 Hello and welcome to My Career in Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp and today we're talking to Nikita Patel from Softrans. With a robust catalog of courses offered on demand and industry leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dativersity.net and use code DVTOX for 20% off your purchase. Hello and welcome my name is Shannon Kemp and I'm the Chief Digital Officer at Dataversity and this is My Career in Data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to talk with people who help make those careers a little bit easier. To keep up to date in the latest in data management education go to dataversity.net forward slash subscribe. Today we are joined by Nikita Patel, a Senior Data Analyst at Softrans and normally this is where a podcast host would read a short bio of the guests but in this podcast your bio is what we're here to talk about. Nikita, hello and welcome. Hey Shannon, thank you so much and thanks for having me on this spot on this podcast today. I'm so glad you're here so tell me, okay so you're a Senior Data Analyst at Softrans so tell me what type of business is Softrans? That is right, Softrans is not a typical software consulting firm. We focus a lot on innovation, user research and specialize in delivering innovative software solutions for federal industry primarily. We've received a lot of awards for innovation and just recently we were ranked 28th on the Inc Best Workplace for Innovators. Oh, how very cool, congratulations. Thank you. That's awesome. So what do you do for Softrans, what's your typical work week look like? So I work on a project that supports the federal healthcare client, the Center for Medicare and Medicaid Services. The goal of this project is to support CMS's vision to reduce the cost and improving the quality of services by implementing several healthcare models for alternative payment options and we build serverless cloud-based provider enrollment system that allows accountable care organizations to enroll providers. So essentially we produce a lot of data from this system that we've built and we're talking about Terabyte here. So essentially we are at the tip of the entire model management data ecosystem and the data starts from us but we also collect a lot of data from other stakeholders. So we store transient data in data marks and once a month an automated data pipeline moves all this data from the marks to the data warehouse. So just to take a step back here and answer your original question, what do I do here on this project? So my role expands beyond data and I'm not just being involved on the back-end side of the systems but I also have to really deal with everything or anything or really any system that touches data. So I have to understand the system that produces this data and the data pipelines that transform the data at several stages and also the systems that stores this data. I have to ensure that the systems are doing the right thing in the production environment and the entire process is ongoing and it repeats on a periodic basis depending on what model you're looking at. So it starts with defining really the quality of data and I'm not going to use any acronyms or any terms from the Dhamma book but when I say really defining the quality of data it could be as simple as making sure the required values are there or looking at the nulls or the min-max value, looking at the basic stats. There could be a whole lot of data dimensions that I could be looking at but just defining what quality data looks like and then once I have the quality data defined I would start thinking about the business data rules and the data metrics and then start building these shacks that govern the data quality requirements and once I have these in place I would execute them on an ongoing basis. So really making sure that the quality of data is maintained throughout the data flow from when it's pretty used to transform when it's saved and when it's ready for analytics to be generated out of the data and right now we are in the process of automating all these checks and I want to build an automated data pipeline that generates and puts all these data metrics on a dashboard format for us to be able to share with our stakeholders for them to view on an ongoing basis but I know I'm making a big difference here because this data that we shared with the downstream systems will be used to make the final determinations of how the payments are processed to the accountable care organizations that are participating in these healthcare models and now that we've built this provider enrollment solution this year we're going to focus a lot on AI for BI which is what our CEO says. So really we would like to use Generative AI to let our stakeholders generate analytics for them. So we would just build the AI engine with a catalog of data libraries and they could use plain English language to build dashboards and really get answers to any business or policy related questions they would have. So just to summarize what I do, I do a lot of data quality, data analysis, build data pipelines, do data analytics, I've also played around with Gen AI and I cannot wait to start working on Gen AI starting this year. Very cool and what a great use case for that. So tell me Nikita, was this the dream, say when you were 6 years old, was this the dream did you say I'm going to grow up and be a senior data analyst? Well, I knew I wanted to be an engineer but really didn't learn about my knack for data until few years in my career. So it took some time and realization to figure out that I really love and have a passion for data. What was the dream when you were just when you were really young, what was it? So I have an undergrad degree in electronics and communication engineering and I started my career as an intern designing batch antenna for Indian space research organization. I was really programming the radio frequencies and the dimensions of the antenna to really attain the right radiations. I really liked the programming part of it so I figured to get into software programming. Although I was an electronics engineer by education, so I prepared and got accepted into a global software training program at Infosys. Again, it's one of the top five software consulting firms in India and I was trained there like rigorous