, I’ll be talking with Arnuld, a self-made programmer and software engineer, who is also a data science practitioner lately. His insights on programming languages are on-point and even though this talk gets quite technical at times, it’s something very useful for any data analytics professional, especially those aiming for a career in data science. If you like programming, you’d enjoy this episode, for sure. If you don’t like it, you may end up liking it afterward. The episode is a bit longer than all the previous ones, but it’s worth listening to in its entirety.

In an effort to get closer to real-world situations, much like in episode 4, in the next few episodes, we’ll delve into everyday activities and where private data can be found there. In this episode, we’ll explore the datascape of social media as well as that of online communications. These are both places of questionable privacy (at best!) while at the same time brimming with data products and other analytics-related scenarios. Could it be that there is more than meets the eye, even if you have computer vision at your disposal? Listen to this episode to find out!

Privacy in data files, be in .xlsx ones or .csv, is often a concern when it comes to sensitive data. The latter can be PII-related or not. As an analytics professional or anyone dealing with such files, you may want to keep them private, to avoid any issues that could come about if that sensitive information were to be leaked. In this episode, I attempt to look into this matter and examine some potential solutions that you can incorporate into your workflow so that you maintain the privacy of the people behind those files.

Cloud storage is something most of us deal with in our daily lives. In fact, you may be using the cloud for storing files from your phone, even without realizing it. It’s even more intrusive when it comes to specialized gadgets like the reMarkable tablet, one of the few devices that live up to its name putting its competitors to shame. But what about the privacy aspects of cloud storage? Could it be that behind the scenes your PII is at a high risk of being compromised? Let’s find out by delving into this topic in some depth. This is a technically light episode aimed at a general audience.

Things are a bit more technical in this episode but not so much that you won’t be able to follow it. After all, we need to get into the nitty-gritty at one point and look at particular technologies and methodologies that can help make privacy a feasible possibility. In this episode I attempt to do just that, covering two great tools that can help preserve privacy, especially if they are used in tandem. The best part is that you can find FOSS out there that can make these technologies work for you without investing any money. If you are a business person and wish to invest in more professional tools, you could (and probably should) do that too. In any case, learning about compression and encryption is the first step in this journey of making privacy happen.

Who doesn’t like online apps? I know I do, which is why I’m a big fan of FOSS and other kinds of software that make my data analytics work easier. I’m sure you know about this application type and perhaps even use such applications regularly. Did you know about virtual desktops though? What about how all this software relates to privacy? If you are curious about all this, this episode is for you! You may even get some ideas about how you can improve your workflow while on the go, without compromising your privacy.

If privacy was a given, we wouldn’t be talking about it that much, right? Well, that’s what this episode is all about, examining where privacy is at risk, both in our personal lives and in the workplace. One can never be too careful! But what about the specifics of it all? In this episode, I’ll look into this matter from a very pragmatic perspective. If you pay attention to this, you may leave this podcast episode with a better sense of where privacy is threatened and more awareness about this whole matter. This is particularly useful if you deal with data containing PII in your everyday work.

Ethics may be a field in Philosophy, but it’s also something very practical and necessary in our line of work. That’s especially the case when we deal with sensitive data, such as that related to personally identifiable information. But what are all these terms and how do they impact our lives? This episode explores them and attempts to find connections with our work and the mindset that governs it. Without getting overly technical, you gain an understanding of what PII is, what kind of variables may contain it, and some hints on how you can deal with it, in your data analytics projects.

So far, we have gotten an idea about privacy and analytics from a high-level standpoint. What about the business world though? How does privacy look from a data professional’s perspective who is also a business person and works closely with other stakeholders in an organization? Steve Hoberman of Technics Publications (www.TechnicsPub.com) joins me in this episode to explain how things work in the data-business universe and how having a common business language can help us communicate better for a data project in real-world scenarios. Building on his many years of experience in the industry, his teaching experience in Columbia University, and the dozens of books he has read on this topic, he breaks it down for us and to some extent inspires us to be more successful analytics professionals.

What’s the value of privacy? How does it relate to analytics? What’s personally identifiable information? These are questions many of us have had, questions this episode attempts to answer. Without getting too technical, but without oversimplifying the topic either, I make an effort to explain it the best I can and incite your interest in Privacy. This is not just for your own benefit but also for all the people whose data you may be handling in your work. So, let’s embark on this journey of privacy and analytics, shedding light into this whole matter, shall we?