Unexpected Surprises in Analytical Projects w/ Piyanka Jain at Aryng (Episode 48) #DataTalk

Listen to the podcast:

Every week, we talk about important data and analytics topics with data science leaders from around the world on Facebook Live.  You can subscribe to the DataTalk podcast on iTunesGoogle PlayStitcherSoundCloud and Spotify.

Join our DataTalk show on Facebook Live with Piyanka Jain, President & CEO of Aryng as she shares unexpected surprises in analytical projects — and how to handle.

This data science video series is part of Experian’s effort to help people understand how data-powered decisions can help organizations develop innovative solutions.

To keep up with upcoming events, join our Data Science Community on Facebook. To suggest future data science topics or guests, please contact Mike Delgado.

Here’s a complete transcript:

Mike Delgado: Welcome to #DataTalk, a show where we talk to data science leaders from around the world. We’re super excited to talk with Piyanka Jain. She’s the CEO and President of Aryng, which is a management consulting company focused on analytics for business impact. Some of her clients include IBM, Google, SAP, Apple, and a lot of other top data and tech companies.
Today, we’re talking about unexpected surprises in analytical projects, and we are just super excited and blessed to have Piyanka as our guest today. Piyanka, how are you doing?

Piyanka Jain: I’m good, and it’s an honor to be here with you. I’m looking forward to this. This is really fun.

Mike Delgado: This is gonna be a great conversation, and this is sponsored by NyQuil because I’ve been sick all week. You could probably hear it in my voice. I apologize for any crackling you hear. But for those that are listening to the podcast, you’ve got to make sure to check out Piyanka’s business website. Again, the company’s name is Aryng, which is spelled A-R-Y-N-G. So make sure to google that after today’s episode.
So, Piyanka, we always like to start these shows getting to know you and your journey into data science.

Piyanka Jain: Happy to share. By the way, you were tuning out for me, so I don’t know if everybody else is also experiencing it, but we’ll roll. I think I got the question. Basically, how did I start out?

Mike Delgado: Yes.

Piyanka Jain: My background is in quant and math. I’m more of a math person, I think, but it turned out to be that I happened to … In two of my master’s degrees, both the theses involved AI and applied statistics. And I was always interested in solving big problems. So with my first master’s, I was solving a problem of radioactive spillage. And the second problem I was solving was with artificial intelligence in a routing table. So, it was a good, fun problem, but at that point, I’m completely gonna date myself, but at that point there wasn’t necessarily a thing called “analytics,” but it was also interesting. Math was always interesting for me, and quantitative methods and solving big problems. And after I graduated, I was working as a software engineer, you know developer, coder and so on and so forth, but at some point I realized that’s not me and I founded a company on the other end of the spectrum in advertising. In between, I also worked for Google, pre-IPO, so all sorts of things —

Mike Delgado: Wow.

Piyanka Jain: Not really finding myself, you know, not really like, “This is me.” And then I was just thinking about my next gig after my company Out of the Box Media, and I was just browsing and I saw this job description for a senior analyst at Adobe on Craigslist and I looked at that job and I was like, “This is me!” And so I made my résumé in the kind of format that I teach people how to do it now, when I help people transition, because I sort of had a transition as well. I wasn’t an analyst to begin with, and I became an analyst. And I went for my job, and I was hired on the spot.

Mike Delgado: Wow.

Piyanka Jain: I was given an offer —

Mike Delgado: Really?

Piyanka Jain: I couldn’t believe it. And I was like, “Wow.” Truly, I was on fire. People could see the passion I had, and you know what it is. But you know getting a job and then solving problems is two different things. So my quantitative methods didn’t really prepare me for the reality of solving big problems with data. Because in academics, you can run a hundred simulations. This is very different than when you come to the real world of analytics. So I had to learn a lot through this whole process of how do you actually begin to drive impact using data, using logic, using simple math, using more complex models? Whatever it takes, how do you begin to drive impact? So that’s my journey. And I’m so excited that I found my passion.

Mike Delgado: That is so cool, especially hearing about your different paths that you took. And you’re like, “I’m not happy here.” And then stumbling upon, of all things, a Craigslist listing. And so when you read that job description, you were like, “This is it.”

Piyanka Jain: Yes.

Mike Delgado: “This is exactly what I’m looking for.” But you were like, “I don’t necessarily have the résumé for it.” Was that —

Piyanka Jain: Yep. I didn’t have the résumé for it, and I made the résumé exactly like how I teach. I have a book called Acing Your Analytics Career Transition. For your readers, if they’re interested. And in that book I have an entire five-step method of how you transition. And that’s exactly what I did pretty much. And one of the chapters is on that 8-second résumé. How do you make an 8-second résumé?