training on different software languages and I was trained to be a software developer and a tester and some of the languages that I was trained on, which I can think of right now were Java and C, C++, JavaScript and CICS and Kabul and DB2 and SQL. Some of the languages are even up obsolete right now. But as I was learning these languages, I realized my love for database languages. But really I started developing this passion for data during one of my projects with Dun and Brad Street and it was a data modernization project and the need for the client was to migrate all the legacy systems to data warehouse and I was working on validating the system for a very complex data overriding module and it was then that was my first encounter of working on real-life production data and although originally I was tasked to validate the system, I started paying close attention to data and started finding system issues from the inside of drew from data and this is when I realized what difference data could make on businesses. So really there were all these different businesses data that Dun and Brad Street was accountable for and businesses financial, regulatory compliance, sales and marketing data was stored in their warehouse and they were really, they heavily relied on Dun and Brad Street's accurate migration of this data from legacy systems to the data warehouse to ensure what their data was correct and they didn't lose any critical information in this migration process. But at the end of the project, I received a star award for my technology and data savvy contributions to the project and it was then that I realized that I really needed to get a formal education in information systems and management. So I pursued a graduate degree information systems and management from Carnegie Mellon University and at CMU I got heavily involved on a lot of data intensive research projects with IBM with Bayer, MITRE and even a local hospital which was West Bend Allegheny Health Systems. So now that I've been in the healthcare industry for several years, I've realized that once you've mastered the best practices and solutions for one industry, I think it can be easily tailored and simulated across other industries as well. Visit dataversity.net and expand your knowledge with thousands of articles and blogs written by industry experts plus free live and on-demand webinars covering the complete data management spectrum. While you're there, subscribe to the weekly newsletter so you'll never miss a beat. Yeah, absolutely. Well, let me, so let me back it up a little bit. So you initially went into university for engineering. So what made you initially want to be an engineer? What drew you to that? So I was always driven by technology and I was really intrigued by the hardware and the software component of electronics engineering. So being able to understand how those different components fit together was my dream, which is why I really figured electronics engineering is the right track to choose because that would give me enough exposure to be involved both on the hardware as well as the software, like the programming side of it to give me a holistic picture of, you know, like a complete engineering, like building but at the same time programming to make sure what you build is working correctly. That makes sense. And then you, and then from their emphasis was your first job from university? Yes. Gotcha. Gotcha. So, and then you transition to, well, sorry, let me back it up a little. So after my electronics engineering degree, I interned at Indian Space Research Organization for a bit. That's when I was really designing patch antennas for space and because, you know, not only building these antennas, but I was also programming the antennas, the software programming part of it to ensure, you know, all the programming was coupled correctly with the hardware piece of it. So like the antennas should be radiating in a certain way. There should be accurate patterns established. And that can be only done if it was programmed correctly. And that's when I really realized that I was more passionate about programming versus really building things. Sure. Yeah. That makes sense. And what a cool place to learn that. I think the number one answer I get to what people wanted to be when they grew up was an astronaut. Definitely not an astronaut, but I think I did love being an engineer, not being on Moon, but an engineer who could send people to Moon. Yeah, that's very cool. I love that. So that's a, so then emphasis and Dan and Brad Street. And then from Dan and Brad Street, so when did you transition to Softrans? What's the link there? Yes, so I did have some journey. Well, so after graduation, I was working as a system data analyst for financial industry for one of the financial clients. It was a big syndicate lending software company, MISIS. And I worked as a system data analyst for them for a bit. And then I was also working for Next Gen healthcare, again, as a data and measures and systems analyst. And I switched in the same role, but I switched to Mathematica. But again, I was working as a policy researcher and a data analyst, really using data, draw insights for policy and decision making and Mathematica. And from Mathematica, it's been two years since I've been at Softrans. Very cool. Oh, so I love that. So tell me what's been your biggest career, your biggest lesson so far in your career? So my biggest lesson would be to continuously learn new technologies and businesses. And also to be hands on, because it's really important to apply what you learn in real life. And I think it's crucial for success. I think you're so right, because it's so different when you start applying it, right? So having a passion for data for most of for almost all of your career, what is your definition of data? That's a good question. So data to me is information. And it's everywhere. And it's in different forms. So when you think of data, it doesn't necessarily have to be only structured data in databases and technology. It could be unstructured data like voice, could be digital or non-digital. You're looking at digital forms of data like images and non-digital like papers and documents. But really, it's just a matter of collecting the right information in the right format to make accurate decisions. And I can think