Mike Delgado: Oh.

Piyanka Jain: And at that point, I didn’t know what I was doing. I just intuitively made my 8-second résumé because I had to pretty much show that I had what it took for that job. Although I didn’t. Like if you looked at what I was doing, as you said, I was going here and then here and then here.

Mike Delgado: Yeah.

Piyanka Jain: I didn’t quite have it. But I had it. And so I had to tell that story in the most fluent and graceful manner. So I developed this kind of format. I call it RFQ format, but your listeners and audience can go in and look at this book. It’s on Amazon. Acing Your Analytics Career Transition. That helped me. It can help you.

Mike Delgado: Awesome. We’ll make sure to put a link to that book on our website. And for those listening in, the URL’s ex.pn/datatalk56. That’s the URL where we’ll provide a link to that book. It sounds fascinating, especially for those of us who have been all over the place in different careers and are trying to make a transition. Because sometimes I think, Piyanka, recruiters look at the résumé and say, “This person’s all over the place. I’m not even gonna talk to them.”

Piyanka Jain: Exactly.

Mike Delgado: But you’ve somehow managed to be able to paint a nice picture, to show that you’d be a nice fit —

Piyanka Jain:  I think what happens for most of us is that we tend to identify ourselves with where we have been, right? We have our own momentum, we’ve been doing this, we’ve been doing that. And when we’ve been doing this and that, we have an incoherent story. An incoherent story is really a putoff because it’s basically 8 seconds. You know, recruiters and hiring managers take about 8 seconds or less to read your résumé and say, “Uh, ‘no’ pile.” Because you’re just all over the place. So, the idea is to tell the story about where you wanna go, where you wanna be, while collecting relevant information from your background to show that you have demonstrative experience doing it. It’s not really rocket science, and there’s no dishonesty. You’re not doing anything crazy here, but it’s just about painting a coherent story of where you wanna be, what you’re passionate about, what inspires you. And going from there.

Mike Delgado: That’s beautiful. I’m gonna check out your book. I’m definitely gonna check it out and buy it. That sounds fascinating. I love your approach to telling a story of a passion for your career. So what then led you from your dream job, where you’re like, “Yes, I love this,” to starting Aryng, your own company on analytics?

Piyanka Jain: That was a journey too. I was at Adobe for three-and-a-half years. I learned a lot about analytics, as applied to advertising, marketing, marketing operations, somewhat product — but basically the whole customer experience. And I really learned a lot. I had some amazing mentors and people around me and also great clients. Internal clients who challenged and said, “Why should I accept this?” And so on.
So I had started developing a … When I joined in, I think I had one product and I was reporting back on one product. By the time I left, the entire like Friday 3:30 p.m. was my email was going out for the entire set of relationship marketing campaigns. I was able to synthesize a lot of data into meaningful stuff for folks. I think that was what it was. And I was effective in solving problems using analytics, but I was still learning.
And then I joined PayPal, and I learned a lot about analytics as applied to product, to fraud, to the entire customer segments, customer operations, CS, and became a little bit more complete in my understanding of how you apply analytics in all these different places. Meanwhile, I was speaking at conferences, I was keynoting in the predictive analytics world and at many other business conferences. And I was seeing the gap where people thought analytics and data sciences were just rocket science. And they were oversolving it and jumping too wide, too far. It’s not rocket science. That’s where I started thinking about how do I empower people with this know-how of how to make decisions using data? It’s really not rocket science, and it can be taught. And I started putting all these frameworks that I had in my head on paper.

I starting thinking about how do I empower people? Out of that came my framework. It’s very intuitive. Most people who are doing analytics would be following something like that. It’s called BADIR, which is a five-step framework. It’s an acronym for these five steps: business questions, analysis plan, data collection, derive insights, and recommendations. So it’s a five-step method. And it’s there in my first book, Behind Every Good Decision. It’s also on Amazon. It’s published by Amacom. And your local bookstore. Behind Every Good Decision is the name of the book, and [BADR] is the framework. That is what I take now to my clients, my corporate consulting clients — that we solve big problems in an accelerated manner because we have that. And the whole beauty of BADIR is that it’s just not about data science; it’s also about decision science.

Because at the end of the day, it’s you and I who are making decisions. Machines are not making decisions, right?

And so, it’s about understanding who it is that needs this information from the data and what actions they’re ready to take, and what do they need by when, what are their constraints, what are the constraints of the data and the environment that we’re in. Putting it all together, and then driving towards actionable insights, which can be like … This framework basically incorporates the entire data science decision science, and it’s very sound algorithmically as well as in the decision science ask. If you follow that framework, or some such framework —

Mike Delgado: Yeah.

Piyanka Jain: You are going to make sure that what the analytics you do will be effective. It’s not gonna sit on a shelf. I speak at Predictive Analytics World and other such data science conferences. The biggest problem that data scientists will tell you today is that, “I do really good work and nobody cares.” Like, “My biggest recommendation, this is the greatest model I build, best accuracy, and it sits on a shelf somewhere. What happened?”

Mike Delgado: Yeah.

Piyanka Jain: What happened was that you missed that whole green track. You missed the whole decision science aspect of it that you didn’t engage with the stakeholders at the right time. You just thought, “I’ll build the best model and then I’ll try to push it down somebody’s throat.” It doesn’t work that way. And I’ve done it. I’ve done it, and that’s why I’ve learned.

When I started at Adobe, I wanted to solve every problem with the most complex algorithm I could find because that was my training. I learned nonlinear regression before I learned linear regression. So this is our training, but that doesn’t work very well because if people don’t understand you and you don’t understand them, they’re not gonna take action on whatever insights you have. So, long story short, BADIR is the framework that I would encourage for anybody who’s looking to do analytics. If they’re a data scientist and they’re not being effective, find this book and get that framework and start practicing that whole decision science aspect of it. And if you’re looking to get into analytics, make sure that you are learning analytics in a structured manner, so you can drive impact.

Mike Delgado: Yeah, that framework sounds awesome. Asking the right questions, helping to guide the person along, and also to get leadership and stakeholders involved. Can you talk a little bit about … Because you said, and I thought this was really interesting, that many data scientists are coming to you and saying, “I’m solving these problems, I’m coming up with these solutions, but then I’m not getting it across to leadership and it just sits there.” Can you speak to that data scientist who’s in that position right now?

They’re discovering some really fascinating insights. They’re sharing it as best they can with leadership, but it’s not being interpreted correctly and it’s just sitting there. What advice would you have for that data scientist?

Piyanka Jain: The short advice I have is, “Follow the BADIR framework.” The first thing is influence starts early. You think influence starts after you have some really amazing ruby-like insights, and then you’re thinking, “Oh, I’m gonna go and do the stage performance, ‘Voilà,’ and people will get excited.” Doesn’t work that way in the real world. Because everybody has their own context. The person whom you’re presenting to has their own problems set in their own context, and you need to have an understanding of their context, their problem set. If you have the CFO as your client, the CMO is responsible to the CEO for some metrics or something. He’s held accountable for something.

What is that? What are those things he’s responsible for? What is it that he’s thinking day in, day out? What’s keeping him up at night? You need to understand those things before you can think, “I have a beautiful solution.” But see, beautiful solution to what? What do you have? Understanding what problem you’re trying to solve means the biggest part, the front part, of BADIR business question and laying a hypothesis-driven plan. That’s the most important part of framing the problem, figuring out who’s wanting what answers, is it actionable, who are the critical stakeholders, who’s gonna sponsor it, who’s gonna take action on it. All of that needs to be figured out. And then also, what hypothesis do they have? Because hypothesis is the fastest way. I call it the detective route.

If you wanna find treasure in the Pacific Ocean, there are two approaches. You can start swimming and say, “Oh, wow, beautiful nice water, whatever else” and swim for a long time before you’re gonna find some treasures. Or you’re can say, “Where are the best possible sites or highest probability sites where the ship must have sunk?” Find your best spots and then go diving down into it. The second approach is the detective route, and this is the approach that most data scientists need to have. First, understand the problem. What is it? Which treasure are you looking for — these shiny whales or this alga which solves cancer or these rubies which got sunk in a 1952 wreck? Let’s find that first and then lay out your hypotheses. Where are the highest probability areas where you can find the solution? And hypotheses come from stakeholders.

The same person who has asked you the problem and everybody else in the room, let’s put our heads together and figure out where are the places where we can get the best solutions, the hypothesis you’ve been planning. And then let’s all of us come together and agree to a plan. This is how I’m gonna look at my metrics; this is how I’m gonna prove and disprove my hypothesis. All of that needs to come together.
Once you have a solid plan, you’re most likely to succeed. And then, of course, there’s the whole aspect of touching base, making sure early insights and presenting right. So all of those things need to happen right for you to be effective as a data scientist.