of a recent analogy when it comes to data and its definition. So I had a power outage in my formal dining room a few weeks ago and had an electrician come over to check it out. I told him that the breaker for that room had tripped and it would not turn back on. I'd also told him that this had happened second time in three months. And it happens every time it rains. Although the last time it had occurred, I could turn the breaker back on. I think a week after the breaker was tripped. We didn't really have anybody come over to diagnose the problem because the breaker turned back on. But now that we were facing the same issue, I figured to have it checked by an electrician. So with this set of data, the electrician had something to start with. But with this information, he went straight to the formal dining room and he started taking all the light fixtures out. And when I asked him, he said in his experience when there is an outage in a room, it's most likely due to a faulty light fixture. And he ended up not finding any issues with the fixture and he went back. I had another electrician come over and who processed the same information I gave to the prior electrician. And he went to the basement, pulled the circuit breaker and what he found was really surprising. He found a lot of water in the circuit breaker. Turns out that the circuit breaker, which was connected to the main line outside my home, the water from the main line was seeping through the line and getting collected in the circuit breaker. And every time it would rain, the water would get collected and it was seeping all the time through the breaker switch. So really what I thought was the first guy failed to recognize an important piece of data that had given him and straight up introduced bias in his data collection. And with bias data, he ended up producing a biased analysis and eventually didn't even have figured out the root cause for the problem. But anyways, this was just my way of explaining what data is and what information is and how you could use to derive analysis and results and find out root causes for any issues you're finding. It's a great example. And probably the last place you want water collecting. Oh, goodness. So tell me then, do you see the importance, especially as you're playing with generative AI, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? That's again a great question. I would say absolutely yes. I mean, we have financials, regulatory compliance, legal, sales, marketing, policy, innovations, and all these are going to be data driven. Businesses are going to start relying on data more and more to answer these questions. And as we're moving towards machine learning and AI, I think if you're having biased data or non-contextual data that you are feeding into the analytics engine or even the models, essentially AI is going to deliver biased information. So your data has to be contextual and it has to be unbiased to put it in context in layman's terms. This question always reminds me of a movie I watched, The Mitchells versus the Machines. And in the movie, the model was trained on biased data. And at the end of the end, the entire mission fails since AI could not recognize the difference between a dog versus a pig. So data is going to be mission critical. I mean, systems are going to be vulnerable if these data threats are recognized by attackers. So we really need to address the root cause, which is designing and collecting high quality contextual data to get correct and unbiased AI decisions. So really focusing on the data infrastructure and data quality and spreading the data literacy I think is going to be key. I could not disagree with that at all. I'm right there with you. So what advice would you give then to people looking to get into career in data management in any aspect of data? That's a good question again. So I would say for all the folks out there who are trying to get into a career in data, do your research on what's out there and what you like. And again, data is not just about data storage. There are all these different aspects to data. There is data architecture and there's data mining and collection and there's data storage and data security, data quality, data transformations and data governance and data management and data analytics, AI and machine learning. So really learn about all these terms and what they really mean and what aspects of data you really enjoy and feel connected with. And there are so many online resources and online learning platforms and there's so much open source tools and technologies that you could leverage and get some hands-on experience. And there are so many open source data sets that you could use to get that hands-on feeling after you do your research to figure out if you really enjoy working with data and what aspect of data you're really passionate about before you make the final determination. I like that a lot because there are so many aspects especially and there's new ones emerging all the time. This has been such fun and so exciting to get to know you, but I would be remiss if I didn't ask if somebody wanted to solicit the help of SoftRounds, how would they get a hold of you or SoftRounds? Yes, absolutely. I think you would have my LinkedIn information. I'm happy to share my SoftRounds email or even my personal email address. I'm happy to connect with folks who are interested to be part of this organization. If you have any questions, feel free to touch base with me or I think we have a big HR group who should be able to answer all the questions that folks would have in terms of recruitment or what are the open positions we have and what are the interests of passion really? I love it. Thank you so much and we will get those links posted to the podcast page for sure. Thank you so much, Nikita for taking the time to chat with us today. Thank you for having me. This was great. Absolutely. I love listening and hearing everybody's journey. For all of our listeners out there, if you'd like to queue up to date in the latest podcast and in the latest in data management education, you may go to Dativersity.net forward slash subscribe. Until next time, stay curious, everyone. Thank you for listening to Dativersity Talks, a podcast brought to you by Dativersity. 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