Mike Delgado: I love that answer, Piyanka, and I think it’s very helpful, especially for the young data scientists, because that is something that is sometimes only learned once you’re in the business.

Piyanka Jain: Right.

Mike Delgado: Because you’re doing all this research, you’re doing all this analysis. You’re sitting at your desk with your computer, finding all these insights, but then you realize there’s all this other stuff that has to happen along the way to get recognized, to get buy-in to actually move forward. So there’s always pre-homework that has to be done to make something actually actionable and have success with it. Today’s topic is all about unexpected surprises in analytics. Could you share some case studies or some times where you’ve worked on some projects coming in with a hypothesis and all of a sudden the data’s telling you a different story?

Piyanka Jain: Yeah. It happens a lot. In fact, the more it happens, the highest your impact. It’s a given that the business was working this way, and you know the hypothesis had come in, but what you found was completely this way, and that means the business has to shift significantly. And that means there’s the biggest opportunity there. So it’s actually a great blessing to get these amazing 180-degree turns. And I can think of many cases, but one example that comes to my mind is, this is sometime back, I was with PayPal and I was contacted by the head of … Basically, this is a CEO-level project which basically said CSAT customer satisfaction is going down, and we don’t know what’s going on, and we are looking really hard at these metrics. Average speed of answer (ASA), average hold time, these were the standard metrics for any customer service. How quickly are you answering the phone? How much is the hold time? We wanted to make sure that the customer service … but the CSATs were still going down. So what’s going on? And I was looked at at that point as the SWAT team. So anywhere there’s a problem that cannot be solved, I would be parachuted in with my team and they’d say, “Solve this.”

Mike Delgado: “Give it to Piyanka.”

Piyanka Jain: That’s right. And it could be a good thing, but it was also always a bad thing. I was within crossfires of high-powered discussion.

Mike Delgado: Oh, boy.

Piyanka Jain: You’re like, “What am I doing here? Somebody get me out of here.” But I learned a lot. So, I went to Omaha Operations team, which was based out of Omaha, Nebraska. And I tried to understand using the BADIR framework, which was in my head at that point. But basically, figuring out what the question is. What’s going on in the field? Talk to agents who are picking up the phone. I talked to them and said, “Why do you think the customer is unsatisfied?” And so on and so forth. Based on that, we took [unintelligible 00:20:01] hypotheses, we put down in our analysis and we collected all the relevant data. So another thing BADIR framework is it basically helps you think through the problem so you’re not boiling the ocean. You’re just looking at the relevant data sets. That accelerates your analysis because the more data set users analyzed, the more time it’s gonna take and you know all of that. Signal to noise ratio versus if you’re just looking at a small data set based on the hypotheses. So that’s what we did. And we looked at the data coming from the surveys and all that.

And what we found was ASA and AHT and those two or three metrics, operational metrics, were not even correlated to CSAT. What was correlated to CSAT was whether there … which we then turned the term call FCR, first call resolution, and how many times a customer had to call and whether the customer perceived the agent as friendly or not, right? This was not expected at all. What had happened was initially when the average hold time, average speed to answer, ASA and AHT, when they used to be rather long, they were key metrics that were driving customer satisfaction.

Mike Delgado: Yeah.

Piyanka Jain: When those metrics came under, like within, a tolerable amount and then the other metrics became important. But the business had moved, but people’s dashboard had not. So they were still in their dashboard. The greens and reds were still showing ASA and AHT. Whereas CSAT was going down. And once we found that, the agents were trained, the metrics were changed, FCR and whether the customer had to call again, all of those things changed. And that was a big hullabaloo about we have to change our ways of looking at things, and I was like, “This is what we are,” because of following BADIR and because of involving the stakeholders early on. Those were involved early on; they knew what we were doing. We were not moving forward, I did not move forward, until they said, “Your plan looks good.” I was not moving anywhere. Like, “You guys believe this is how we’re gonna approach it, because this is the conversation I’m having with these high-value stakeholders early on. Before I’ve even had any insights.” And so, they say, “Yeah, this is looking good.” They felt heard.

Mike Delgado: Yes.

Piyanka Jain: When the insights were ready, they were ready to act on it, because they came along with us on that journey. So it was a very successful project. Things completely changed and [C-SAT] improved from then on. So, big surprises, but to date those are the metrics that are still being used by not only just [ACS] for PayPal, but many other companies.

Mike Delgado: Wow. I just love how you from the very beginning were bringing them along with you on the journey. Sharing with them your thoughts, getting buy-in, so that they were with you on this process.

Piyanka Jain: Yeah.

Mike Delgado: So when you did share this surprising data, they were surprised with you, but like, “Oh, we can act on this.”

Piyanka Jain: Yes. And they had confidence on it. The biggest thing is that people … The only reason people don’t accept your answer is when they don’t have confidence on it. But because of this process that we followed and … I mean of course one thing was that I’d built significant credibility within the organization, but the other thing was I was following this process where they were coming along with me. They brought the process, they bought like, “Yeah, this sounds like a sound approach.” And they realized how focused we were to solving that CSAT. So we were all on the same team; we’re gonna move that CSAT. And so that’s when you know when you’re driving a big boulder and you’re all pushing from one direction you’re more likely to move it, versus people pushing it from different directions.

Mike Delgado: Piyanka, have you worked on a project where when you were sharing, going through your process, bringing them along with you and then the insights you shared were just not believed? Like the leader was like, “Well, this doesn’t make sense to me”? Have you ever encountered that?

Piyanka Jain: Very early on before I’d understood the process. And if you, your audience, any of you data scientists out there, if you’re experiencing it, that just tells you you are not engaging the stakeholders early on. The only way your stakeholders … Your stakeholders want answers. And wanted answers yesterday. So they are ready and primed to look for answers from you. So, if they’re not buying your answers, fundamentally what’s going on is you are not finding alignment with them. And if you’re not finding alignment with them, that means your early part of BNA, business question analysis plan, all of that analysis plan needs to be locked and loaded and agreed upon by everybody saying, “Yeah, this is how we’re gonna look at it.” If you do that, you’re not going to face that at all.

Sometimes what happens is you start with three stakeholders, and then you know the VP of Marketing left and another marketing person came and this is a three-month-old project. Another person came in, so they joined in a little late. As a data scientist or on the analytics side, you have to make sure that the new person who’s coming in also comes along with you. And that they get an opportunity to provide input. Because unless they feel heard, they will not hear you. It’s as simple as that.

So they need to feel heard. All the stakeholders need to come along. And there often is a situation where the stakeholders get added later on; make sure to have a separate meeting with them. Bring them along, take their feedback, incorporate it, so that when you present people are ready and primed. If you haven’t heard them, they’re not gonna hear you. It’s as simple as that. So you need to make sure that you’ve heard people. You have presented with them, after hearing all of them, you’ve presented a cohesive plan, and everybody agrees with that plan.

Mike Delgado: For those listening to the podcast, if you’d like to learn more about Piyanka’s books, this framework, you can always go to her website at Aryng, which is spelled A-R-Y-N-G. And just Google that or you can go to the Experian blog at ex.pn/datatalk56. We’ll have links to her books and the framework and her website. Before we go, Piyanka, I’m just loving all these stories that you’re sharing about how to approach analytics, and I think this is super helpful, especially for the person who’s just starting out in data science. Or the person who’s just struggling with getting heard, because that can be very frustrating where you work so hard. You just wanna leave that company because you’re not being listened to.
Piyanka Jain: Yeah. Unfortunately, you take yourself along with you. You leave the company, but you take yourself along with you and you have the same situation all over again. So the problem doesn’t lie outside the problem area. Changing your way. I wanted to say one last thing to the folks who are looking to transition their career to analytics, you know, this data science, machine learning AI. It’s just such a blitzy world out there, and everybody wants to be getting on it. It’s not for everybody. If it is your passion, please find right ways to get into it. But it’s not for everybody, so go take an aptitude test. We have one on Aryng.com.

There’s an analytics aptitude test. It’s a pretty cohesive, simple test. We can send the link to you as well, Michael. But go get a test. Make sure this is you. If you take the test on Aryng.com — A-R-Y-N-G, Aryng.com — you will actually hear back from us and we’ll tell you whether this is right for you or not. Make sure you understand that this is for you, and once you understand this is for you, you’ve always loved puzzles, and you like seeing patterns and solving complex problems, you’re a problem solver, then make sure to …

Again, another thing which happens to people who are looking to transition or are going to is the tools. “I should learn Tableau and Python and R.” It’s not about the tool. It’s about problem-solving and understanding the framework of how to solve it, and so find a training, find ways in which you can understand or learn analytics as applied to business. I’ve come from academia, so it’s different from academia. And it’s different almost always from statistics as well. You apply statistics but really learn analytics as applied to solving problems. And do a real project. Not you being able to optimize [cagel] ranking. Yes, you’ve optimized in the data science aspect of it, and maybe you’ve understood feature optimization, but that’s not gonna get you into analytics. We have such a program. I have an entire program in career transition, but if you find such a program where basically applied analytics, real-life examples, real-time client work, that is where you’ll say, “This is me” versus “This is so overwhelming. This whole predictive analytics thing is killing me.” It’s better to find out now …

Mike Delgado: That’s right.

Piyanka Jain: … than going in later and saying, “I’m so unsuccessful. I don’t like this. I’ve spent three years taking all sorts of MOOC courses on Coursera and all that and I don’t like this.” You don’t want to end up in that position.
Find out if it’s right for you. Take the test, make sure to choose the right program, and once you start doing it hands-on you’ll know whether this is for you or not.

Mike Delgado: Great advice. For those listening to the podcast, again the website is A-R-Y-N-G.com. That’s Piyanka’s business website where you can take the test, learn more about how to become a data scientist. And one last question, Piyanka, before you go. You know, one of the common questions we get in our data science community is from people who are excited about … They read all about data science and AI in the news all the time. And they’re always asking, “How do I get started? Where do I start?” And for those that have taken your quiz and realized, “Yes, I like to solve problems. I feel like I’d be a good fit,” what would you say is a good place to start to get the wheels going, to help them start their careers in data science?

Piyanka Jain: A great place to start is with us. If you score well in that test, I will be your direct mentor. So yeah, I have limited time with the number of people I can spend and directly mentor, but if you score well, you’ll be assigned to me. And I will walk you through this process. We’ll teach you business analytics, predictive analytics, AB testing, all with problems. Like just examples I’m giving you. Same from all of our clients. We give you examples after examples and exercises and capstone cases. And then at the end of the training, you work on a client project. One of our clients. And by the time you’re done with that, you’re pretty solid.

Then when you go to an interview, and we of course prepare you for the interview, we have that acing analytics career transition, that entire method, résumé building, targeting your job and all of that. But when you come and sit in front of that hiring manager, you know how to solve a problem using data because you’ve done it already. Many times in the class, then in the real project with your client.
So I would say, “Come join us!” We’ll take care of you. And I would love to be your mentor. I love this transition from taking people from A to Z where they come in and they don’t know much about analytics, they’re unsure about themselves and by the time I get my call from folks saying, “I just got hired! I got this job!” it’s so exhilarating,
most satisfying for me. I do consulting. Analytics consulting’s our main thing, but this individual stuff that I still mentor people, I really enjoy it. I help people. I love doing this empowering of folks and getting them to their dream jobs.

Mike Delgado: And that is awesome. Piyanka, it’s been awesome having you as our guest. You can tell just from her passion and enthusiasm for data science, not only as an analyst, as a brilliant data scientist, but also as a mentor. Someone who to empower people. So if you are someone who’s interested in getting involved in the data science field, make sure to reach out to her. Go to the website Aryng.com, again A-R-Y-N-G.com. Check out the resources there, take the quiz. And who knows? You might end up being mentored by Piyanka, which would be amazing. And this is so cool, Piyanka, to see how you’re giving back to the data science community because of your love for data science and helping people not make the mistakes you made early on and being able to succeed as a data scientist.

Thank you for all your contributions to the data science community. Thank you for inspiring so many people just in today’s chat. And for those listening, again follow her on LinkedIn. Connect with her, and also connect with her business. And again, if you’d like to learn more about her, you can go to the Experian blog, where we’ll have a full transcription of today’s episode and the video, etc. And the URL’s simply ex.pn/datatalk56. Piyanka, thank you so much for your time.

Piyanka Jain: Thank you.

About Piyanka Jain

Piyanka Jain is the President and CEO of Aryng – a management consulting company focused on Analytics for business impact. As a highly regarded industry thought leader in analytics, she has been a keynote speaker at business and analytics conferences including American Marketing Association, Predictive Analytics World, GigaOm, Google Analytics User Conference. She speaks about data driven decision making to gain competitive advantage.

In 15 years as an analytics leader, she has had 150M+ demonstrated impact on business. As a gifted problem solver, she seeks out patterns and insights to drive change in her client’s organizations and impact top levers of business. She considers customer satisfaction, empowerment and positive engagement as the highest rewards, and dollar impact as a natural consequence of it. Her book ‘Behind Every Good Decision’ is an actionable guide for business managers on data driven decision making through business analytics.

She has two Masters degrees with thesis involving applied mathematics and statistics.

Check out our upcoming data science live video chats.